Distance_&_Density.ipynb

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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AditiSatsangi/Jodhpur-Map-and-OD-Matrix-for-Multiclass-Vehicles/blob/main/Distance_%26_Density.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Code used to extract the table from excel sheet\n",
        "\"\"\"\n",
        "import pandas as pd\n",
        "df = pd.read_excel(\"bus_matrix.xlsx\")\n",
        "bus_matrix = [df.iloc[3+1+11*j:12+11*j,1:].to_numpy() for j in range(10)]\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "LQ2n8uCUmQa8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "9b072f9d-a05c-4835-add7-c15b647ca723"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nimport pandas as pd\\ndf = pd.read_excel(\"bus_matrix.xlsx\")\\nbus_matrix = [df.iloc[3+1+11*j:12+11*j,1:].to_numpy() for j in range(10)]\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Flow Speed Function"
      ],
      "metadata": {
        "id": "Q0bixfvfv0lE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\\begin{equation}\n",
        "v_f(r) = \\left\\{\n",
        "\\begin{array}{ll}\n",
        "\\frac{r}{r_c}V_{c}+ \\left ( 1 - \\frac{r}{r_{c}} \\right)V^{u, max} & \\text{if } r < r_c \\\\\n",
        "\\frac{r_{jam} - r_a}{r_{jam} - r_{c}}V_c r_c & \\text{if } r \\ge r_c\n",
        "\\end{array}\n",
        "\\right.\n",
        "\\end{equation}"
      ],
      "metadata": {
        "id": "DKCgYYkvzWZm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def flow(density, vehicle_class):\n",
        "  \"\"\"\n",
        "  Calculates the  flow speed given the density and vehicle_class.\n",
        "  Args:\n",
        "    density: The density of vehicles in vehicles per kilometer.\n",
        "    vehicle_class: The vehicle class\n",
        "\n",
        "  Remarks:\n",
        "    All critical and max values are specific to the vehicle class\n",
        "\n",
        "  Returns:\n",
        "    The vehicle flow speed in kilometers per hour.\n",
        "  \"\"\"\n",
        "\n",
        "  if density < critical_density_class:\n",
        "    return (density/critical_density_class)*critical_flow + (1-(density/critical_density_class))*max_flow_vehicle_class\n",
        "\n",
        "  else:\n",
        "    return ((jam_density - density)/(jam_density - criticial_density_class)) * critical_flow * critical_density_class\n",
        "\n"
      ],
      "metadata": {
        "id": "3v8QhWRiv6rm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Traversal distance Computation"
      ],
      "metadata": {
        "id": "dbQBAkGPdvX8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "def compute_travel_time(density, flow, vehicle_class):\n",
        "    # Define the parameters specific to the vehicle class\n",
        "    if vehicle_class == 'motorcycle':\n",
        "        beta_0 = 0.01  # Coefficient for the traversable distance equation\n",
        "        beta_1 = 0.02  # Coefficient for the traversable distance equation\n",
        "        D_l =  0.001   # Look ahead distance for the driver\n",
        "    elif vehicle_class == 'car':\n",
        "        beta_0 = 0.12  # Coefficient for the traversable distance equation\n",
        "        beta_1 = 0.15  # Coefficient for the traversable distance equation\n",
        "        D_l = 0.001  # Look ahead distance for the driver\n",
        "    elif vehicle_class == 'bus':  # car truck motorcycle bus\n",
        "        beta_0 = 0.03  # Coefficient for the traversable distance equation\n",
        "        beta_1 = 0.01  # Coefficient for the traversable distance equation\n",
        "        D_l = 0.002    # Look ahead distance for the driver\n",
        "    elif vehicle_class == 'truck':  # car truck motorcycle bus\n",
        "        beta_0 = 0.03  # Coefficient for the traversable distance equation\n",
        "        beta_1 = 0.01  # Coefficient for the traversable distance equation\n",
        "        D_l = 0.002   # Look ahead distance for the driver(cell length)\n",
        "    else:\n",
        "        raise ValueError(\"Invalid vehicle class\")\n",
        "\n",
        "    # Compute the traversable distance based on density and flow\n",
        "    traversable_distance = beta_0 * density + beta_1 * flow\n",
        "\n",
        "    # Compute the travel time using the traversable distance and other parameters\n",
        "    travel_time = D_l / (flow * (1 - density) + traversable_distance)\n",
        "    return traversable_distance, travel_time\n",
        "\n",
        "density=np.zeros((8, 8))\n",
        "flow = np.array     ([[0,\t8,\t0,\t0,\t0,\t17,\t14,\t0],\n",
        "                      [9,\t0,\t9,\t0,\t0,\t14,\t16,\t11],\n",
        "                      [0,\t10,\t0,\t0,\t0,\t0,\t0,\t12],\n",
        "                      [0,\t0, 0,\t0,\t0,\t11,\t0,\t12],\n",
        "                      [0,\t0,\t0,\t0,\t0,\t12,\t15,\t0],\n",
        "                      [14,\t0,\t0,\t15,\t17,\t0,\t14,\t0],\n",
        "                      [17,\t13,\t0,\t0,\t12,\t10,\t0,\t0],\n",
        "                      [0,\t17,\t14,\t10,\t0,\t0,\t0, 0]])\n",
        "\n",
        "# Example flow matrix\n",
        "travel_time= np.zeros((8,8))\n",
        "vehicle_class = 'car'\n",
        "travel_time = compute_travel_time(density, flow, vehicle_class)\n",
        "#print(\"Travel time:\")\n",
        "#print(travel_time)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JQyDeKFhJh7K",
        "outputId": "0c2a8fe9-7f02-44a8-9d00-588d04983183"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-70-c492ad516f4e>:27: RuntimeWarning: divide by zero encountered in true_divide\n",
            "  travel_time = D_l / (flow * (1 - density) + traversable_distance)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## BUS"
      ],
      "metadata": {
        "id": "v7n_VivmrEHM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "t1 = np.array([[0, 12, 0, 0, 0, 19, 17, 0],\n",
        "       [11, 0, 9, 0, 0, 17, 13, 11],\n",
        "       [0, 10, 0, 0, 0, 0, 0, 12],\n",
        "       [0, 0, 0, 0, 0, 15, 0, 12],\n",
        "       [0, 0, 0, 0, 0, 12, 17, 0],\n",
        "       [14, 0, 0, 15, 17, 0, 16, 0],\n",
        "       [16, 13, 0, 0, 14, 13, 0, 0],\n",
        "       [0, 19, 14, 11, 0, 0, 0, 0]])\n",
        "\n",
        "t2 = np.array([[0, 17, 0, 0, 0, 12, 14, 0],\n",
        "       [10, 0, 11, 0, 0, 11, 15, 18],\n",
        "       [0, 14, 0, 0, 0, 0, 0, 15],\n",
        "       [0, 0, 0, 0, 0, 13, 0, 14],\n",
        "       [0, 0, 0, 0, 0, 11, 13, 0],\n",
        "       [15, 0, 0, 13, 17, 0, 14, 0],\n",
        "       [18, 13, 0, 0, 19, 11, 0, 0],\n",
        "       [0, 12, 13, 10, 0, 0, 0, 0]])\n",
        "\n",
        "t3 = np.array([[0, 8, 0, 0, 0, 11, 17, 0],\n",
        "       [7, 0, 18, 0, 0, 12, 17, 19],\n",
        "       [0, 11, 0, 0, 0, 0, 0, 17],\n",
        "       [0, 0, 0, 0, 0, 14, 0, 15],\n",
        "       [0, 0, 0, 0, 0, 12, 14, 0],\n",
        "       [9, 0, 0, 13, 11, 0, 12, 0],\n",
        "       [12, 14, 0, 0, 12, 12, 0, 0],\n",
        "       [0, 20, 10, 10, 0, 0, 0, 0]])\n",
        "\n",
        "t4 = np.array([[0, 15, 0, 0, 0, 13, 14, 0],\n",
        "       [10, 0, 14, 0, 0, 10, 11, 10],\n",
        "       [0, 16, 0, 0, 0, 0, 0, 16],\n",
        "       [0, 0, 0, 0, 0, 14, 0, 16],\n",
        "       [0, 0, 0, 0, 0, 12, 13, 0],\n",
        "       [11, 0, 0, 12, 20, 0, 12, 0],\n",
        "       [12, 15, 0, 0, 21, 12, 0, 0],\n",
        "       [0, 15, 7, 11, 0, 0, 0, 0]])\n",
        "\n",
        "t5 = np.array([[0, 17, 0, 0, 0, 17, 13, 0],\n",
        "       [15, 0, 14, 0, 0, 16, 14, 13],\n",
        "       [0, 5, 0, 0, 0, 0, 0, 15],\n",
        "       [0, 0, 0, 0, 0, 14, 0, 20],\n",
        "       [0, 0, 0, 0, 0, 14, 18, 0],\n",
        "       [8, 0, 0, 13, 15, 0, 16, 0],\n",
        "       [10, 14, 0, 0, 20, 12, 0, 0],\n",
        "       [0, 19, 10, 16, 0, 0, 0, 0]])\n",
        "\n",
        "t6 = np.array([[0, 21, 0, 0, 0, 13, 14, 0],\n",
        "       [12, 0, 17, 0, 0, 16, 16, 14],\n",
        "       [0, 19, 0, 0, 0, 0, 0, 17],\n",
        "       [0, 0, 0, 0, 0, 12, 0, 15],\n",
        "       [0, 0, 0, 0, 0, 11, 12, 0],\n",
        "       [8, 0, 0, 15, 13, 0, 10, 0],\n",
        "       [23, 14, 0, 0, 17, 10, 0, 0],\n",
        "       [0, 12, 16, 19, 0, 0, 0, 0]])\n",
        "\n",
        "t7 = np.array([[0, 15, 0, 0, 0, 14, 13, 0],\n",
        "       [22, 0, 12, 0, 0, 15, 18, 15],\n",
        "       [0, 14, 0, 0, 0, 0, 0, 10],\n",
        "       [0, 0, 0, 0, 0, 9, 0, 8],\n",
        "       [0, 0, 0, 0, 0, 10, 14, 0],\n",
        "       [17, 0, 0, 15, 14, 0, 19, 0],\n",
        "       [19, 18, 0, 0, 18, 13, 0, 0],\n",
        "       [0, 20, 9, 15, 0, 0, 0, 0]])\n",
        "\n",
        "t8 = np.array([[0, 10, 0, 0, 0, 15, 15, 0],\n",
        "       [12, 0, 11, 0, 0, 9, 19, 18],\n",
        "       [0, 14, 0, 0, 0, 0, 0, 10],\n",
        "       [0, 0, 0, 0, 0, 17, 0, 20],\n",
        "       [0, 0, 0, 0, 0, 14, 16, 0],\n",
        "       [17, 0, 0, 13, 19, 0, 17, 0],\n",
        "       [16, 17, 0, 0, 1, 12, 0, 0],\n",
        "       [0, 12, 10, 10, 0, 0, 0, 0]])\n",
        "\n",
        "t9 = np.array([[0, 10, 0, 0, 0, 15, 13, 0],\n",
        "       [14, 0, 14, 0, 0, 10, 17, 10],\n",
        "       [0, 11, 0, 0, 0, 0, 0, 11],\n",
        "       [0, 0, 0, 0, 0, 13, 0, 15],\n",
        "       [0, 0, 0, 0, 0, 11, 15, 0],\n",
        "       [12, 0, 0, 18, 18, 0, 20, 0],\n",
        "       [19, 15, 0, 0, 15, 17, 0, 0],\n",
        "       [0, 20, 16, 14, 0, 0, 0, 0]])\n",
        "\n",
        "t10 = np.array([[0, 10, 0, 0, 0, 20, 9, 0],\n",
        "       [17, 0, 12, 0, 0, 10, 10, 11],\n",
        "       [0, 14, 0, 0, 0, 0, 0, 15],\n",
        "       [0, 0, 0, 0, 0, 13, 0, 9],\n",
        "       [0, 0, 0, 0, 0, 11, 20, 0],\n",
        "       [12, 0, 0, 10, 14, 0, 14, 0],\n",
        "       [16, 13, 0, 0, 13, 12, 0, 0],\n",
        "       [0, 12, 10, 14, 0, 0, 0, 0]])"
      ],
      "metadata": {
        "id": "BPrJA2jDmL5H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bus = np.array([t1, t2, t3, t4, t5, t6, t7, t8, t9, t10])\n",
        "bus;"
      ],
      "metadata": {
        "id": "gQYRqazJ_Gnn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bus.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vxw8WLngKAkD",
        "outputId": "6a3172ab-52b6-4eb9-c0f5-755a88d72dbd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10, 8, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bus[0].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PLWYSjPDU-Of",
        "outputId": "56b81333-8806-4d1b-922d-7edd18748704"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0v_ihnBozZND"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bus_traversable_distance = [compute_travel_time(density, bus[i], \"bus\")[0] for i in range(10)]\n",
        "bus_traversable_distance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XxXt2A9AJzu_",
        "outputId": "4e394feb-fe88-4721-de63-b2907dad6b72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-70-c492ad516f4e>:27: RuntimeWarning: divide by zero encountered in true_divide\n",
            "  travel_time = D_l / (flow * (1 - density) + traversable_distance)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[0.  , 0.12, 0.  , 0.  , 0.  , 0.19, 0.17, 0.  ],\n",
              "        [0.11, 0.  , 0.09, 0.  , 0.  , 0.17, 0.13, 0.11],\n",
              "        [0.  , 0.1 , 0.  , 0.  , 0.  , 0.  , 0.  , 0.12],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.15, 0.  , 0.12],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.12, 0.17, 0.  ],\n",
              "        [0.14, 0.  , 0.  , 0.15, 0.17, 0.  , 0.16, 0.  ],\n",
              "        [0.16, 0.13, 0.  , 0.  , 0.14, 0.13, 0.  , 0.  ],\n",
              "        [0.  , 0.19, 0.14, 0.11, 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 0.17, 0.  , 0.  , 0.  , 0.12, 0.14, 0.  ],\n",
              "        [0.1 , 0.  , 0.11, 0.  , 0.  , 0.11, 0.15, 0.18],\n",
              "        [0.  , 0.14, 0.  , 0.  , 0.  , 0.  , 0.  , 0.15],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.13, 0.  , 0.14],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.11, 0.13, 0.  ],\n",
              "        [0.15, 0.  , 0.  , 0.13, 0.17, 0.  , 0.14, 0.  ],\n",
              "        [0.18, 0.13, 0.  , 0.  , 0.19, 0.11, 0.  , 0.  ],\n",
              "        [0.  , 0.12, 0.13, 0.1 , 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 0.08, 0.  , 0.  , 0.  , 0.11, 0.17, 0.  ],\n",
              "        [0.07, 0.  , 0.18, 0.  , 0.  , 0.12, 0.17, 0.19],\n",
              "        [0.  , 0.11, 0.  , 0.  , 0.  , 0.  , 0.  , 0.17],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.14, 0.  , 0.15],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.12, 0.14, 0.  ],\n",
              "        [0.09, 0.  , 0.  , 0.13, 0.11, 0.  , 0.12, 0.  ],\n",
              "        [0.12, 0.14, 0.  , 0.  , 0.12, 0.12, 0.  , 0.  ],\n",
              "        [0.  , 0.2 , 0.1 , 0.1 , 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 0.15, 0.  , 0.  , 0.  , 0.13, 0.14, 0.  ],\n",
              "        [0.1 , 0.  , 0.14, 0.  , 0.  , 0.1 , 0.11, 0.1 ],\n",
              "        [0.  , 0.16, 0.  , 0.  , 0.  , 0.  , 0.  , 0.16],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.14, 0.  , 0.16],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.12, 0.13, 0.  ],\n",
              "        [0.11, 0.  , 0.  , 0.12, 0.2 , 0.  , 0.12, 0.  ],\n",
              "        [0.12, 0.15, 0.  , 0.  , 0.21, 0.12, 0.  , 0.  ],\n",
              "        [0.  , 0.15, 0.07, 0.11, 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 0.17, 0.  , 0.  , 0.  , 0.17, 0.13, 0.  ],\n",
              "        [0.15, 0.  , 0.14, 0.  , 0.  , 0.16, 0.14, 0.13],\n",
              "        [0.  , 0.05, 0.  , 0.  , 0.  , 0.  , 0.  , 0.15],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.14, 0.  , 0.2 ],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.14, 0.18, 0.  ],\n",
              "        [0.08, 0.  , 0.  , 0.13, 0.15, 0.  , 0.16, 0.  ],\n",
              "        [0.1 , 0.14, 0.  , 0.  , 0.2 , 0.12, 0.  , 0.  ],\n",
              "        [0.  , 0.19, 0.1 , 0.16, 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 0.21, 0.  , 0.  , 0.  , 0.13, 0.14, 0.  ],\n",
              "        [0.12, 0.  , 0.17, 0.  , 0.  , 0.16, 0.16, 0.14],\n",
              "        [0.  , 0.19, 0.  , 0.  , 0.  , 0.  , 0.  , 0.17],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.12, 0.  , 0.15],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.11, 0.12, 0.  ],\n",
              "        [0.08, 0.  , 0.  , 0.15, 0.13, 0.  , 0.1 , 0.  ],\n",
              "        [0.23, 0.14, 0.  , 0.  , 0.17, 0.1 , 0.  , 0.  ],\n",
              "        [0.  , 0.12, 0.16, 0.19, 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 0.15, 0.  , 0.  , 0.  , 0.14, 0.13, 0.  ],\n",
              "        [0.22, 0.  , 0.12, 0.  , 0.  , 0.15, 0.18, 0.15],\n",
              "        [0.  , 0.14, 0.  , 0.  , 0.  , 0.  , 0.  , 0.1 ],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.09, 0.  , 0.08],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.1 , 0.14, 0.  ],\n",
              "        [0.17, 0.  , 0.  , 0.15, 0.14, 0.  , 0.19, 0.  ],\n",
              "        [0.19, 0.18, 0.  , 0.  , 0.18, 0.13, 0.  , 0.  ],\n",
              "        [0.  , 0.2 , 0.09, 0.15, 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 0.1 , 0.  , 0.  , 0.  , 0.15, 0.15, 0.  ],\n",
              "        [0.12, 0.  , 0.11, 0.  , 0.  , 0.09, 0.19, 0.18],\n",
              "        [0.  , 0.14, 0.  , 0.  , 0.  , 0.  , 0.  , 0.1 ],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.17, 0.  , 0.2 ],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.14, 0.16, 0.  ],\n",
              "        [0.17, 0.  , 0.  , 0.13, 0.19, 0.  , 0.17, 0.  ],\n",
              "        [0.16, 0.17, 0.  , 0.  , 0.01, 0.12, 0.  , 0.  ],\n",
              "        [0.  , 0.12, 0.1 , 0.1 , 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 0.1 , 0.  , 0.  , 0.  , 0.15, 0.13, 0.  ],\n",
              "        [0.14, 0.  , 0.14, 0.  , 0.  , 0.1 , 0.17, 0.1 ],\n",
              "        [0.  , 0.11, 0.  , 0.  , 0.  , 0.  , 0.  , 0.11],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.13, 0.  , 0.15],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.11, 0.15, 0.  ],\n",
              "        [0.12, 0.  , 0.  , 0.18, 0.18, 0.  , 0.2 , 0.  ],\n",
              "        [0.19, 0.15, 0.  , 0.  , 0.15, 0.17, 0.  , 0.  ],\n",
              "        [0.  , 0.2 , 0.16, 0.14, 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 0.1 , 0.  , 0.  , 0.  , 0.2 , 0.09, 0.  ],\n",
              "        [0.17, 0.  , 0.12, 0.  , 0.  , 0.1 , 0.1 , 0.11],\n",
              "        [0.  , 0.14, 0.  , 0.  , 0.  , 0.  , 0.  , 0.15],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.13, 0.  , 0.09],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.11, 0.2 , 0.  ],\n",
              "        [0.12, 0.  , 0.  , 0.1 , 0.14, 0.  , 0.14, 0.  ],\n",
              "        [0.16, 0.13, 0.  , 0.  , 0.13, 0.12, 0.  , 0.  ],\n",
              "        [0.  , 0.12, 0.1 , 0.14, 0.  , 0.  , 0.  , 0.  ]])]"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bus_time_travel = [compute_travel_time(density, bus[i], \"bus\")[1] for i in range(10)]\n",
        "bus_time_travel;"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SW767wNzKDy5",
        "outputId": "6fe696e9-3439-4d57-93fe-2e263d0eab94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-70-c492ad516f4e>:27: RuntimeWarning: divide by zero encountered in true_divide\n",
            "  travel_time = D_l / (flow * (1 - density) + traversable_distance)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## TRUCK"
      ],
      "metadata": {
        "id": "hq61FnVjrJHS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "truck = [np.array([[0, 12, 0, 0, 0, 19, 17, 0],\n",
        "        [11, 0, 9, 0, 0, 17, 13, 11],\n",
        "        [0, 10, 0, 0, 0, 0, 0, 12],\n",
        "        [0, 0, 0, 0, 0, 15, 0, 12],\n",
        "        [0, 0, 0, 0, 0, 12, 17, 0],\n",
        "        [14, 0, 0, 15, 17, 0, 16, 0],\n",
        "        [16, 13, 0, 0, 14, 13, 0, 0],\n",
        "        [0, 19, 14, 11, 0, 0, 0, 0]]),\n",
        "\n",
        "        np.array([[0, 17, 0, 0, 0, 12, 14, 0],\n",
        "        [10, 0, 11, 0, 0, 11, 15, 18],\n",
        "        [0, 14, 0, 0, 0, 0, 0, 15],\n",
        "        [0, 0, 0, 0, 0, 13, 0, 14],\n",
        "        [0, 0, 0, 0, 0, 11, 13, 0],\n",
        "        [15, 0, 0, 13, 17, 0, 14, 0],\n",
        "        [18, 13, 0, 0, 19, 11, 0, 0],\n",
        "        [0, 12, 13, 10, 0, 0, 0, 0]]),\n",
        "\n",
        "        np.array([[0, 8, 0, 0, 0, 11, 17, 0],\n",
        "        [7, 0, 18, 0, 0, 12, 17, 19],\n",
        "        [0, 11, 0, 0, 0, 0, 0, 17],\n",
        "        [0, 0, 0, 0, 0, 14, 0, 15],\n",
        "        [0, 0, 0, 0, 0, 12, 14, 0],\n",
        "        [9, 0, 0, 13, 11, 0, 12, 0],\n",
        "        [12, 14, 0, 0, 12, 12, 0, 0],\n",
        "        [0, 20, 10, 10, 0, 0, 0, 0]]),\n",
        "\n",
        "        np.array([[0, 15, 0, 0, 0, 13, 14, 0],\n",
        "        [10, 0, 14, 0, 0, 10, 11, 10],\n",
        "        [0, 16, 0, 0, 0, 0, 0, 16],\n",
        "        [0, 0, 0, 0, 0, 14, 0, 16],\n",
        "        [0, 0, 0, 0, 0, 12, 13, 0],\n",
        "        [11, 0, 0, 12, 20, 0, 12, 0],\n",
        "        [12, 15, 0, 0, 21, 12, 0, 0],\n",
        "        [0, 15, 7, 11, 0, 0, 0, 0]]),\n",
        "\n",
        "        np.array([[0, 17, 0, 0, 0, 17, 13, 0],\n",
        "        [15, 0, 14, 0, 0, 16, 14, 13],\n",
        "        [0, 5, 0, 0, 0, 0, 0, 15],\n",
        "        [0, 0, 0, 0, 0, 14, 0, 20],\n",
        "        [0, 0, 0, 0, 0, 14, 18, 0],\n",
        "        [8, 0, 0, 13, 15, 0, 16, 0],\n",
        "        [10, 14, 0, 0, 20, 12, 0, 0],\n",
        "        [0, 19, 10, 16, 0, 0, 0, 0]]),\n",
        "\n",
        "        np.array([[0, 21, 0, 0, 0, 13, 14, 0],\n",
        "        [12, 0, 17, 0, 0, 16, 16, 14],\n",
        "        [0, 19, 0, 0, 0, 0, 0, 17],\n",
        "        [0, 0, 0, 0, 0, 12, 0, 15],\n",
        "        [0, 0, 0, 0, 0, 11, 12, 0],\n",
        "        [8, 0, 0, 15, 13, 0, 10, 0],\n",
        "        [23, 14, 0, 0, 17, 10, 0, 0],\n",
        "        [0, 12, 16, 19, 0, 0, 0, 0]]),\n",
        "\n",
        "        np.array([[0, 15, 0, 0, 0, 14, 13, 0],\n",
        "        [22, 0, 12, 0, 0, 15, 18, 15],\n",
        "        [0, 14, 0, 0, 0, 0, 0, 10],\n",
        "        [0, 0, 0, 0, 0, 9, 0, 8],\n",
        "        [0, 0, 0, 0, 0, 10, 14, 0],\n",
        "        [17, 0, 0, 15, 14, 0, 19, 0],\n",
        "        [19, 18, 0, 0, 18, 13, 0, 0],\n",
        "        [0, 20, 9, 15, 0, 0, 0, 0]]),\n",
        "\n",
        "        np.array([[0, 10, 0, 0, 0, 15, 15, 0],\n",
        "        [12, 0, 11, 0, 0, 9, 19, 18],\n",
        "        [0, 14, 0, 0, 0, 0, 0, 10],\n",
        "        [0, 0, 0, 0, 0, 17, 0, 20],\n",
        "        [0, 0, 0, 0, 0, 14, 16, 0],\n",
        "        [17, 0, 0, 13, 19, 0, 17, 0],\n",
        "        [16, 17, 0, 0, 1, 12, 0, 0],\n",
        "        [0, 12, 10, 10, 0, 0, 0, 0]]),\n",
        "\n",
        "        np.array([[0, 10, 0, 0, 0, 15, 13, 0],\n",
        "        [14, 0, 14, 0, 0, 10, 17, 10],\n",
        "        [0, 11, 0, 0, 0, 0, 0, 11],\n",
        "        [0, 0, 0, 0, 0, 13, 0, 15],\n",
        "        [0, 0, 0, 0, 0, 11, 15, 0],\n",
        "        [12, 0, 0, 18, 18, 0, 20, 0],\n",
        "        [19, 15, 0, 0, 15, 17, 0, 0],\n",
        "        [0, 20, 16, 14, 0, 0, 0, 0]]),\n",
        "\n",
        "         np.array([[0, 10, 0, 0, 0, 20, 9, 0],\n",
        "        [17, 0, 12, 0, 0, 10, 10, 11],\n",
        "        [0, 14, 0, 0, 0, 0, 0, 15],\n",
        "        [0, 0, 0, 0, 0, 13, 0, 9],\n",
        "        [0, 0, 0, 0, 0, 11, 20, 0],\n",
        "        [12, 0, 0, 10, 14, 0, 14, 0],\n",
        "        [16, 13, 0, 0, 13, 12, 0, 0],\n",
        "        [0, 12, 10, 14, 0, 0, 0, 0]])]"
      ],
      "metadata": {
        "id": "uQ8LfgYIrGuv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(truck[0].reshape((8,8,1)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uJNHo1nw44d_",
        "outputId": "14b0dab2-7474-498d-8b5b-5182d3fa93d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[[ 0]\n",
            "  [12]\n",
            "  [ 0]\n",
            "  [ 0]\n",
            "  [ 0]\n",
            "  [19]\n",
            "  [17]\n",
            "  [ 0]]\n",
            "\n",
            " [[11]\n",
            "  [ 0]\n",
            "  [ 9]\n",
            "  [ 0]\n",
            "  [ 0]\n",
            "  [17]\n",
            "  [13]\n",
            "  [11]]\n",
            "\n",
            " [[ 0]\n",
            "  [10]\n",
            "  [ 0]\n",
            "  [ 0]\n",
            "  [ 0]\n",
            "  [ 0]\n",
            "  [ 0]\n",
            "  [12]]\n",
            "\n",
            " [[ 0]\n",
            "  [ 0]\n",
            "  [ 0]\n",
            "  [ 0]\n",
            "  [ 0]\n",
            "  [15]\n",
            "  [ 0]\n",
            "  [12]]\n",
            "\n",
            " [[ 0]\n",
            "  [ 0]\n",
            "  [ 0]\n",
            "  [ 0]\n",
            "  [ 0]\n",
            "  [12]\n",
            "  [17]\n",
            "  [ 0]]\n",
            "\n",
            " [[14]\n",
            "  [ 0]\n",
            "  [ 0]\n",
            "  [15]\n",
            "  [17]\n",
            "  [ 0]\n",
            "  [16]\n",
            "  [ 0]]\n",
            "\n",
            " [[16]\n",
            "  [13]\n",
            "  [ 0]\n",
            "  [ 0]\n",
            "  [14]\n",
            "  [13]\n",
            "  [ 0]\n",
            "  [ 0]]\n",
            "\n",
            " [[ 0]\n",
            "  [19]\n",
            "  [14]\n",
            "  [11]\n",
            "  [ 0]\n",
            "  [ 0]\n",
            "  [ 0]\n",
            "  [ 0]]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "truck = np.array(truck)\n",
        "truck;"
      ],
      "metadata": {
        "id": "LDmupXo98xcD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "truck_traversable_distance = [compute_travel_time(density, truck[i], \"truck\")[0] for i in range(10)]\n",
        "truck_traversable_distance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ejnlBARLKNnP",
        "outputId": "47ed0165-9d74-4c97-a52c-00a30859c834"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-70-c492ad516f4e>:27: RuntimeWarning: divide by zero encountered in true_divide\n",
            "  travel_time = D_l / (flow * (1 - density) + traversable_distance)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[0.  , 0.12, 0.  , 0.  , 0.  , 0.19, 0.17, 0.  ],\n",
              "        [0.11, 0.  , 0.09, 0.  , 0.  , 0.17, 0.13, 0.11],\n",
              "        [0.  , 0.1 , 0.  , 0.  , 0.  , 0.  , 0.  , 0.12],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.15, 0.  , 0.12],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.12, 0.17, 0.  ],\n",
              "        [0.14, 0.  , 0.  , 0.15, 0.17, 0.  , 0.16, 0.  ],\n",
              "        [0.16, 0.13, 0.  , 0.  , 0.14, 0.13, 0.  , 0.  ],\n",
              "        [0.  , 0.19, 0.14, 0.11, 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 0.17, 0.  , 0.  , 0.  , 0.12, 0.14, 0.  ],\n",
              "        [0.1 , 0.  , 0.11, 0.  , 0.  , 0.11, 0.15, 0.18],\n",
              "        [0.  , 0.14, 0.  , 0.  , 0.  , 0.  , 0.  , 0.15],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.13, 0.  , 0.14],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.11, 0.13, 0.  ],\n",
              "        [0.15, 0.  , 0.  , 0.13, 0.17, 0.  , 0.14, 0.  ],\n",
              "        [0.18, 0.13, 0.  , 0.  , 0.19, 0.11, 0.  , 0.  ],\n",
              "        [0.  , 0.12, 0.13, 0.1 , 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 0.08, 0.  , 0.  , 0.  , 0.11, 0.17, 0.  ],\n",
              "        [0.07, 0.  , 0.18, 0.  , 0.  , 0.12, 0.17, 0.19],\n",
              "        [0.  , 0.11, 0.  , 0.  , 0.  , 0.  , 0.  , 0.17],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.14, 0.  , 0.15],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.12, 0.14, 0.  ],\n",
              "        [0.09, 0.  , 0.  , 0.13, 0.11, 0.  , 0.12, 0.  ],\n",
              "        [0.12, 0.14, 0.  , 0.  , 0.12, 0.12, 0.  , 0.  ],\n",
              "        [0.  , 0.2 , 0.1 , 0.1 , 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 0.15, 0.  , 0.  , 0.  , 0.13, 0.14, 0.  ],\n",
              "        [0.1 , 0.  , 0.14, 0.  , 0.  , 0.1 , 0.11, 0.1 ],\n",
              "        [0.  , 0.16, 0.  , 0.  , 0.  , 0.  , 0.  , 0.16],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.14, 0.  , 0.16],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.12, 0.13, 0.  ],\n",
              "        [0.11, 0.  , 0.  , 0.12, 0.2 , 0.  , 0.12, 0.  ],\n",
              "        [0.12, 0.15, 0.  , 0.  , 0.21, 0.12, 0.  , 0.  ],\n",
              "        [0.  , 0.15, 0.07, 0.11, 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 0.17, 0.  , 0.  , 0.  , 0.17, 0.13, 0.  ],\n",
              "        [0.15, 0.  , 0.14, 0.  , 0.  , 0.16, 0.14, 0.13],\n",
              "        [0.  , 0.05, 0.  , 0.  , 0.  , 0.  , 0.  , 0.15],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.14, 0.  , 0.2 ],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.14, 0.18, 0.  ],\n",
              "        [0.08, 0.  , 0.  , 0.13, 0.15, 0.  , 0.16, 0.  ],\n",
              "        [0.1 , 0.14, 0.  , 0.  , 0.2 , 0.12, 0.  , 0.  ],\n",
              "        [0.  , 0.19, 0.1 , 0.16, 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 0.21, 0.  , 0.  , 0.  , 0.13, 0.14, 0.  ],\n",
              "        [0.12, 0.  , 0.17, 0.  , 0.  , 0.16, 0.16, 0.14],\n",
              "        [0.  , 0.19, 0.  , 0.  , 0.  , 0.  , 0.  , 0.17],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.12, 0.  , 0.15],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.11, 0.12, 0.  ],\n",
              "        [0.08, 0.  , 0.  , 0.15, 0.13, 0.  , 0.1 , 0.  ],\n",
              "        [0.23, 0.14, 0.  , 0.  , 0.17, 0.1 , 0.  , 0.  ],\n",
              "        [0.  , 0.12, 0.16, 0.19, 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 0.15, 0.  , 0.  , 0.  , 0.14, 0.13, 0.  ],\n",
              "        [0.22, 0.  , 0.12, 0.  , 0.  , 0.15, 0.18, 0.15],\n",
              "        [0.  , 0.14, 0.  , 0.  , 0.  , 0.  , 0.  , 0.1 ],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.09, 0.  , 0.08],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.1 , 0.14, 0.  ],\n",
              "        [0.17, 0.  , 0.  , 0.15, 0.14, 0.  , 0.19, 0.  ],\n",
              "        [0.19, 0.18, 0.  , 0.  , 0.18, 0.13, 0.  , 0.  ],\n",
              "        [0.  , 0.2 , 0.09, 0.15, 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 0.1 , 0.  , 0.  , 0.  , 0.15, 0.15, 0.  ],\n",
              "        [0.12, 0.  , 0.11, 0.  , 0.  , 0.09, 0.19, 0.18],\n",
              "        [0.  , 0.14, 0.  , 0.  , 0.  , 0.  , 0.  , 0.1 ],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.17, 0.  , 0.2 ],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.14, 0.16, 0.  ],\n",
              "        [0.17, 0.  , 0.  , 0.13, 0.19, 0.  , 0.17, 0.  ],\n",
              "        [0.16, 0.17, 0.  , 0.  , 0.01, 0.12, 0.  , 0.  ],\n",
              "        [0.  , 0.12, 0.1 , 0.1 , 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 0.1 , 0.  , 0.  , 0.  , 0.15, 0.13, 0.  ],\n",
              "        [0.14, 0.  , 0.14, 0.  , 0.  , 0.1 , 0.17, 0.1 ],\n",
              "        [0.  , 0.11, 0.  , 0.  , 0.  , 0.  , 0.  , 0.11],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.13, 0.  , 0.15],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.11, 0.15, 0.  ],\n",
              "        [0.12, 0.  , 0.  , 0.18, 0.18, 0.  , 0.2 , 0.  ],\n",
              "        [0.19, 0.15, 0.  , 0.  , 0.15, 0.17, 0.  , 0.  ],\n",
              "        [0.  , 0.2 , 0.16, 0.14, 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 0.1 , 0.  , 0.  , 0.  , 0.2 , 0.09, 0.  ],\n",
              "        [0.17, 0.  , 0.12, 0.  , 0.  , 0.1 , 0.1 , 0.11],\n",
              "        [0.  , 0.14, 0.  , 0.  , 0.  , 0.  , 0.  , 0.15],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.13, 0.  , 0.09],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.11, 0.2 , 0.  ],\n",
              "        [0.12, 0.  , 0.  , 0.1 , 0.14, 0.  , 0.14, 0.  ],\n",
              "        [0.16, 0.13, 0.  , 0.  , 0.13, 0.12, 0.  , 0.  ],\n",
              "        [0.  , 0.12, 0.1 , 0.14, 0.  , 0.  , 0.  , 0.  ]])]"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "truck_time_travel = [compute_travel_time(density, truck[i], \"truck\")[1] for i in range(10)]\n",
        "truck_time_travel = np.nan_to_num(truck_time_travel , nan=0.0, posinf=0.0, neginf=0.0)\n",
        "truck_time_travel;"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ts04La18Kb3C",
        "outputId": "8415350f-9a16-4da0-c6d5-c3aa0e0fd094"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-70-c492ad516f4e>:27: RuntimeWarning: divide by zero encountered in true_divide\n",
            "  travel_time = D_l / (flow * (1 - density) + traversable_distance)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Motorcycle"
      ],
      "metadata": {
        "id": "QGAWZvr6AOVZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "motorcycle = np.array([np.array([[0, 11, 0, 0, 0, 12, 11, 0],\n",
        "        [7, 0, 8, 0, 0, 21, 17, 19],\n",
        "        [0, 10, 0, 0, 0, 0, 0, 9],\n",
        "        [0, 0, 0, 0, 0, 6, 0, 11],\n",
        "        [0, 0, 0, 0, 0, 7, 12, 0],\n",
        "        [8, 0, 0, 11, 7, 0, 6, 0],\n",
        "        [6, 5, 0, 0, 8, 9, 0, 0],\n",
        "        [0, 7, 8, 6, 0, 0, 0, 0]]),\n",
        "\n",
        "np.array([[0, 14, 0, 0, 0, 7, 8, 0],\n",
        "        [4, 0, 4, 0, 0, 8, 2, 3],\n",
        "        [0, 7, 0, 0, 0, 0, 0, 15],\n",
        "        [0, 0, 0, 0, 0, 17, 0, 12],\n",
        "        [0, 0, 0, 0, 0, 14, 19, 0],\n",
        "        [20, 0, 0, 12, 4, 0, 6, 0],\n",
        "        [9, 14, 0, 0, 16, 19, 0, 0],\n",
        "        [0, 18, 13, 15, 0, 0, 0, 0]]),\n",
        "\n",
        "np.array([[0, 11, 0, 0, 0, 14, 8, 0],\n",
        "        [6, 0, 10, 0, 0, 13, 20, 19],\n",
        "        [0, 5, 0, 0, 0, 0, 0, 10],\n",
        "        [0, 0, 0, 0, 0, 8, 0, 9],\n",
        "        [0, 0, 0, 0, 0, 1, 2, 0],\n",
        "        [10, 11, 13, 14, 9, 0, 4, 0],\n",
        "        [13, 5, 0, 0, 8, 6, 0, 0],\n",
        "        [0, 12, 14, 6, 0, 0, 0, 0]]),\n",
        "\n",
        "np.array([[0, 20, 0, 0, 0, 14, 11, 0],\n",
        "        [17, 0, 6, 0, 0, 16, 10, 1],\n",
        "        [0, 3, 0, 0, 0, 0, 0, 3],\n",
        "        [0, 0, 0, 0, 0, 15, 0, 10],\n",
        "        [0, 0, 0, 0, 0, 12, 6, 0],\n",
        "        [13, 14, 12, 16, 6, 0, 8, 0],\n",
        "        [12, 7, 0, 0, 9, 15, 0, 0],\n",
        "        [0, 7, 8, 9, 0, 0, 0, 0]]),\n",
        "\n",
        " np.array([[0, 15, 0, 0, 0, 12, 7, 0],\n",
        "        [13, 0, 6, 0, 0, 12, 8, 9],\n",
        "        [0, 11, 0, 0, 0, 0, 0, 14],\n",
        "        [0, 0, 0, 0, 0, 15, 0, 15],\n",
        "        [0, 0, 0, 0, 0, 19, 16, 0],\n",
        "        [17, 12, 15, 14, 10, 0, 18, 0],\n",
        "        [4, 6, 0, 0, 6, 14, 0, 0],\n",
        "        [0, 8, 13, 8, 0, 0, 0, 0]]),\n",
        "\n",
        "np.array([[0, 3, 0, 0, 0, 12, 19, 0],\n",
        "        [16, 0, 2, 0, 0, 10, 13, 16],\n",
        "        [0, 8, 0, 0, 0, 5, 6, 15],\n",
        "        [0, 0, 0, 0, 0, 9, 0, 11],\n",
        "        [0, 0, 0, 0, 0, 14, 13, 0],\n",
        "        [13, 7, 12, 9, 13, 0, 15, 0],\n",
        "        [14, 13, 0, 0, 15, 17, 0, 0],\n",
        "        [0, 20, 18, 9, 0, 0, 0, 0]]),\n",
        "\n",
        "np.array([[0, 14, 0, 0, 0, 7, 9, 0],\n",
        "        [2, 0, 5, 0, 0, 12, 20, 17],\n",
        "        [0, 16, 0, 0, 0, 7, 0, 9],\n",
        "        [0, 0, 0, 0, 0, 14, 17, 18],\n",
        "        [0, 0, 0, 0, 0, 14, 20, 0],\n",
        "        [12, 13, 15, 12, 12, 0, 10, 0],\n",
        "        [8, 9, 0, 0, 10, 11, 0, 0],\n",
        "        [0, 6, 11, 16, 0, 0, 0, 0]]),\n",
        "\n",
        " np.array([[0, 13, 0, 0, 0, 9, 8, 0],\n",
        "        [14, 0, 12, 0, 0, 15, 15, 17],\n",
        "        [0, 16, 0, 0, 0, 9, 0, 7],\n",
        "        [0, 0, 0, 0, 0, 8, 16, 18],\n",
        "        [0, 0, 0, 0, 0, 16, 17, 0],\n",
        "        [10, 10, 10, 16, 17, 0, 12, 0],\n",
        "        [13, 14, 0, 0, 16, 16, 0, 0],\n",
        "        [0, 12, 12, 12, 0, 0, 0, 0]]),\n",
        "\n",
        " np.array([[0, 17, 0, 0, 0, 18, 16, 0],\n",
        "        [16, 0, 13, 0, 0, 16, 14, 15],\n",
        "        [0, 20, 0, 0, 0, 16, 0, 9],\n",
        "        [0, 0, 0, 0, 0, 6, 8, 7],\n",
        "        [0, 0, 0, 0, 0, 12, 12, 0],\n",
        "        [10, 13, 14, 8, 6, 0, 14, 0],\n",
        "        [12, 10, 0, 0, 8, 9, 0, 0],\n",
        "        [0, 19, 17, 11, 0, 0, 0, 0]]),\n",
        "np.array([[0, 13, 0, 0, 0, 19, 9, 0],\n",
        "        [6, 0, 9, 0, 0, 11, 16, 12],\n",
        "        [0, 6, 0, 0, 0, 10, 0, 13],\n",
        "        [0, 0, 0, 0, 0, 14, 15, 13],\n",
        "        [0, 0, 0, 0, 0, 15, 13, 0],\n",
        "        [9, 7, 12, 16, 7, 0, 17, 0],\n",
        "        [13, 14, 0, 0, 19, 16, 0, 0],\n",
        "        [0, 6, 10, 13, 0, 0, 0, 0]])\n",
        " ])"
      ],
      "metadata": {
        "id": "M2vcNBoH9EFG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "motorcycle;"
      ],
      "metadata": {
        "id": "witVEJnPA5bl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "motorcycle_traversable_distance = [compute_travel_time(density, motorcycle[i], \"motorcycle\")[0] for i in range(10)]\n",
        "motorcycle_traversable_distance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O2jDhymJKyBR",
        "outputId": "d99f97a7-4770-48f3-c253-a19ed1abaf29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-70-c492ad516f4e>:27: RuntimeWarning: divide by zero encountered in true_divide\n",
            "  travel_time = D_l / (flow * (1 - density) + traversable_distance)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[0.  , 0.22, 0.  , 0.  , 0.  , 0.24, 0.22, 0.  ],\n",
              "        [0.14, 0.  , 0.16, 0.  , 0.  , 0.42, 0.34, 0.38],\n",
              "        [0.  , 0.2 , 0.  , 0.  , 0.  , 0.  , 0.  , 0.18],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.12, 0.  , 0.22],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.14, 0.24, 0.  ],\n",
              "        [0.16, 0.  , 0.  , 0.22, 0.14, 0.  , 0.12, 0.  ],\n",
              "        [0.12, 0.1 , 0.  , 0.  , 0.16, 0.18, 0.  , 0.  ],\n",
              "        [0.  , 0.14, 0.16, 0.12, 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 0.28, 0.  , 0.  , 0.  , 0.14, 0.16, 0.  ],\n",
              "        [0.08, 0.  , 0.08, 0.  , 0.  , 0.16, 0.04, 0.06],\n",
              "        [0.  , 0.14, 0.  , 0.  , 0.  , 0.  , 0.  , 0.3 ],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.34, 0.  , 0.24],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.28, 0.38, 0.  ],\n",
              "        [0.4 , 0.  , 0.  , 0.24, 0.08, 0.  , 0.12, 0.  ],\n",
              "        [0.18, 0.28, 0.  , 0.  , 0.32, 0.38, 0.  , 0.  ],\n",
              "        [0.  , 0.36, 0.26, 0.3 , 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 0.22, 0.  , 0.  , 0.  , 0.28, 0.16, 0.  ],\n",
              "        [0.12, 0.  , 0.2 , 0.  , 0.  , 0.26, 0.4 , 0.38],\n",
              "        [0.  , 0.1 , 0.  , 0.  , 0.  , 0.  , 0.  , 0.2 ],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.16, 0.  , 0.18],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.02, 0.04, 0.  ],\n",
              "        [0.2 , 0.22, 0.26, 0.28, 0.18, 0.  , 0.08, 0.  ],\n",
              "        [0.26, 0.1 , 0.  , 0.  , 0.16, 0.12, 0.  , 0.  ],\n",
              "        [0.  , 0.24, 0.28, 0.12, 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 0.4 , 0.  , 0.  , 0.  , 0.28, 0.22, 0.  ],\n",
              "        [0.34, 0.  , 0.12, 0.  , 0.  , 0.32, 0.2 , 0.02],\n",
              "        [0.  , 0.06, 0.  , 0.  , 0.  , 0.  , 0.  , 0.06],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.3 , 0.  , 0.2 ],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.24, 0.12, 0.  ],\n",
              "        [0.26, 0.28, 0.24, 0.32, 0.12, 0.  , 0.16, 0.  ],\n",
              "        [0.24, 0.14, 0.  , 0.  , 0.18, 0.3 , 0.  , 0.  ],\n",
              "        [0.  , 0.14, 0.16, 0.18, 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 0.3 , 0.  , 0.  , 0.  , 0.24, 0.14, 0.  ],\n",
              "        [0.26, 0.  , 0.12, 0.  , 0.  , 0.24, 0.16, 0.18],\n",
              "        [0.  , 0.22, 0.  , 0.  , 0.  , 0.  , 0.  , 0.28],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.3 , 0.  , 0.3 ],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.38, 0.32, 0.  ],\n",
              "        [0.34, 0.24, 0.3 , 0.28, 0.2 , 0.  , 0.36, 0.  ],\n",
              "        [0.08, 0.12, 0.  , 0.  , 0.12, 0.28, 0.  , 0.  ],\n",
              "        [0.  , 0.16, 0.26, 0.16, 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 0.06, 0.  , 0.  , 0.  , 0.24, 0.38, 0.  ],\n",
              "        [0.32, 0.  , 0.04, 0.  , 0.  , 0.2 , 0.26, 0.32],\n",
              "        [0.  , 0.16, 0.  , 0.  , 0.  , 0.1 , 0.12, 0.3 ],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.18, 0.  , 0.22],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.28, 0.26, 0.  ],\n",
              "        [0.26, 0.14, 0.24, 0.18, 0.26, 0.  , 0.3 , 0.  ],\n",
              "        [0.28, 0.26, 0.  , 0.  , 0.3 , 0.34, 0.  , 0.  ],\n",
              "        [0.  , 0.4 , 0.36, 0.18, 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 0.28, 0.  , 0.  , 0.  , 0.14, 0.18, 0.  ],\n",
              "        [0.04, 0.  , 0.1 , 0.  , 0.  , 0.24, 0.4 , 0.34],\n",
              "        [0.  , 0.32, 0.  , 0.  , 0.  , 0.14, 0.  , 0.18],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.28, 0.34, 0.36],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.28, 0.4 , 0.  ],\n",
              "        [0.24, 0.26, 0.3 , 0.24, 0.24, 0.  , 0.2 , 0.  ],\n",
              "        [0.16, 0.18, 0.  , 0.  , 0.2 , 0.22, 0.  , 0.  ],\n",
              "        [0.  , 0.12, 0.22, 0.32, 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 0.26, 0.  , 0.  , 0.  , 0.18, 0.16, 0.  ],\n",
              "        [0.28, 0.  , 0.24, 0.  , 0.  , 0.3 , 0.3 , 0.34],\n",
              "        [0.  , 0.32, 0.  , 0.  , 0.  , 0.18, 0.  , 0.14],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.16, 0.32, 0.36],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.32, 0.34, 0.  ],\n",
              "        [0.2 , 0.2 , 0.2 , 0.32, 0.34, 0.  , 0.24, 0.  ],\n",
              "        [0.26, 0.28, 0.  , 0.  , 0.32, 0.32, 0.  , 0.  ],\n",
              "        [0.  , 0.24, 0.24, 0.24, 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 0.34, 0.  , 0.  , 0.  , 0.36, 0.32, 0.  ],\n",
              "        [0.32, 0.  , 0.26, 0.  , 0.  , 0.32, 0.28, 0.3 ],\n",
              "        [0.  , 0.4 , 0.  , 0.  , 0.  , 0.32, 0.  , 0.18],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.12, 0.16, 0.14],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.24, 0.24, 0.  ],\n",
              "        [0.2 , 0.26, 0.28, 0.16, 0.12, 0.  , 0.28, 0.  ],\n",
              "        [0.24, 0.2 , 0.  , 0.  , 0.16, 0.18, 0.  , 0.  ],\n",
              "        [0.  , 0.38, 0.34, 0.22, 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 0.26, 0.  , 0.  , 0.  , 0.38, 0.18, 0.  ],\n",
              "        [0.12, 0.  , 0.18, 0.  , 0.  , 0.22, 0.32, 0.24],\n",
              "        [0.  , 0.12, 0.  , 0.  , 0.  , 0.2 , 0.  , 0.26],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.28, 0.3 , 0.26],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 0.3 , 0.26, 0.  ],\n",
              "        [0.18, 0.14, 0.24, 0.32, 0.14, 0.  , 0.34, 0.  ],\n",
              "        [0.26, 0.28, 0.  , 0.  , 0.38, 0.32, 0.  , 0.  ],\n",
              "        [0.  , 0.12, 0.2 , 0.26, 0.  , 0.  , 0.  , 0.  ]])]"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "motorcycle_time_travel = [compute_travel_time(density, motorcycle[i], \"motorcycle\")[1] for i in range(10)]\n",
        "motorcycle_time_travel = np.nan_to_num(motorcycle_time_travel  , nan=0.0, posinf=0.0, neginf=0.0)\n",
        "motorcycle_time_travel;"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FsvAXQ2nLC91",
        "outputId": "2f68fa10-eacb-476f-e45b-07c6a74e6ead"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-70-c492ad516f4e>:27: RuntimeWarning: divide by zero encountered in true_divide\n",
            "  travel_time = D_l / (flow * (1 - density) + traversable_distance)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## CAR"
      ],
      "metadata": {
        "id": "piuRn3mqBi-N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "car = np.array([\n",
        "np.array([[0, 8, 0, 0, 0, 17, 14, 0],\n",
        "        [9, 0, 9, 0, 0, 14, 16, 11],\n",
        "        [0, 10, 0, 0, 0, 0, 0, 12],\n",
        "        [0, 0, 0, 0, 0, 11, 0, 12],\n",
        "        [0, 0, 0, 0, 0, 12, 15, 0],\n",
        "        [14, 0, 0, 15, 17, 0, 14, 0],\n",
        "        [17, 13, 0, 0, 12, 10, 0, 0],\n",
        "        [0, 17, 14, 10, 0, 0, 0, 0]]),\n",
        "\n",
        "np.array([[0, 17, 0, 0, 0, 12, 14, 0],\n",
        "        [10, 0, 11, 0, 0, 11, 15, 16],\n",
        "        [0, 14, 0, 0, 0, 0, 0, 14],\n",
        "        [0, 0, 0, 0, 0, 13, 0, 14],\n",
        "        [0, 0, 0, 0, 0, 14, 13, 0],\n",
        "        [15, 0, 0, 14, 18, 0, 10, 0],\n",
        "        [18, 13, 0, 0, 20, 11, 0, 0],\n",
        "        [0, 16, 12, 11, 0, 0, 0, 0]]),\n",
        "\n",
        "np.array([[0, 8, 0, 0, 0, 11, 18, 0],\n",
        "        [6, 0, 18, 0, 0, 12, 17, 20],\n",
        "        [0, 10, 0, 0, 0, 0, 0, 17],\n",
        "        [0, 0, 0, 0, 0, 15, 0, 15],\n",
        "        [0, 0, 0, 0, 0, 12, 14, 0],\n",
        "        [9, 0, 0, 13, 11, 0, 12, 0],\n",
        "        [14, 13, 0, 0, 12, 12, 0, 0],\n",
        "        [0, 19, 9, 11, 0, 0, 0, 0]]),\n",
        "\n",
        "np.array([[0, 15, 0, 0, 0, 12, 14, 0],\n",
        "        [14, 0, 14, 0, 0, 10, 11, 10],\n",
        "        [0, 14, 0, 0, 0, 0, 0, 16],\n",
        "        [0, 0, 0, 0, 0, 14, 0, 16],\n",
        "        [0, 0, 0, 0, 0, 12, 13, 0],\n",
        "        [11, 0, 0, 12, 20, 0, 5, 0],\n",
        "        [12, 15, 0, 0, 20, 12, 0, 0],\n",
        "        [0, 15, 7, 11, 0, 0, 0, 0]]),\n",
        "\n",
        "np.array([[0, 16, 0, 0, 0, 17, 13, 0],\n",
        "        [14, 0, 12, 0, 0, 16, 14, 13],\n",
        "        [0, 5, 0, 0, 0, 0, 0, 15],\n",
        "        [0, 0, 0, 0, 0, 14, 0, 20],\n",
        "        [0, 0, 0, 0, 0, 14, 18, 0],\n",
        "        [8, 0, 0, 13, 12, 0, 15, 0],\n",
        "        [11, 14, 0, 0, 20, 12, 0, 0],\n",
        "        [0, 20, 10, 16, 0, 0, 0, 0]]),\n",
        "\n",
        "np.array([[0, 20, 0, 0, 0, 14, 14, 0],\n",
        "        [14, 0, 18, 0, 0, 16, 16, 14],\n",
        "        [0, 19, 0, 0, 0, 0, 0, 17],\n",
        "        [0, 0, 0, 0, 0, 12, 0, 15],\n",
        "        [0, 0, 0, 0, 0, 11, 12, 0],\n",
        "        [8, 0, 0, 16, 12, 0, 10, 0],\n",
        "        [23, 14, 0, 0, 17, 10, 0, 0],\n",
        "        [0, 12, 18, 19, 0, 0, 0, 0]]),\n",
        "\n",
        "np.array([[0, 15, 0, 0, 0, 14, 12, 0],\n",
        "        [15, 0, 12, 0, 0, 15, 18, 15],\n",
        "        [0, 16, 0, 0, 0, 0, 0, 11],\n",
        "        [0, 0, 0, 0, 0, 9, 0, 8],\n",
        "        [0, 0, 0, 0, 0, 10, 11, 0],\n",
        "        [17, 0, 0, 16, 14, 0, 19, 0],\n",
        "        [19, 18, 0, 0, 18, 13, 0, 0],\n",
        "        [0, 20, 9, 14, 0, 0, 0, 0]]),\n",
        "\n",
        "np.array([[0, 10, 0, 0, 0, 15, 15, 0],\n",
        "        [12, 0, 10, 0, 0, 9, 19, 16],\n",
        "        [0, 12, 0, 0, 0, 0, 0, 10],\n",
        "        [0, 0, 0, 0, 0, 17, 0, 20],\n",
        "        [0, 0, 0, 0, 0, 14, 16, 0],\n",
        "        [17, 0, 0, 13, 18, 0, 16, 0],\n",
        "        [16, 17, 0, 0, 1, 11, 0, 0],\n",
        "        [0, 12, 10, 10, 0, 0, 0, 0]]),\n",
        "\n",
        "np.array([[0, 10, 0, 0, 0, 15, 13, 0],\n",
        "        [12, 0, 15, 0, 0, 10, 17, 10],\n",
        "        [0, 11, 0, 0, 0, 0, 0, 11],\n",
        "        [0, 0, 0, 0, 0, 13, 0, 15],\n",
        "        [0, 0, 0, 0, 0, 11, 15, 0],\n",
        "        [12, 0, 0, 17, 18, 0, 20, 0],\n",
        "        [20, 16, 0, 0, 15, 17, 0, 0],\n",
        "        [0, 20, 16, 14, 0, 0, 0, 0]]),\n",
        "\n",
        " np.array([[0, 10, 0, 0, 0, 20, 9, 0],\n",
        "        [16, 0, 12, 0, 0, 10, 10, 11],\n",
        "        [0, 14, 0, 0, 0, 0, 0, 11],\n",
        "        [0, 0, 0, 0, 0, 13, 0, 9],\n",
        "        [0, 0, 0, 0, 0, 11, 20, 0],\n",
        "        [12, 0, 0, 10, 14, 0, 16, 0],\n",
        "        [16, 20, 0, 0, 13, 10, 0, 0],\n",
        "        [0, 12, 10, 16, 0, 0, 0, 0]]),\n",
        "])"
      ],
      "metadata": {
        "id": "lm6YjM9KBgJC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "car_traversable_distance = [compute_travel_time(density, car[i], \"car\")[0] for i in range(10)]\n",
        "car_traversable_distance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2_O3gYJYGF7k",
        "outputId": "66faf188-0057-4a86-da5f-1f3ffeba42d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-70-c492ad516f4e>:27: RuntimeWarning: divide by zero encountered in true_divide\n",
            "  travel_time = D_l / (flow * (1 - density) + traversable_distance)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[0.  , 1.2 , 0.  , 0.  , 0.  , 2.55, 2.1 , 0.  ],\n",
              "        [1.35, 0.  , 1.35, 0.  , 0.  , 2.1 , 2.4 , 1.65],\n",
              "        [0.  , 1.5 , 0.  , 0.  , 0.  , 0.  , 0.  , 1.8 ],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 1.65, 0.  , 1.8 ],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 1.8 , 2.25, 0.  ],\n",
              "        [2.1 , 0.  , 0.  , 2.25, 2.55, 0.  , 2.1 , 0.  ],\n",
              "        [2.55, 1.95, 0.  , 0.  , 1.8 , 1.5 , 0.  , 0.  ],\n",
              "        [0.  , 2.55, 2.1 , 1.5 , 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 2.55, 0.  , 0.  , 0.  , 1.8 , 2.1 , 0.  ],\n",
              "        [1.5 , 0.  , 1.65, 0.  , 0.  , 1.65, 2.25, 2.4 ],\n",
              "        [0.  , 2.1 , 0.  , 0.  , 0.  , 0.  , 0.  , 2.1 ],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 1.95, 0.  , 2.1 ],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 2.1 , 1.95, 0.  ],\n",
              "        [2.25, 0.  , 0.  , 2.1 , 2.7 , 0.  , 1.5 , 0.  ],\n",
              "        [2.7 , 1.95, 0.  , 0.  , 3.  , 1.65, 0.  , 0.  ],\n",
              "        [0.  , 2.4 , 1.8 , 1.65, 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 1.2 , 0.  , 0.  , 0.  , 1.65, 2.7 , 0.  ],\n",
              "        [0.9 , 0.  , 2.7 , 0.  , 0.  , 1.8 , 2.55, 3.  ],\n",
              "        [0.  , 1.5 , 0.  , 0.  , 0.  , 0.  , 0.  , 2.55],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 2.25, 0.  , 2.25],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 1.8 , 2.1 , 0.  ],\n",
              "        [1.35, 0.  , 0.  , 1.95, 1.65, 0.  , 1.8 , 0.  ],\n",
              "        [2.1 , 1.95, 0.  , 0.  , 1.8 , 1.8 , 0.  , 0.  ],\n",
              "        [0.  , 2.85, 1.35, 1.65, 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 2.25, 0.  , 0.  , 0.  , 1.8 , 2.1 , 0.  ],\n",
              "        [2.1 , 0.  , 2.1 , 0.  , 0.  , 1.5 , 1.65, 1.5 ],\n",
              "        [0.  , 2.1 , 0.  , 0.  , 0.  , 0.  , 0.  , 2.4 ],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 2.1 , 0.  , 2.4 ],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 1.8 , 1.95, 0.  ],\n",
              "        [1.65, 0.  , 0.  , 1.8 , 3.  , 0.  , 0.75, 0.  ],\n",
              "        [1.8 , 2.25, 0.  , 0.  , 3.  , 1.8 , 0.  , 0.  ],\n",
              "        [0.  , 2.25, 1.05, 1.65, 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 2.4 , 0.  , 0.  , 0.  , 2.55, 1.95, 0.  ],\n",
              "        [2.1 , 0.  , 1.8 , 0.  , 0.  , 2.4 , 2.1 , 1.95],\n",
              "        [0.  , 0.75, 0.  , 0.  , 0.  , 0.  , 0.  , 2.25],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 2.1 , 0.  , 3.  ],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 2.1 , 2.7 , 0.  ],\n",
              "        [1.2 , 0.  , 0.  , 1.95, 1.8 , 0.  , 2.25, 0.  ],\n",
              "        [1.65, 2.1 , 0.  , 0.  , 3.  , 1.8 , 0.  , 0.  ],\n",
              "        [0.  , 3.  , 1.5 , 2.4 , 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 3.  , 0.  , 0.  , 0.  , 2.1 , 2.1 , 0.  ],\n",
              "        [2.1 , 0.  , 2.7 , 0.  , 0.  , 2.4 , 2.4 , 2.1 ],\n",
              "        [0.  , 2.85, 0.  , 0.  , 0.  , 0.  , 0.  , 2.55],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 1.8 , 0.  , 2.25],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 1.65, 1.8 , 0.  ],\n",
              "        [1.2 , 0.  , 0.  , 2.4 , 1.8 , 0.  , 1.5 , 0.  ],\n",
              "        [3.45, 2.1 , 0.  , 0.  , 2.55, 1.5 , 0.  , 0.  ],\n",
              "        [0.  , 1.8 , 2.7 , 2.85, 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 2.25, 0.  , 0.  , 0.  , 2.1 , 1.8 , 0.  ],\n",
              "        [2.25, 0.  , 1.8 , 0.  , 0.  , 2.25, 2.7 , 2.25],\n",
              "        [0.  , 2.4 , 0.  , 0.  , 0.  , 0.  , 0.  , 1.65],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 1.35, 0.  , 1.2 ],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 1.5 , 1.65, 0.  ],\n",
              "        [2.55, 0.  , 0.  , 2.4 , 2.1 , 0.  , 2.85, 0.  ],\n",
              "        [2.85, 2.7 , 0.  , 0.  , 2.7 , 1.95, 0.  , 0.  ],\n",
              "        [0.  , 3.  , 1.35, 2.1 , 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 1.5 , 0.  , 0.  , 0.  , 2.25, 2.25, 0.  ],\n",
              "        [1.8 , 0.  , 1.5 , 0.  , 0.  , 1.35, 2.85, 2.4 ],\n",
              "        [0.  , 1.8 , 0.  , 0.  , 0.  , 0.  , 0.  , 1.5 ],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 2.55, 0.  , 3.  ],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 2.1 , 2.4 , 0.  ],\n",
              "        [2.55, 0.  , 0.  , 1.95, 2.7 , 0.  , 2.4 , 0.  ],\n",
              "        [2.4 , 2.55, 0.  , 0.  , 0.15, 1.65, 0.  , 0.  ],\n",
              "        [0.  , 1.8 , 1.5 , 1.5 , 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 1.5 , 0.  , 0.  , 0.  , 2.25, 1.95, 0.  ],\n",
              "        [1.8 , 0.  , 2.25, 0.  , 0.  , 1.5 , 2.55, 1.5 ],\n",
              "        [0.  , 1.65, 0.  , 0.  , 0.  , 0.  , 0.  , 1.65],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 1.95, 0.  , 2.25],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 1.65, 2.25, 0.  ],\n",
              "        [1.8 , 0.  , 0.  , 2.55, 2.7 , 0.  , 3.  , 0.  ],\n",
              "        [3.  , 2.4 , 0.  , 0.  , 2.25, 2.55, 0.  , 0.  ],\n",
              "        [0.  , 3.  , 2.4 , 2.1 , 0.  , 0.  , 0.  , 0.  ]]),\n",
              " array([[0.  , 1.5 , 0.  , 0.  , 0.  , 3.  , 1.35, 0.  ],\n",
              "        [2.4 , 0.  , 1.8 , 0.  , 0.  , 1.5 , 1.5 , 1.65],\n",
              "        [0.  , 2.1 , 0.  , 0.  , 0.  , 0.  , 0.  , 1.65],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 1.95, 0.  , 1.35],\n",
              "        [0.  , 0.  , 0.  , 0.  , 0.  , 1.65, 3.  , 0.  ],\n",
              "        [1.8 , 0.  , 0.  , 1.5 , 2.1 , 0.  , 2.4 , 0.  ],\n",
              "        [2.4 , 3.  , 0.  , 0.  , 1.95, 1.5 , 0.  , 0.  ],\n",
              "        [0.  , 1.8 , 1.5 , 2.4 , 0.  , 0.  , 0.  , 0.  ]])]"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "car_travel_time = [compute_travel_time(density, car[i], \"car\")[1] for i in range(10)]\n",
        "car_travel_time = np.nan_to_num(car_travel_time  , nan=0.0, posinf=0.0, neginf=0.0)\n",
        "car_travel_time;"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nlnMf7qCGYxL",
        "outputId": "d4a592cc-cbed-49a1-99ff-842d6d46bf94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-70-c492ad516f4e>:27: RuntimeWarning: divide by zero encountered in true_divide\n",
            "  travel_time = D_l / (flow * (1 - density) + traversable_distance)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Flow Speed"
      ],
      "metadata": {
        "id": "cfnBa2tH5XIK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "#motorcycle': 1, 'car': 2, 'bus': 3, 'truck': 4}\n",
        "# Given data\n",
        "Dl = np.array([1,1,2,2])\n",
        "#motorcycle 50 km/hr, car- 60 km/hr, bus 25km/hr, truck 40 km/hr\n",
        "Vuf = np.array([50,60,25,40])\n",
        "Dutr = np.array([motorcycle_traversable_distance, car_traversable_distance, bus_traversable_distance, truck_traversable_distance ])\n",
        "\n",
        "def compute_flow_speed(vehicle_class_i, Vuf, Dutr, Dl):\n",
        "    D = Dl[vehicle_class_i]\n",
        "    V = Vuf[vehicle_class_i]\n",
        "    Dutr= Dutr[vehicle_class_i]\n",
        "    Vu = (V * Dutr) / D\n",
        "    return Vu\n",
        "\n",
        "V_motorcycle = compute_flow_speed(0, Vuf, Dutr, Dl)\n",
        "V_car = compute_flow_speed(1, Vuf, Dutr, Dl)\n",
        "V_bus = compute_flow_speed(2, Vuf, Dutr, Dl)\n",
        "V_truck = compute_flow_speed(3, Vuf, Dutr, Dl)\n",
        "\n",
        "\"\"\"for vehicle_class_i in range(len(Dl)):\n",
        "  vehicle_class= ['motorcycle','car', 'bus', 'truck'][vehicle_class_i]\n",
        "  Vu = compute_flow_speed(vehicle_class_i, Vuf, Dutr, Dl)\n",
        "  print(f\"Flow Speed for {vehicle_class}:\")\n",
        "  print(Vu)\"\"\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "KWPIObYRI6Tr",
        "outputId": "0f510c6e-02ee-425e-9fee-56adff42a42c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'for vehicle_class_i in range(len(Dl)):\\n  vehicle_class= [\\'motorcycle\\',\\'car\\', \\'bus\\', \\'truck\\'][vehicle_class_i]\\n  Vu = compute_flow_speed(vehicle_class_i, Vuf, Dutr, Dl)\\n  print(f\"Flow Speed for {vehicle_class}:\")\\n  print(Vu)'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Car"
      ],
      "metadata": {
        "id": "Km8C4m2l57WK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "V_car"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "croO48xp5U7X",
        "outputId": "a8582bde-d7d7-4583-ba75-0e1b7d58cb04"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[  0.,  72.,   0.,   0.,   0., 153., 126.,   0.],\n",
              "        [ 81.,   0.,  81.,   0.,   0., 126., 144.,  99.],\n",
              "        [  0.,  90.,   0.,   0.,   0.,   0.,   0., 108.],\n",
              "        [  0.,   0.,   0.,   0.,   0.,  99.,   0., 108.],\n",
              "        [  0.,   0.,   0.,   0.,   0., 108., 135.,   0.],\n",
              "        [126.,   0.,   0., 135., 153.,   0., 126.,   0.],\n",
              "        [153., 117.,   0.,   0., 108.,  90.,   0.,   0.],\n",
              "        [  0., 153., 126.,  90.,   0.,   0.,   0.,   0.]],\n",
              "\n",
              "       [[  0., 153.,   0.,   0.,   0., 108., 126.,   0.],\n",
              "        [ 90.,   0.,  99.,   0.,   0.,  99., 135., 144.],\n",
              "        [  0., 126.,   0.,   0.,   0.,   0.,   0., 126.],\n",
              "        [  0.,   0.,   0.,   0.,   0., 117.,   0., 126.],\n",
              "        [  0.,   0.,   0.,   0.,   0., 126., 117.,   0.],\n",
              "        [135.,   0.,   0., 126., 162.,   0.,  90.,   0.],\n",
              "        [162., 117.,   0.,   0., 180.,  99.,   0.,   0.],\n",
              "        [  0., 144., 108.,  99.,   0.,   0.,   0.,   0.]],\n",
              "\n",
              "       [[  0.,  72.,   0.,   0.,   0.,  99., 162.,   0.],\n",
              "        [ 54.,   0., 162.,   0.,   0., 108., 153., 180.],\n",
              "        [  0.,  90.,   0.,   0.,   0.,   0.,   0., 153.],\n",
              "        [  0.,   0.,   0.,   0.,   0., 135.,   0., 135.],\n",
              "        [  0.,   0.,   0.,   0.,   0., 108., 126.,   0.],\n",
              "        [ 81.,   0.,   0., 117.,  99.,   0., 108.,   0.],\n",
              "        [126., 117.,   0.,   0., 108., 108.,   0.,   0.],\n",
              "        [  0., 171.,  81.,  99.,   0.,   0.,   0.,   0.]],\n",
              "\n",
              "       [[  0., 135.,   0.,   0.,   0., 108., 126.,   0.],\n",
              "        [126.,   0., 126.,   0.,   0.,  90.,  99.,  90.],\n",
              "        [  0., 126.,   0.,   0.,   0.,   0.,   0., 144.],\n",
              "        [  0.,   0.,   0.,   0.,   0., 126.,   0., 144.],\n",
              "        [  0.,   0.,   0.,   0.,   0., 108., 117.,   0.],\n",
              "        [ 99.,   0.,   0., 108., 180.,   0.,  45.,   0.],\n",
              "        [108., 135.,   0.,   0., 180., 108.,   0.,   0.],\n",
              "        [  0., 135.,  63.,  99.,   0.,   0.,   0.,   0.]],\n",
              "\n",
              "       [[  0., 144.,   0.,   0.,   0., 153., 117.,   0.],\n",
              "        [126.,   0., 108.,   0.,   0., 144., 126., 117.],\n",
              "        [  0.,  45.,   0.,   0.,   0.,   0.,   0., 135.],\n",
              "        [  0.,   0.,   0.,   0.,   0., 126.,   0., 180.],\n",
              "        [  0.,   0.,   0.,   0.,   0., 126., 162.,   0.],\n",
              "        [ 72.,   0.,   0., 117., 108.,   0., 135.,   0.],\n",
              "        [ 99., 126.,   0.,   0., 180., 108.,   0.,   0.],\n",
              "        [  0., 180.,  90., 144.,   0.,   0.,   0.,   0.]],\n",
              "\n",
              "       [[  0., 180.,   0.,   0.,   0., 126., 126.,   0.],\n",
              "        [126.,   0., 162.,   0.,   0., 144., 144., 126.],\n",
              "        [  0., 171.,   0.,   0.,   0.,   0.,   0., 153.],\n",
              "        [  0.,   0.,   0.,   0.,   0., 108.,   0., 135.],\n",
              "        [  0.,   0.,   0.,   0.,   0.,  99., 108.,   0.],\n",
              "        [ 72.,   0.,   0., 144., 108.,   0.,  90.,   0.],\n",
              "        [207., 126.,   0.,   0., 153.,  90.,   0.,   0.],\n",
              "        [  0., 108., 162., 171.,   0.,   0.,   0.,   0.]],\n",
              "\n",
              "       [[  0., 135.,   0.,   0.,   0., 126., 108.,   0.],\n",
              "        [135.,   0., 108.,   0.,   0., 135., 162., 135.],\n",
              "        [  0., 144.,   0.,   0.,   0.,   0.,   0.,  99.],\n",
              "        [  0.,   0.,   0.,   0.,   0.,  81.,   0.,  72.],\n",
              "        [  0.,   0.,   0.,   0.,   0.,  90.,  99.,   0.],\n",
              "        [153.,   0.,   0., 144., 126.,   0., 171.,   0.],\n",
              "        [171., 162.,   0.,   0., 162., 117.,   0.,   0.],\n",
              "        [  0., 180.,  81., 126.,   0.,   0.,   0.,   0.]],\n",
              "\n",
              "       [[  0.,  90.,   0.,   0.,   0., 135., 135.,   0.],\n",
              "        [108.,   0.,  90.,   0.,   0.,  81., 171., 144.],\n",
              "        [  0., 108.,   0.,   0.,   0.,   0.,   0.,  90.],\n",
              "        [  0.,   0.,   0.,   0.,   0., 153.,   0., 180.],\n",
              "        [  0.,   0.,   0.,   0.,   0., 126., 144.,   0.],\n",
              "        [153.,   0.,   0., 117., 162.,   0., 144.,   0.],\n",
              "        [144., 153.,   0.,   0.,   9.,  99.,   0.,   0.],\n",
              "        [  0., 108.,  90.,  90.,   0.,   0.,   0.,   0.]],\n",
              "\n",
              "       [[  0.,  90.,   0.,   0.,   0., 135., 117.,   0.],\n",
              "        [108.,   0., 135.,   0.,   0.,  90., 153.,  90.],\n",
              "        [  0.,  99.,   0.,   0.,   0.,   0.,   0.,  99.],\n",
              "        [  0.,   0.,   0.,   0.,   0., 117.,   0., 135.],\n",
              "        [  0.,   0.,   0.,   0.,   0.,  99., 135.,   0.],\n",
              "        [108.,   0.,   0., 153., 162.,   0., 180.,   0.],\n",
              "        [180., 144.,   0.,   0., 135., 153.,   0.,   0.],\n",
              "        [  0., 180., 144., 126.,   0.,   0.,   0.,   0.]],\n",
              "\n",
              "       [[  0.,  90.,   0.,   0.,   0., 180.,  81.,   0.],\n",
              "        [144.,   0., 108.,   0.,   0.,  90.,  90.,  99.],\n",
              "        [  0., 126.,   0.,   0.,   0.,   0.,   0.,  99.],\n",
              "        [  0.,   0.,   0.,   0.,   0., 117.,   0.,  81.],\n",
              "        [  0.,   0.,   0.,   0.,   0.,  99., 180.,   0.],\n",
              "        [108.,   0.,   0.,  90., 126.,   0., 144.,   0.],\n",
              "        [144., 180.,   0.,   0., 117.,  90.,   0.,   0.],\n",
              "        [  0., 108.,  90., 144.,   0.,   0.,   0.,   0.]]])"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Bus"
      ],
      "metadata": {
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              "        [0.   , 1.5  , 1.25 , 1.75 , 0.   , 0.   , 0.   , 0.   ]]])"
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          "metadata": {},
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              "        [0. , 0. , 0. , 0. , 0. , 2.2, 2.4, 0. ],\n",
              "        [1.6, 0. , 0. , 3. , 2.6, 0. , 2. , 0. ],\n",
              "        [4.6, 2.8, 0. , 0. , 3.4, 2. , 0. , 0. ],\n",
              "        [0. , 2.4, 3.2, 3.8, 0. , 0. , 0. , 0. ]],\n",
              "\n",
              "       [[0. , 3. , 0. , 0. , 0. , 2.8, 2.6, 0. ],\n",
              "        [4.4, 0. , 2.4, 0. , 0. , 3. , 3.6, 3. ],\n",
              "        [0. , 2.8, 0. , 0. , 0. , 0. , 0. , 2. ],\n",
              "        [0. , 0. , 0. , 0. , 0. , 1.8, 0. , 1.6],\n",
              "        [0. , 0. , 0. , 0. , 0. , 2. , 2.8, 0. ],\n",
              "        [3.4, 0. , 0. , 3. , 2.8, 0. , 3.8, 0. ],\n",
              "        [3.8, 3.6, 0. , 0. , 3.6, 2.6, 0. , 0. ],\n",
              "        [0. , 4. , 1.8, 3. , 0. , 0. , 0. , 0. ]],\n",
              "\n",
              "       [[0. , 2. , 0. , 0. , 0. , 3. , 3. , 0. ],\n",
              "        [2.4, 0. , 2.2, 0. , 0. , 1.8, 3.8, 3.6],\n",
              "        [0. , 2.8, 0. , 0. , 0. , 0. , 0. , 2. ],\n",
              "        [0. , 0. , 0. , 0. , 0. , 3.4, 0. , 4. ],\n",
              "        [0. , 0. , 0. , 0. , 0. , 2.8, 3.2, 0. ],\n",
              "        [3.4, 0. , 0. , 2.6, 3.8, 0. , 3.4, 0. ],\n",
              "        [3.2, 3.4, 0. , 0. , 0.2, 2.4, 0. , 0. ],\n",
              "        [0. , 2.4, 2. , 2. , 0. , 0. , 0. , 0. ]],\n",
              "\n",
              "       [[0. , 2. , 0. , 0. , 0. , 3. , 2.6, 0. ],\n",
              "        [2.8, 0. , 2.8, 0. , 0. , 2. , 3.4, 2. ],\n",
              "        [0. , 2.2, 0. , 0. , 0. , 0. , 0. , 2.2],\n",
              "        [0. , 0. , 0. , 0. , 0. , 2.6, 0. , 3. ],\n",
              "        [0. , 0. , 0. , 0. , 0. , 2.2, 3. , 0. ],\n",
              "        [2.4, 0. , 0. , 3.6, 3.6, 0. , 4. , 0. ],\n",
              "        [3.8, 3. , 0. , 0. , 3. , 3.4, 0. , 0. ],\n",
              "        [0. , 4. , 3.2, 2.8, 0. , 0. , 0. , 0. ]],\n",
              "\n",
              "       [[0. , 2. , 0. , 0. , 0. , 4. , 1.8, 0. ],\n",
              "        [3.4, 0. , 2.4, 0. , 0. , 2. , 2. , 2.2],\n",
              "        [0. , 2.8, 0. , 0. , 0. , 0. , 0. , 3. ],\n",
              "        [0. , 0. , 0. , 0. , 0. , 2.6, 0. , 1.8],\n",
              "        [0. , 0. , 0. , 0. , 0. , 2.2, 4. , 0. ],\n",
              "        [2.4, 0. , 0. , 2. , 2.8, 0. , 2.8, 0. ],\n",
              "        [3.2, 2.6, 0. , 0. , 2.6, 2.4, 0. , 0. ],\n",
              "        [0. , 2.4, 2. , 2.8, 0. , 0. , 0. , 0. ]]])"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Density"
      ],
      "metadata": {
        "id": "OxeBlt5mVfn5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Motorcycle"
      ],
      "metadata": {
        "id": "OR5VzYjJV6VW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "V_motorcycle; #Speed\n",
        "#k_motorcycle = np.array(motorcycle_res_k).reshape(10,8,8); # Density\n",
        "#q_motorcycle = np.array(motorcycle_res_q).reshape(10,8,8); # Flow\n",
        "#for i in range(10):\n",
        "k_motorcycle = motorcycle/V_motorcycle\n",
        "k_motorcycle= np.nan_to_num(k_motorcycle, nan=0.0, posinf=0.0, neginf=0.0)\n",
        "k_motorcycle"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dz2_GvMZVo21",
        "outputId": "a908e27d-e532-4884-9f5c-f56af7a6f030"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-94-00e898cb9cdf>:5: RuntimeWarning: invalid value encountered in true_divide\n",
            "  k_motorcycle = motorcycle/V_motorcycle\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[0., 1., 0., 0., 0., 1., 1., 0.],\n",
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              "        [1., 1., 0., 0., 1., 1., 0., 0.],\n",
              "        [0., 1., 1., 1., 0., 0., 0., 0.]],\n",
              "\n",
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              "        [0., 1., 0., 0., 0., 0., 0., 1.],\n",
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              "        [1., 1., 0., 0., 1., 1., 0., 0.],\n",
              "        [0., 1., 1., 1., 0., 0., 0., 0.]],\n",
              "\n",
              "       [[0., 1., 0., 0., 0., 1., 1., 0.],\n",
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              "        [0., 1., 1., 1., 0., 0., 0., 0.]],\n",
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              "       [[0., 1., 0., 0., 0., 1., 1., 0.],\n",
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              "        [1., 1., 0., 0., 1., 1., 0., 0.],\n",
              "        [0., 1., 1., 1., 0., 0., 0., 0.]],\n",
              "\n",
              "       [[0., 1., 0., 0., 0., 1., 1., 0.],\n",
              "        [1., 0., 1., 0., 0., 1., 1., 1.],\n",
              "        [0., 1., 0., 0., 0., 1., 0., 1.],\n",
              "        [0., 0., 0., 0., 0., 1., 1., 1.],\n",
              "        [0., 0., 0., 0., 0., 1., 1., 0.],\n",
              "        [1., 1., 1., 1., 1., 0., 1., 0.],\n",
              "        [1., 1., 0., 0., 1., 1., 0., 0.],\n",
              "        [0., 1., 1., 1., 0., 0., 0., 0.]],\n",
              "\n",
              "       [[0., 1., 0., 0., 0., 1., 1., 0.],\n",
              "        [1., 0., 1., 0., 0., 1., 1., 1.],\n",
              "        [0., 1., 0., 0., 0., 1., 0., 1.],\n",
              "        [0., 0., 0., 0., 0., 1., 1., 1.],\n",
              "        [0., 0., 0., 0., 0., 1., 1., 0.],\n",
              "        [1., 1., 1., 1., 1., 0., 1., 0.],\n",
              "        [1., 1., 0., 0., 1., 1., 0., 0.],\n",
              "        [0., 1., 1., 1., 0., 0., 0., 0.]],\n",
              "\n",
              "       [[0., 1., 0., 0., 0., 1., 1., 0.],\n",
              "        [1., 0., 1., 0., 0., 1., 1., 1.],\n",
              "        [0., 1., 0., 0., 0., 1., 0., 1.],\n",
              "        [0., 0., 0., 0., 0., 1., 1., 1.],\n",
              "        [0., 0., 0., 0., 0., 1., 1., 0.],\n",
              "        [1., 1., 1., 1., 1., 0., 1., 0.],\n",
              "        [1., 1., 0., 0., 1., 1., 0., 0.],\n",
              "        [0., 1., 1., 1., 0., 0., 0., 0.]]])"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Car"
      ],
      "metadata": {
        "id": "pBDuSmTgV-Gy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "V_car; #Speed\n",
        "#k_car = np.array(car_res_k).reshape(10,8,8); # Density\n",
        "#q_car = np.array(car_res_q).reshape(10,8,8); # Flow\n",
        "k_car= car/V_car\n",
        "k_car= np.nan_to_num(k_car, nan=0.0, posinf=0.0, neginf=0.0)\n",
        "k_car"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0pdkWpGGVtiV",
        "outputId": "9565beb9-bf78-4bb2-83d8-0d53e1cd5e5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-95-30e344e9499f>:4: RuntimeWarning: invalid value encountered in true_divide\n",
            "  k_car= car/V_car\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "        [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
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              "        [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
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              "        [0.11111111, 0.        , 0.        , 0.11111111, 0.11111111,\n",
              "         0.        , 0.11111111, 0.        ],\n",
              "        [0.11111111, 0.11111111, 0.        , 0.        , 0.11111111,\n",
              "         0.11111111, 0.        , 0.        ],\n",
              "        [0.        , 0.11111111, 0.11111111, 0.11111111, 0.        ,\n",
              "         0.        , 0.        , 0.        ]]])"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Truck"
      ],
      "metadata": {
        "id": "YcIC_TShWD9-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "V_truck; #Speed\n",
        "#k_truck = np.array(truck_res_k).reshape(10,8,8); # Density\n",
        "#q_truck = np.array(truck_res_q).reshape(10,8,8); # Flow\n",
        "k_truck= truck/V_truck\n",
        "k_truck= np.nan_to_num(k_truck, nan=0.0, posinf=0.0, neginf=0.0)\n",
        "k_truck"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tu3XdLHqVxiF",
        "outputId": "abcb5855-45bb-4791-a965-bdc901f0cbb4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-96-67df7c87cbdc>:4: RuntimeWarning: invalid value encountered in true_divide\n",
            "  k_truck= truck/V_truck\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[0., 5., 0., 0., 0., 5., 5., 0.],\n",
              "        [5., 0., 5., 0., 0., 5., 5., 5.],\n",
              "        [0., 5., 0., 0., 0., 0., 0., 5.],\n",
              "        [0., 0., 0., 0., 0., 5., 0., 5.],\n",
              "        [0., 0., 0., 0., 0., 5., 5., 0.],\n",
              "        [5., 0., 0., 5., 5., 0., 5., 0.],\n",
              "        [5., 5., 0., 0., 5., 5., 0., 0.],\n",
              "        [0., 5., 5., 5., 0., 0., 0., 0.]],\n",
              "\n",
              "       [[0., 5., 0., 0., 0., 5., 5., 0.],\n",
              "        [5., 0., 5., 0., 0., 5., 5., 5.],\n",
              "        [0., 5., 0., 0., 0., 0., 0., 5.],\n",
              "        [0., 0., 0., 0., 0., 5., 0., 5.],\n",
              "        [0., 0., 0., 0., 0., 5., 5., 0.],\n",
              "        [5., 0., 0., 5., 5., 0., 5., 0.],\n",
              "        [5., 5., 0., 0., 5., 5., 0., 0.],\n",
              "        [0., 5., 5., 5., 0., 0., 0., 0.]],\n",
              "\n",
              "       [[0., 5., 0., 0., 0., 5., 5., 0.],\n",
              "        [5., 0., 5., 0., 0., 5., 5., 5.],\n",
              "        [0., 5., 0., 0., 0., 0., 0., 5.],\n",
              "        [0., 0., 0., 0., 0., 5., 0., 5.],\n",
              "        [0., 0., 0., 0., 0., 5., 5., 0.],\n",
              "        [5., 0., 0., 5., 5., 0., 5., 0.],\n",
              "        [5., 5., 0., 0., 5., 5., 0., 0.],\n",
              "        [0., 5., 5., 5., 0., 0., 0., 0.]],\n",
              "\n",
              "       [[0., 5., 0., 0., 0., 5., 5., 0.],\n",
              "        [5., 0., 5., 0., 0., 5., 5., 5.],\n",
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              "        [0., 0., 0., 0., 0., 5., 5., 0.],\n",
              "        [5., 0., 0., 5., 5., 0., 5., 0.],\n",
              "        [5., 5., 0., 0., 5., 5., 0., 0.],\n",
              "        [0., 5., 5., 5., 0., 0., 0., 0.]],\n",
              "\n",
              "       [[0., 5., 0., 0., 0., 5., 5., 0.],\n",
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              "        [5., 5., 0., 0., 5., 5., 0., 0.],\n",
              "        [0., 5., 5., 5., 0., 0., 0., 0.]],\n",
              "\n",
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              "        [5., 0., 0., 5., 5., 0., 5., 0.],\n",
              "        [5., 5., 0., 0., 5., 5., 0., 0.],\n",
              "        [0., 5., 5., 5., 0., 0., 0., 0.]],\n",
              "\n",
              "       [[0., 5., 0., 0., 0., 5., 5., 0.],\n",
              "        [5., 0., 5., 0., 0., 5., 5., 5.],\n",
              "        [0., 5., 0., 0., 0., 0., 0., 5.],\n",
              "        [0., 0., 0., 0., 0., 5., 0., 5.],\n",
              "        [0., 0., 0., 0., 0., 5., 5., 0.],\n",
              "        [5., 0., 0., 5., 5., 0., 5., 0.],\n",
              "        [5., 5., 0., 0., 5., 5., 0., 0.],\n",
              "        [0., 5., 5., 5., 0., 0., 0., 0.]],\n",
              "\n",
              "       [[0., 5., 0., 0., 0., 5., 5., 0.],\n",
              "        [5., 0., 5., 0., 0., 5., 5., 5.],\n",
              "        [0., 5., 0., 0., 0., 0., 0., 5.],\n",
              "        [0., 0., 0., 0., 0., 5., 0., 5.],\n",
              "        [0., 0., 0., 0., 0., 5., 5., 0.],\n",
              "        [5., 0., 0., 5., 5., 0., 5., 0.],\n",
              "        [5., 5., 0., 0., 5., 5., 0., 0.],\n",
              "        [0., 5., 5., 5., 0., 0., 0., 0.]],\n",
              "\n",
              "       [[0., 5., 0., 0., 0., 5., 5., 0.],\n",
              "        [5., 0., 5., 0., 0., 5., 5., 5.],\n",
              "        [0., 5., 0., 0., 0., 0., 0., 5.],\n",
              "        [0., 0., 0., 0., 0., 5., 0., 5.],\n",
              "        [0., 0., 0., 0., 0., 5., 5., 0.],\n",
              "        [5., 0., 0., 5., 5., 0., 5., 0.],\n",
              "        [5., 5., 0., 0., 5., 5., 0., 0.],\n",
              "        [0., 5., 5., 5., 0., 0., 0., 0.]]])"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Bus"
      ],
      "metadata": {
        "id": "Hix2ibUiWFtl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#q_bus/V_bus\n",
        "k_bus= bus/V_bus\n",
        "k_bus= np.nan_to_num(k_bus, nan=0.0, posinf=0.0, neginf=0.0)\n",
        "k_bus"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OLxnjCiJV2J5",
        "outputId": "23ffdcc9-ad9c-44fe-8290-f315b25cffb5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-97-76d624bc61a4>:2: RuntimeWarning: invalid value encountered in true_divide\n",
            "  k_bus= bus/V_bus\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[0., 8., 0., 0., 0., 8., 8., 0.],\n",
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              "        [0., 8., 8., 8., 0., 0., 0., 0.]],\n",
              "\n",
              "       [[0., 8., 0., 0., 0., 8., 8., 0.],\n",
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              "        [8., 8., 0., 0., 8., 8., 0., 0.],\n",
              "        [0., 8., 8., 8., 0., 0., 0., 0.]],\n",
              "\n",
              "       [[0., 8., 0., 0., 0., 8., 8., 0.],\n",
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              "        [0., 8., 0., 0., 0., 0., 0., 8.],\n",
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              "        [0., 8., 8., 8., 0., 0., 0., 0.]],\n",
              "\n",
              "       [[0., 8., 0., 0., 0., 8., 8., 0.],\n",
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              "        [0., 8., 0., 0., 0., 0., 0., 8.],\n",
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              "        [8., 0., 0., 8., 8., 0., 8., 0.],\n",
              "        [8., 8., 0., 0., 8., 8., 0., 0.],\n",
              "        [0., 8., 8., 8., 0., 0., 0., 0.]],\n",
              "\n",
              "       [[0., 8., 0., 0., 0., 8., 8., 0.],\n",
              "        [8., 0., 8., 0., 0., 8., 8., 8.],\n",
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              "        [8., 0., 0., 8., 8., 0., 8., 0.],\n",
              "        [8., 8., 0., 0., 8., 8., 0., 0.],\n",
              "        [0., 8., 8., 8., 0., 0., 0., 0.]],\n",
              "\n",
              "       [[0., 8., 0., 0., 0., 8., 8., 0.],\n",
              "        [8., 0., 8., 0., 0., 8., 8., 8.],\n",
              "        [0., 8., 0., 0., 0., 0., 0., 8.],\n",
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              "        [0., 0., 0., 0., 0., 8., 8., 0.],\n",
              "        [8., 0., 0., 8., 8., 0., 8., 0.],\n",
              "        [8., 8., 0., 0., 8., 8., 0., 0.],\n",
              "        [0., 8., 8., 8., 0., 0., 0., 0.]],\n",
              "\n",
              "       [[0., 8., 0., 0., 0., 8., 8., 0.],\n",
              "        [8., 0., 8., 0., 0., 8., 8., 8.],\n",
              "        [0., 8., 0., 0., 0., 0., 0., 8.],\n",
              "        [0., 0., 0., 0., 0., 8., 0., 8.],\n",
              "        [0., 0., 0., 0., 0., 8., 8., 0.],\n",
              "        [8., 0., 0., 8., 8., 0., 8., 0.],\n",
              "        [8., 8., 0., 0., 8., 8., 0., 0.],\n",
              "        [0., 8., 8., 8., 0., 0., 0., 0.]],\n",
              "\n",
              "       [[0., 8., 0., 0., 0., 8., 8., 0.],\n",
              "        [8., 0., 8., 0., 0., 8., 8., 8.],\n",
              "        [0., 8., 0., 0., 0., 0., 0., 8.],\n",
              "        [0., 0., 0., 0., 0., 8., 0., 8.],\n",
              "        [0., 0., 0., 0., 0., 8., 8., 0.],\n",
              "        [8., 0., 0., 8., 8., 0., 8., 0.],\n",
              "        [8., 8., 0., 0., 8., 8., 0., 0.],\n",
              "        [0., 8., 8., 8., 0., 0., 0., 0.]],\n",
              "\n",
              "       [[0., 8., 0., 0., 0., 8., 8., 0.],\n",
              "        [8., 0., 8., 0., 0., 8., 8., 8.],\n",
              "        [0., 8., 0., 0., 0., 0., 0., 8.],\n",
              "        [0., 0., 0., 0., 0., 8., 0., 8.],\n",
              "        [0., 0., 0., 0., 0., 8., 8., 0.],\n",
              "        [8., 0., 0., 8., 8., 0., 8., 0.],\n",
              "        [8., 8., 0., 0., 8., 8., 0., 0.],\n",
              "        [0., 8., 8., 8., 0., 0., 0., 0.]],\n",
              "\n",
              "       [[0., 8., 0., 0., 0., 8., 8., 0.],\n",
              "        [8., 0., 8., 0., 0., 8., 8., 8.],\n",
              "        [0., 8., 0., 0., 0., 0., 0., 8.],\n",
              "        [0., 0., 0., 0., 0., 8., 0., 8.],\n",
              "        [0., 0., 0., 0., 0., 8., 8., 0.],\n",
              "        [8., 0., 0., 8., 8., 0., 8., 0.],\n",
              "        [8., 8., 0., 0., 8., 8., 0., 0.],\n",
              "        [0., 8., 8., 8., 0., 0., 0., 0.]]])"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Time Travel"
      ],
      "metadata": {
        "id": "n5OX0Dyxd0kf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## $Bus$"
      ],
      "metadata": {
        "id": "UfSYqXNufX4d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "bus_time_travel = [compute_travel_time(k_bus[i], bus[i], \"bus\")[1] for i in range(10)]\n",
        "bus_time_travel = np.nan_to_num(bus_time_travel , nan=0.0, posinf=0.0, neginf=0.0)\n",
        "bus_time_travel"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JCj7qdMhA7WR",
        "outputId": "3fdc3068-9735-458e-c31a-fe179b2cb6ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-70-c492ad516f4e>:27: RuntimeWarning: divide by zero encountered in true_divide\n",
            "  travel_time = D_l / (flow * (1 - density) + traversable_distance)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "         -1.68648284e-05,  0.00000000e+00],\n",
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              "          0.00000000e+00,  0.00000000e+00]],\n",
              "\n",
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              "         -2.04876050e-05,  0.00000000e+00],\n",
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              "          0.00000000e+00,  0.00000000e+00, -1.43307538e-05,\n",
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              "          0.00000000e+00,  0.00000000e+00, -2.87108814e-05,\n",
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              "          0.00000000e+00,  0.00000000e+00, -2.20677480e-05,\n",
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              "        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n",
              "          0.00000000e+00,  0.00000000e+00, -2.60926288e-05,\n",
              "         -1.43307538e-05,  0.00000000e+00],\n",
              "        [-2.39120038e-05,  0.00000000e+00,  0.00000000e+00,\n",
              "         -2.87108814e-05, -2.04876050e-05,  0.00000000e+00,\n",
              "         -2.04876050e-05,  0.00000000e+00],\n",
              "        [-1.79211470e-05, -2.20677480e-05,  0.00000000e+00,\n",
              "          0.00000000e+00, -2.20677480e-05, -2.39120038e-05,\n",
              "          0.00000000e+00,  0.00000000e+00],\n",
              "        [ 0.00000000e+00, -2.39120038e-05, -2.87108814e-05,\n",
              "         -2.04876050e-05,  0.00000000e+00,  0.00000000e+00,\n",
              "          0.00000000e+00,  0.00000000e+00]]])"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bus_time_travel.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qm-9vlMZTEFh",
        "outputId": "ef8f9706-87d2-4a14-ace9-da2b024b9f62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10, 8, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## $Car$"
      ],
      "metadata": {
        "id": "uRmsvWPMfjA3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "car_time_travel = [compute_travel_time(k_car[i], car[i], \"car\")[1] for i in range(10)]\n",
        "car_time_travel = np.nan_to_num(car_time_travel , nan=0.0, posinf=0.0, neginf=0.0)\n",
        "car_time_travel"
      ],
      "metadata": {
        "id": "BnKaKCImDVSm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d6ab7ddc-5381-4c67-d5aa-b90379b3b93f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-70-c492ad516f4e>:27: RuntimeWarning: divide by zero encountered in true_divide\n",
            "  travel_time = D_l / (flow * (1 - density) + traversable_distance)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "\n",
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              "         0.00000000e+00, 8.74040983e-05, 4.80974776e-05, 0.00000000e+00],\n",
              "        [8.01282051e-05, 0.00000000e+00, 0.00000000e+00, 9.61333048e-05,\n",
              "         6.86918028e-05, 0.00000000e+00, 6.01122095e-05, 0.00000000e+00],\n",
              "        [6.01122095e-05, 4.80974776e-05, 0.00000000e+00, 0.00000000e+00,\n",
              "         7.39705761e-05, 9.61333048e-05, 0.00000000e+00, 0.00000000e+00],\n",
              "        [0.00000000e+00, 8.01282051e-05, 9.61333048e-05, 6.01122095e-05,\n",
              "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]]])"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## $Motorcycle$"
      ],
      "metadata": {
        "id": "vM-Wz5aCf85R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "motorcycle_time_travel = [compute_travel_time(k_motorcycle[i], motorcycle[i], \"motorcycle\")[1] for i in range(10)]\n",
        "motorcycle_time_travel = np.nan_to_num(motorcycle_time_travel , nan=0.0, posinf=0.0, neginf=0.0)\n",
        "\n",
        "motorcycle_time_travel\n"
      ],
      "metadata": {
        "id": "LyUTDoEvCZx-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c7271124-4633-4a3f-85c4-5492a408cddf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-70-c492ad516f4e>:27: RuntimeWarning: divide by zero encountered in true_divide\n",
            "  travel_time = D_l / (flow * (1 - density) + traversable_distance)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "         0.        , 0.00285714, 0.        ],\n",
              "        [0.0037037 , 0.00344828, 0.        , 0.        , 0.0025641 ,\n",
              "         0.0030303 , 0.        , 0.        ],\n",
              "        [0.        , 0.00769231, 0.0047619 , 0.0037037 , 0.        ,\n",
              "         0.        , 0.        , 0.        ]]])"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## $Truck$"
      ],
      "metadata": {
        "id": "KvinssnpgF4o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "truck_time_travel = [compute_travel_time(k_truck[i], bus[i], \"bus\")[1] for i in range(10)]\n",
        "truck_time_travel = np.nan_to_num(truck_time_travel , nan=0.0, posinf=0.0, neginf=0.0)\n",
        "truck_time_travel"
      ],
      "metadata": {
        "id": "0XLX0vbEC_KR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cf9d4fc3-d6a1-4969-89fe-6ff188760d7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-70-c492ad516f4e>:27: RuntimeWarning: divide by zero encountered in true_divide\n",
            "  travel_time = D_l / (flow * (1 - density) + traversable_distance)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "         -2.51098556e-05,  0.00000000e+00],\n",
              "        [-4.19023675e-05,  0.00000000e+00,  0.00000000e+00,\n",
              "         -5.03144654e-05, -3.59001975e-05,  0.00000000e+00,\n",
              "         -3.59001975e-05,  0.00000000e+00],\n",
              "        [-3.14021039e-05, -3.86697602e-05,  0.00000000e+00,\n",
              "          0.00000000e+00, -3.86697602e-05, -4.19023675e-05,\n",
              "          0.00000000e+00,  0.00000000e+00],\n",
              "        [ 0.00000000e+00, -4.19023675e-05, -5.03144654e-05,\n",
              "         -3.59001975e-05,  0.00000000e+00,  0.00000000e+00,\n",
              "          0.00000000e+00,  0.00000000e+00]]])"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.array(motorcycle_traversable_distance).shape"
      ],
      "metadata": {
        "id": "wurZofkAiuL8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9bbac62e-e26e-43b5-c755-de96cd36386c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10, 8, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "V_bus.shape"
      ],
      "metadata": {
        "id": "yDFGbilygVOA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "380c81d1-2f57-4e46-b76f-e07421811ffa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10, 8, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "V_motorcycle.shape"
      ],
      "metadata": {
        "id": "9OFqznrfa7Vb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "771ef28a-172a-45ca-9454-f9399ac60c84"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10, 8, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Shortest path"
      ],
      "metadata": {
        "id": "JT8un6KOql1h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import heapq\n",
        "\n",
        "def dijkstra(graph, source):\n",
        "    distances = {vertex: float(\"inf\") for vertex in graph}\n",
        "    distances[source] = 0\n",
        "    visited = set()\n",
        "    pq = []\n",
        "    heapq.heappush(pq, (0, source))\n",
        "    while pq:\n",
        "        _, current = heapq.heappop(pq)\n",
        "        if current in visited:\n",
        "            continue\n",
        "        visited.add(current)\n",
        "        for neighbor, weight in graph[current].items():\n",
        "            if distances[neighbor] > distances[current] + weight:\n",
        "                distances[neighbor] = distances[current] + weight\n",
        "                heapq.heappush(pq, (distances[neighbor], neighbor))\n",
        "    return distances\n",
        "\n",
        "def main():\n",
        "    graph = {\n",
        "        0: {1: 1, 2: 3},\n",
        "        1: {2: 2},\n",
        "        2: {0: 4}\n",
        "    }\n",
        "    print(dijkstra(graph, 0))\n"
      ],
      "metadata": {
        "id": "9InwUVGNqqCD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "main()"
      ],
      "metadata": {
        "id": "oCrlCbUyrqBj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "be61d8ec-4c8c-4b37-a952-397826aaec9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{0: 0, 1: 1, 2: 3}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Graphs"
      ],
      "metadata": {
        "id": "0Q3CYz2bXS-H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "k_motorcycle[9]"
      ],
      "metadata": {
        "id": "Z2ToCQbzbDR_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1da8a8e8-1092-434f-ef5b-8804909ce8c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 1., 0., 0., 0., 1., 1., 0.],\n",
              "       [1., 0., 1., 0., 0., 1., 1., 1.],\n",
              "       [0., 1., 0., 0., 0., 1., 0., 1.],\n",
              "       [0., 0., 0., 0., 0., 1., 1., 1.],\n",
              "       [0., 0., 0., 0., 0., 1., 1., 0.],\n",
              "       [1., 1., 1., 1., 1., 0., 1., 0.],\n",
              "       [1., 1., 0., 0., 1., 1., 0., 0.],\n",
              "       [0., 1., 1., 1., 0., 0., 0., 0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.figure()\n",
        "plt.imshow(k_motorcycle[9], cmap = \"hot\", aspect = \"auto\")\n",
        "plt.colorbar(label='Density (vehicles/km)')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "x9Fg-cvrsFRx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "outputId": "ebaf2845-df7c-4a6b-f99c-d085c8f397e2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "def show_images(images, title, cmap='viridis'):\n",
        "    fig, axes = plt.subplots(2, 5, figsize=(10, 5))\n",
        "    for i in range(10):\n",
        "        im = axes[i // 5, i % 5].imshow(images[i], cmap=cmap)\n",
        "        axes[i // 5, i % 5].set_title(str(i))\n",
        "\n",
        "    # Add a color bar to show the scale of the colormap\n",
        "    fig.colorbar(im, ax=axes.ravel().tolist(), shrink=0.6)\n",
        "\n",
        "    fig.suptitle(title)\n",
        "    plt.show()\n",
        "\n",
        "images = np.random.randint(0, 255, (10, 28, 28))\n",
        "show_images(images, \"Random\", cmap='viridis')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "mDCQ0G8ZfoQt",
        "outputId": "293b9640-44e0-4bb3-b68d-e6b56c24d7f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 11 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "show_images(V_truck, \"Flow Truck\")"
      ],
      "metadata": {
        "id": "3v8ELUhtuD4x",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "outputId": "a14e639d-bbf7-4ecf-dbb5-0f073b4464e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 11 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "show_images(V_car, \"Flow Car\")"
      ],
      "metadata": {
        "id": "QJGLbwoJPZyC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "outputId": "e1b71ee2-cecd-4db1-a4ae-95822614db4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 11 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "V_bus[1]"
      ],
      "metadata": {
        "id": "uK9gz5NGSjQ0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "77465a84-7b4d-4072-a35e-0893665d4952"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.   , 2.125, 0.   , 0.   , 0.   , 1.5  , 1.75 , 0.   ],\n",
              "       [1.25 , 0.   , 1.375, 0.   , 0.   , 1.375, 1.875, 2.25 ],\n",
              "       [0.   , 1.75 , 0.   , 0.   , 0.   , 0.   , 0.   , 1.875],\n",
              "       [0.   , 0.   , 0.   , 0.   , 0.   , 1.625, 0.   , 1.75 ],\n",
              "       [0.   , 0.   , 0.   , 0.   , 0.   , 1.375, 1.625, 0.   ],\n",
              "       [1.875, 0.   , 0.   , 1.625, 2.125, 0.   , 1.75 , 0.   ],\n",
              "       [2.25 , 1.625, 0.   , 0.   , 2.375, 1.375, 0.   , 0.   ],\n",
              "       [0.   , 1.5  , 1.625, 1.25 , 0.   , 0.   , 0.   , 0.   ]])"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "show_images(V_bus, \"Flow Bus\")"
      ],
      "metadata": {
        "id": "cgLRYTPWPdJ-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "outputId": "5a86a8e3-2682-4e47-ff5a-2f3c0a3fbc08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 11 Axes>"
            ],
            "image/png": 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2+6z42tnfVJ8c0MdW+zrVarXeDuSsqTidA3f8378djWf3fYeak7ndu2OJo7GOfveIrfbBmAOOjwE/y3Q0XtrcdY76hcr2P1xku889w99xNNarQ1JstQ+n/Me8nepovLqrnP2uhErRC71t90mKr3I0VuLYnbbah9t3wHUflToaz+68DzUn7+t/d4xwNFby+G2W2wYr/2hcSAvy+n+eilELxXis/yJGx8c6Gs/OGCYkJtgvrlvHRDsay/Zn8Z/HRTX1Pyk6nQOtE0L0vkPMydxu0bqlo7HCYQ44PgbExjkaL9zzH9XK/vtq1cbZYdvN+Xc65xXm+Y+Ot5//6NbOnuUXDvn/9uvZnQMhm/ch5uR9haQmClL+0ThHp1vnzZunjIwMxcXFKTMzU+vXr2/quBDGyH9kI/9gDkQ28g80PdsF+ZIlS5Sbm6u8vDxt2rRJAwYMUE5Ojg4cOBCM+BBmyH9kI/9gDkQ28g8Eh+2C/PHHH9fUqVM1efJk9e7dW/Pnz1d8fLwWLlwYjPgQZsh/ZCP/YA5ENvIPBIetgrympkYbN25Udnb2Ny8QFaXs7GwVFBSc1L66ulrl5eUNNriX3fxLzIHmhPyD74DIxjEACB5bBfmhQ4fk9XqVktLwquWUlBSVlJx8NfucOXOUlJQU2LjVkbvZzb/EHGhOyD/4DohsHAOA4Anq3d5nzpypsrKywFZUVBTM4RCGmAORjfxHNvIP5gBgja377SQnJys6OlqlpQ3vm1laWqrU1JPvExsbG6vYWGe350H4sZt/iTnQnJB/8B0Q2TgGAMFj6wx5y5YtNXjwYOXn5wf2+Xw+5efnKysrq8mDQ3gh/5GN/IM5ENnIPxA8tu9In5ubq0mTJmnIkCEaNmyYnnjiCR07dkyTJ08ORnwIM+Q/spF/MAciG/kHgsN2QT5+/HgdPHhQs2bNUklJiQYOHKhly5addJEHmifyH9nIP5gDkY38A8Hh8fv9zp7D60B5efmJq6wXzLL1yOgLJm5yNN7O39r/J7Ru95361k3BcO+OT2z3eXDHNY7G2lvS3lZ739dVKpo2W2VlZUpMTHQ05qkE5sBjD9qbA9PXORpv2wsX2e7jdL454SS+cf2cxXdL+9W22ldU+DSoz4EmnQP1+e8z9WFFt7Se/45Pr3E03swdH9vuM+f8/o7GcmLn4oG2+9w3aJmjsb7berut9hUVPvXvHZz8Z/z6IUXFWc+/0+Ny2dvdbfdJusre53Q2nHxHeRN8jsaKrrB3DwdfVZV25/0yaN8BP8y/US3btLTcr/jiCmcD5ne232fUPmdjOTC00Gu7z4bDXRyNdfBYa8ttvcertWX8o02efzQuqHdZAQAAAHB6FOQAAACAQRTkAAAAgEEU5AAAAIBBFOQAAACAQRTkAAAAgEEU5AAAAIBBFOQAAACAQRTkAAAAgEEU5AAAAIBBFOQAAACAQRTkAAAAgEEU5AAAAIBBMSYGPX/a/ynG08Jy+23zMh2Nc8H0Akf9QuX35/ez3SdWux2NFf+P1rbae6NqHI1j1fl3fWRrDrT7sL2jcS64ZJOjfqFywUT78RU6HGv+5hG22ldX1kp63eFop9fhf9fZyv/WBUMdjTPnfEfdQqbbDYW2+yxVqrPBPrPX/OvjdZIOOBvrDDLy1tvKf8b6Vo7G2T1su6N+odLtvtB9R9n9DGsqaxx+21hTMqrS1hw4fp2zOiB+1DpH/UJlw8BoB732ORprwNoEy21rKmu0xdEocIoz5AAAAIBBFOQAAACAQRTkAAAAgEEU5AAAAIBBFOQAAACAQRTkAAAAgEEU5AAAAIBBFOQAAACAQRTkAAAAgEEU5AAAAIBBFOQAAACAQRTkAAAAiHhz5szR0KFDlZCQoI4dO+raa6/VF198cdo+ixYtksfjabDFxcXZHjvGadChdMH0daZDCIqBm+33KRzU9HG4wZFLvjIdQlBUv5dhu0/sFbsdjbXneHtb7Wu/rnE0TjD0mLbBdAjBkd/Zfp9R+xwNtbrsAlvtayprJK11NFZT2z3sa9MhBEXZ291t90m6ansQIgl/8a82zzoglMeA589bZblteYVPLzkaxd0++OADTZ8+XUOHDlVdXZ1+8Ytf6IorrtCnn36q1q1bN9ovMTGxQeHu8Xhsj+2KghwAAAAIpmXLljX486JFi9SxY0dt3LhRl156aaP9PB6PUlNTz2psCnIAAAC4RlVVlWpqrP8rrt/vP+msdWxsrGJjY0/br6ysTJLUvv3p/4W5srJSXbp0kc/n00UXXaSHH35Yffr0sRyfREEOAAAAl6iqqlLXLm1UcsBruU+bNm1UWVnZYF9eXp4eeOCBRvv4fD7deeeduuSSS9S3b99G2/Xs2VMLFy5U//79VVZWpkcffVTDhw/Xv//9b3XubH1JEgU5AAAAXKGmpkYlB7zatbGLEhPOfG+S8gqfug7eo6KiIiUmJgb2n+ns+PTp07VlyxatXr36tO2ysrKUlZUV+PPw4cN14YUX6tlnn9WDDz54xvjqUZADAADAVVq3ObGdidd/4r+JiYkNCvLTmTFjht58802tXLnS1lluSWrRooUGDRqk7dvtXYDNbQ8BAADgKj75LW9W+f1+zZgxQ6+++qr+9a9/qWvXrrbj8nq9+uSTT9SpUydb/ThDDgAAAFfxySefxXZWTZ8+XYsXL9brr7+uhIQElZSUSJKSkpLUqlUrSdLEiRN17rnnas6cOZKk2bNn6+KLL1b37t119OhRPfLII9qzZ49uvvlmW++HghwAAACu4vX75fWf+ey3lTb1nnnmGUnS5Zdf3mD/888/r5tuukmStHfvXkVFfbPA5MiRI5o6dapKSkrUrl07DR48WGvWrFHv3r0tjytRkAMAAMBlrC5Hsbtk5UxWrFjR4M9z587V3LlzLY/RGApyAAAAuIpPfnmbuCA3iYIcAAAArlLr96nWQq1d67e+htwkCnIAAAC4iu8/m5V2bkBBbtBbu+w9VlWSntzxkqOxfn++vfZ1/lptdTQS7NhbcvrH8Z7Kee85G+vIJbttta/z1zobCJbtLE223adbvrOxii/eZ6s9+Q++AweSbPcp+3s/Z4MN+8RWc/Ifvmbu+NhRv5y0gZbbnsj/TkfjhIrX4pIVK23CAQU5AAAAXMXr/+ahP2dq5wYU5AAAAHAVlqwAAAAABvnkkVceS+3cgIIcAAAAruLzn9istHMDCnIAAAC4itfiGXIrbcIBBTkAAABchYIcAAAAMKjWH6Vaf5SFdiEIpglQkAMAAMBVvIqSV2cuyL0hiKUpUJADAADAVfx+j3z+My9H8VtoEw4oyAEAAOAqrCEHAAAADPL6o+S1sIacJ3UCAAAAQeCTRz4La8h9ckdFTkEOAAAAV2HJShNo+347tWjd0nL7I5d8FcRoGhq42X6fKxI/cTTW78+332fbp6mOxgo3/VZLsW2sty8cFLxY/lv1exm2+9x//j8djeVkDrT7sL2jsY446hUce381TFFxcZbbZ9xfEMRoGjr4Rk/bfY6UJDoaq8cNG2z32bl4oKOxummfo37BsOOxIYpqZT3/F0xfF8RoGtq2aLDtPiv+50lHY009z34fJ/GFo+KfZSo61vocOPd3a4IYTUNOjgEDOhY7Gqv4Yvu/l8sL+zgaq7mxvmSFM+QAAABAkzuxZOXMZ7+ttAkHFOQAAABwlVp/jGr80RbauaMgP/O5/m954IEH5PF4Gmy9evUKVmwIM+QfzIHIRv4jG/lHOPEpyvLmBrbPkPfp00fLly//5gViOMkeScg/mAORjfxHNvKPcOH1e+S1cPbbSptwYPs3KSYmRqmpzePCQthH/sEciGzkP7KRf4QLr6LktXD22+uS2x7aPo+/bds2paWlqVu3brrxxhu1d+/eRttWV1ervLy8wQZ3s5N/iTnQHHEMiGzkP7LxHYBw4fNHWd7cwFaUmZmZWrRokZYtW6ZnnnlGu3bt0siRI1VRUXHK9nPmzFFSUlJgS09Pb5KgYYbd/EvMgeaGY0BkI/+Rje8AhJP6M+RWNjewFeXo0aM1btw49e/fXzk5OXr77bd19OhRLV269JTtZ86cqbKyssBWVFTUJEHDDLv5l5gDzQ3HgMhG/iMb3wEIJz59s478dJvPdKAWndXVGG3btlWPHj20ffv2U/48NjZWsbGxZzMEwtiZ8i8xB5o7jgGRjfxHNr4DYJLVO6i45S4rZxVlZWWlduzYoU6dOjVVPHAR8g/mQGQj/5GN/MOk+id1WtncwFaUd999tz744APt3r1ba9as0XXXXafo6GhNmDAhWPEhjJB/MAciG/mPbOQf4aT+SZ1WNjewtWRl3759mjBhgg4fPqwOHTpoxIgRWrt2rTp06BCs+BBGyD+YA5GN/Ec28o9wUuOPUbT/zGVsjTvuemivIH/ppZeaZNARbbepVRvrQ7/03lBH48Resdt2n8JB9sfZ8+Fl9jtJqn4v0XafV3s7GqpJNFX+JenH7QqUkGD9H2gmv/cjR+M4mQNO+owq9truI0lTX7jIdp8LLtnkaKym0FRzwNf1aynexlEyv7OzgUbts92lw5gvbPepWDzQdh9J2umgX7cbCh2N1RSa7BjQuk5qVWe5eYsVzpZE1F6+33afC27aaLvPecVtbPeRpG2LBtvu4yS+ptKU3wFVKT5Fxdm43C7MjwHPFxfa7iNJFyy+yX6ngc7Gam58fo98Fh76Y6VNOOARWwAAAHAVn8VbGrrlok4KcgAAALiK1Yf+uOXBQBTkAAAAcBWvPPJauGDTSptwQEEOAAAAV+EMOQAAAGCQV9bOfju75ULoueOvDQAAAMB/1J8ht7JZNWfOHA0dOlQJCQnq2LGjrr32Wn3xxZnvuvP3v/9dvXr1UlxcnPr166e3337b9vuhIAcAAICrBONJnR988IGmT5+utWvX6v3331dtba2uuOIKHTt2rNE+a9as0YQJEzRlyhRt3rxZ1157ra699lpt2bLF1vthyQoAAABcpc4frVp/tIV21u93v2zZsgZ/XrRokTp27KiNGzfq0ksvPWWfJ598UldeeaXuueceSdKDDz6o999/X0899ZTmz59veWzOkAMAAMBV6h8MZGWTpPLy8gZbdXX1GccoKyuTJLVv377RNgUFBcrOzm6wLycnRwUFBbbeDwU5AAAAXMX7nwcDWdkkKT09XUlJSYFtzpw5p319n8+nO++8U5dccon69u3baLuSkhKlpKQ02JeSkqKSkhJb74clKwAAAHCVb5/9PlM7SSoqKlJiYmJgf2xs7Gn7TZ8+XVu2bNHq1avPLlCLKMgBAADgKj5FyWdhoUd9m8TExAYF+enMmDFDb775plauXKnOnTuftm1qaqpKS0sb7CstLVVqaqqlseqxZAUAAACu4vV7LG9W+f1+zZgxQ6+++qr+9a9/qWvXrmfsk5WVpfz8/Ab73n//fWVlZdl6P0bOkL85NFkxnhaW268ofs3ROD/88H9s91m/I8N2nwsu2WS7jyTF6itH/ZqDn/XJsjUHDv2jtaNxkt/LsN1n77aUMzf6LzlptrtIki6Qs7njdhlTPrGV/3eLCx2Nc/7cWxz1s6v7DWtDMk5zcf4thbbyrxWdHI1zaJq9L0RJOjLA+h0Z6jn//d/orGMz0O2+DbbmwP/udbZs4Dtz77bdZ8J3PrTdJydtoO0+ktRNhY76wf6SFSumT5+uxYsX6/XXX1dCQkJgHXhSUpJatWolSZo4caLOPffcwBr0O+64Q5dddpkee+wxXX311XrppZf00UcfacGCBbbeD2fIAQAA4Cp+iw8F8tu4D/kzzzyjsrIyXX755erUqVNgW7JkSaDN3r17tX///sCfhw8frsWLF2vBggUaMGCAXn75Zb322munvRD0VFhDDgAAAFfxyiOvznz220qben6//4xtVqxYcdK+cePGady4cZbHORUKcgAAALhKnS9KUT4LDwbyeUMQzdmjIAcAAICr+OSRz8LZbyttwgEFOQAAAFzF6h1U7NxlxSQKcgAAALhK/UWbVtq5AQU5AAAAXMUni7c9ZMkKAAAA0PT8FteQ+ynIAQAAgKYXjAcDmURBDgAAAFdhDTkAAABgEGfIz0L9E5DqVCud+WFIAeUVPkfj1R6rsd3H93WV7T51/lrbfcJdnU68JytPrbLD6RzwHq92NF6dx34/5sAJwZgDoT4G+Krs59IJ8m+N0/zLwbFckrw19vPv+9r+XCP/1jmdAxUhPAZUV9rPZ53fWXzhKlj5b0p1/ih5LJz9ruMM+ckqKiokSav1tq1+7Xo4HXGn0462FIVkFDMqKiqUlJTUpK8n2Z8DuvH1JoshGJgD1l9LCuUx4FdOO9qyNySjmBEO+ddVTTJ80PD7b+/1JPtzoH9vpyPaPwY87nSoZqip89+UOEN+FtLS0lRUVKSEhAR5PA0/oPLycqWnp6uoqEiJiYmhDCvshMNn4ff7VVFRobS0tCZ93cbmQDi853ARLp9FMOYAxwBrwuGzIP/mhMNnwXeAOeHwWQQr/02JgvwsREVFqXPnzqdtk5iYGPG/jPVMfxbB+FvxmeaA6fccTsLhs2jqOcAxwB7TnwX5N8v0Z8F3gFmmP4twPTNej4IcAAAAMMgvaw/9Cd9V8A1RkAMAAMBVOEMeJLGxscrLy1NsbKzpUIyLxM8iEt9zYyL1s4jU930qkfhZROJ7bkwkfhaR+J4bw2dhTXMryD3+cL6nDQAAAPAf5eXlSkpK0qX/vE0xrc/8l5a6Y9Vaec3TKisrC+vrE8LmDDkAAABgRXM7Q05BDgAAAFfx+z3yWyi2rbQJBxTkAAAAcJU6f5TEkzoBAAAAMzhDDgAAABjU3NaQh815/Hnz5ikjI0NxcXHKzMzU+vXrTYcUcg888IA8Hk+DrVevXqbDCgnyT/7JP/kn/5GZf4k5IDEH7Ko/Q25lc4OwKMiXLFmi3Nxc5eXladOmTRowYIBycnJ04MAB06GFXJ8+fbR///7Atnr1atMhBR35/wb5J//kn/xHUv4l5sC3ReoccML/nzPkZ9ooyG14/PHHNXXqVE2ePFm9e/fW/PnzFR8fr4ULF5oOLeRiYmKUmpoa2JKTk02HFHTk/xvkn/yTf/IfSfmXmAPfFqlzwAm/JL/fwmY6UIuMF+Q1NTXauHGjsrOzA/uioqKUnZ2tgoICg5GZsW3bNqWlpalbt2668cYbtXfvXtMhBRX5b4j8k3/yT/4jJf8Sc+C/ReIccMonj+XNDYwX5IcOHZLX61VKSkqD/SkpKSopKTEUlRmZmZlatGiRli1bpmeeeUa7du3SyJEjVVFRYTq0oCH/3yD/3yD/5J/8N//8S8yBb4vUOeBUc1tDzl1Wwsjo0aMD/9+/f39lZmaqS5cuWrp0qaZMmWIwMoQC+Y9s5D+ykX8wB+zx+T3yNKO7rBgvyJOTkxUdHa3S0tIG+0tLS5WammooqvDQtm1b9ejRQ9u3bzcdStCQ/8aRf/JP/iNXJORfYg6cTqTMAad8Po88PgsFuYU24cD4kpWWLVtq8ODBys/PD+zz+XzKz89XVlaWwcjMq6ys1I4dO9SpUyfToQQN+W8c+Sf/5D9yRUL+JebA6UTKHHCKJStBkJubq0mTJmnIkCEaNmyYnnjiCR07dkyTJ082HVpI3X333brmmmvUpUsXFRcXKy8vT9HR0ZowYYLp0IKK/J9A/sk/+Sf/kZZ/iTlQL5LngBMsWQmC8ePH6+DBg5o1a5ZKSko0cOBALVu27KSLPJq7ffv2acKECTp8+LA6dOigESNGaO3aterQoYPp0IKK/J9A/sk/+Sf/kZZ/iTlQL5LngBP1tzW00s4NPH6/W0IFAABAJCsvL1dSUpIu+Ot9io6PO2N77/EqbfvRb1VWVqbExMQQROhMWJwhBwAAAKyyuj6cNeQAAABAEPhl7SmcblkGYvwuKwAAAIAdwbrLysqVK3XNNdcoLS1NHo9Hr7322mnbr1ixQh6P56TN7oOtKMgBAADgLn4bmw3Hjh3TgAEDNG/ePFv9vvjiC+3fvz+wdezY0VZ/lqwAAADAVfw+j6WH/vhtPhho9OjRDZ6aalXHjh3Vtm1b2/3qcYYcAAAArmJ3yUp5eXmDrbq6uknjGThwoDp16qTvfve7+vDDD233pyAHAACAu/g91jdJ6enpSkpKCmxz5sxpkjA6deqk+fPn65VXXtErr7yi9PR0XX755dq0aZOt12HJCgAAAFzF7oOBioqKGtyHPDY2tkni6Nmzp3r27Bn48/Dhw7Vjxw7NnTtXf/nLXyy/DgU5AAAA3MXmfQ8TExND9mCgYcOGafXq1bb6UJADAADAVcL5wUCFhYXq1KmTrT4U5AAAAHCfIDz1p7KyUtu3bw/8edeuXSosLFT79u113nnnaebMmfryyy/1wgsvSJKeeOIJde3aVX369FFVVZX+9Kc/6V//+pfee+89W+NSkAMAAMBVgnWG/KOPPtJ3vvOdwJ9zc3MlSZMmTdKiRYu0f/9+7d27N/Dzmpoa3XXXXfryyy8VHx+v/v37a/ny5Q1ewwqP329lSTwAAABgVnl5uZKSkpQ+P09RreLO2N73dZWKbvm1ysrKQraG3AnOkAMAAMBdvnVLwzO2cwEKcgAAALiLzbushDsKcgAAALgLZ8gBAAAAc+w+GCjcUZADAADAXViyAgAAABjEkhUAAADAHI//xGalnRtQkAMAAMBdWLICAAAAGMSSFQAAAMAgzpADAAAABvn+s1lp5wIU5AAAAHAXlqwAAAAA5nCXFQAAAMCkZraGPMp0AAAAAEAk4ww5AAAAXMUji0tWgh5J06AgBwAAgLtwUScAAABgUDNbQ05BDgAAAHehIAcAAADM8fhObFbauQEFOQAAANyFM+QAAACAOTwYCAAAADCJu6wAAAAABrFkBQAAADCHJSsAAACASZwhBwAAAAyyeIacghwAAAAIBs6QAwAAAOY0twcDRZkOAAAAAIhknCEHAACAu7BkBQAAADCH2x4CAAAAprmk2LaCghwAAADuwpIVAAAAwJzmtmSFu6wAAADAXfw2NhtWrlypa665RmlpafJ4PHrttdfO2GfFihW66KKLFBsbq+7du2vRokX2BhUFOQAAAFym/gy5lc2OY8eOacCAAZo3b56l9rt27dLVV1+t73znOyosLNSdd96pm2++We+++66tcVmyAgAAAHfx/Wez0s6G0aNHa/To0Zbbz58/X127dtVjjz0mSbrwwgu1evVqzZ07Vzk5OZZfhzPkAAAAcBW7Z8jLy8sbbNXV1U0SR0FBgbKzsxvsy8nJUUFBga3XoSAHAACAu9hcQ56enq6kpKTANmfOnCYJo6SkRCkpKQ32paSkqLy8XF9//bXl12HJCgAAANzF5m0Pi4qKlJiYGNgdGxsblLCcoiAHAACAq9i97WFiYmKDgryppKamqrS0tMG+0tJSJSYmqlWrVpZfhyUrAAAAcJcg3fbQrqysLOXn5zfY9/777ysrK8vW61CQAwAAwFWCddvDyspKFRYWqrCwUNKJ2xoWFhZq7969kqSZM2dq4sSJgfa33HKLdu7cqXvvvVeff/65nn76aS1dulQ/+9nPbI1LQQ4AAAB3CdIZ8o8++kiDBg3SoEGDJEm5ubkaNGiQZs2aJUnav39/oDiXpK5du+qtt97S+++/rwEDBuixxx7Tn/70J1u3PJQkj9/vd8lDRQEAABDJysvLlZSUpAtve1jRsXFnbO+trtJnT/9CZWVlQVlD3lS4qBMAAACu4vnPZqWdG7BkxaEVK1bI4/Gcclu7dq3p8BAimzZt0pgxY9S+fXvFx8erb9+++sMf/mA6LITATTfd1OgxwOPx6MsvvzQdIoJs27Zt+uEPf6jOnTsrPj5evXr10uzZs3X8+HHToSEENm7cqCuvvFKJiYlKSEjQFVdcEVh3jODz+KxvbsAZ8rN0++23a+jQoQ32de/e3VA0CKX33ntP11xzjQYNGqT7779fbdq00Y4dO7Rv3z7ToSEEfvKTn5z0dDa/369bbrlFGRkZOvfccw1FhlAoKirSsGHDlJSUpBkzZqh9+/YqKChQXl6eNm7cqNdff910iAiiTZs2acSIEUpPT1deXp58Pp+efvppXXbZZVq/fr169uxpOsTmz+Z9yMMdBflZGjlypMaOHWs6DIRYeXm5Jk6cqKuvvlovv/yyoqL4x6ZIk5WVddJtrVavXq3jx4/rxhtvNBQVQuUvf/mLjh49qtWrV6tPnz6SpGnTpsnn8+mFF17QkSNH1K5dO8NRIljuv/9+tWrVSgUFBTrnnHMkST/60Y/Uo0cP/eIXv9Arr7xiOMII4ZJi2wqqiCZQUVGhuro602EghBYvXqzS0lI99NBDioqK0rFjx+TzueTfxRA0ixcvlsfj0Q033GA6FARZeXm5JJ30yOxOnTopKipKLVu2NBEWQmTVqlXKzs4OFOPSidxfdtllevPNN1VZWWkwusgQrNsemkJBfpYmT56sxMRExcXF6Tvf+Y4++ugj0yEhBJYvX67ExER9+eWX6tmzp9q0aaPExETdeuutqqqqMh0eDKitrdXSpUs1fPhwZWRkmA4HQXb55ZdLkqZMmaLCwkIVFRVpyZIleuaZZ3T77berdevWZgNEUFVXV5/yKYzx8fGqqanRli1bDEQVYcLkwUBNhSUrDrVs2VI/+MEPdNVVVyk5OVmffvqpHn30UY0cOVJr1qwJ3L8SzdO2bdtUV1en733ve5oyZYrmzJmjFStW6I9//KOOHj2qv/3tb6ZDRIi9++67Onz4MMtVIsSVV16pBx98UA8//LDeeOONwP5f/vKX+s1vfmMwMoRCz549tXbtWnm9XkVHR0uSampqtG7dOkniou4QsHr22y1nyCnIHRo+fLiGDx8e+POYMWM0duxY9e/fXzNnztSyZcsMRodgq6ys1PHjx3XLLbcE7qry/e9/XzU1NXr22Wc1e/ZsXXDBBYajRCgtXrxYLVq00PXXX286FIRIRkaGLr30Uv3gBz/QOeeco7feeksPP/ywUlNTNWPGDNPhIYhuu+023XrrrZoyZYruvfde+Xw+/eY3v9H+/fslSV9//bXhCCMAF3WiMd27d9f3vvc9/eMf/2jwt2Y0P/X/VDlhwoQG+2+44QY9++yzKigooCCPIJWVlXr99deVk5PTYE0pmq+XXnpJ06ZN09atW9W5c2dJJ/5S7vP59POf/1wTJkxgLjRjt9xyi4qKivTII4/oz3/+syRpyJAhuvfee/XQQw+pTZs2hiNs/prbGXLWkDex9PR01dTU6NixY6ZDQRClpaVJOvmCro4dO0qSjhw5EvKYYM5rr73G3VUizNNPP61BgwYFivF6Y8aM0fHjx7V582ZDkSFUHnroIZWWlmrVqlX6+OOPtWHDhsDF/T169DAcXQRoZmvIKcib2M6dOxUXF8ffjpu5wYMHSzp5nWBxcbEkqUOHDiGPCea8+OKLatOmjcaMGWM6FIRIaWmpvF7vSftra2sliTtvRYh27dppxIgR6tevn6QTF/x37txZvXr1MhxZ89fcHgxEQe7QwYMHT9r3f//3f3rjjTd0xRVXcF/qZq5+nfBzzz3XYP+f/vQnxcTEBO7AgObv4MGDWr58ua677jrFx8ebDgch0qNHD23evFlbt25tsP9vf/uboqKi1L9/f0ORwZQlS5Zow4YNuvPOO6kBQqGZnSFnDblD48ePV6tWrTR8+HB17NhRn376qRYsWKD4+Hj99re/NR0egmzQoEH68Y9/rIULF6qurk6XXXaZVqxYob///e+aOXNmYEkLmr8lS5aorq6O5SoR5p577tE777yjkSNHasaMGTrnnHP05ptv6p133tHNN9/MMaCZW7lypWbPnq0rrrhC55xzjtauXavnn39eV155pe644w7T4UUEj98vj//M1baVNuHA4/e7JNIw84c//EEvvviitm/frvLycnXo0EGjRo1SXl6eunfvbjo8hEBtba0efvhhPf/88youLlaXLl00ffp03XnnnaZDQwhlZWVp586dKi4u5kLuCLN+/Xo98MAD2rx5sw4fPqyuXbtq0qRJuvfeexUTw/mu5mzHjh267bbbtGnTJlVUVARyn5uby0Ohgqy8vFxJSUka+KOHFN0y7oztvTVVKvzrL1VWVqbExMQQROgMBTkAAABcob4gH3Sj9YJ884vhX5DzV3gAAAC4C/chBwAAAMxpbvchpyAHAACAu3CGHAAAADCHM+QAAACASX6LD/2hID+Zz+dTcXGxEhIS5PF4Qjk0bPL7/aqoqFBaWlqTPuCAOeAewZgD5N89yH9k4zsgsgUr/03K7z+xWWnnAiEtyIuLi5Wenh7KIXGWioqK1Llz5yZ7PeaA+zTlHCD/7kP+IxvfAZGtqfPflFiychYSEhIkSSN0lWLUwnK/X3xc6Gi8h/sPdNQvVLY/Pch2nxatahyN1WXyv221r1OtVuvtQM6aitM58PWrXRyN1+q6PY76hcr+xb1s96k67uyhE11v/thW+2DMAaf53/nboY7G63bfBkf9QiU1v03IxioZVWmrPfkPPif533Kok6Oxksdvs9U+3L4DmusceHXrJ7b7DH5hiqOxzvvNesttg5X/JsVFnc7V//NUjFooxmP9F7F1grN/LrEzhglRrc58Q/uT+sSH6LP4zwRu6n9SdDoHYlrHOhov3OdAdLz99xWlEH0WQZgDTvMfFWf/d0UK//y3bBO6J/qR//DjJP/Rx937+//t12MOnJDooL4JyWcRpPw3JY/P2hpyS+vMw4Cj6m7evHnKyMhQXFycMjMztX699b91wf3If2Qj/2AORDbyj7Dgt7G5gO2CfMmSJcrNzVVeXp42bdqkAQMGKCcnRwcOHAhGfAgz5D+ykX8wByIb+Ue4qF9DbmVzA9sF+eOPP66pU6dq8uTJ6t27t+bPn6/4+HgtXLgwGPEhzJD/yEb+wRyIbOQfYaP+LitWNhewVZDX1NRo48aNys7O/uYFoqKUnZ2tgoKCk9pXV1ervLy8wQb3spt/iTnQnJB/8B0Q2TgGIJxE9BnyQ4cOyev1KiUlpcH+lJQUlZSUnNR+zpw5SkpKCmzc6sjd7OZfYg40J+QffAdENo4BCCf1F3Va2dwgqHd7nzlzpsrKygJbUVFRMIdDGGIORDbyH9nIP5gDCJpmtmTF1m0Pk5OTFR0drdLS0gb7S0tLlZqaelL72NhYxcY6u0UTwo/d/EvMgeaE/IPvgMjGMQDhpLk9GMjWGfKWLVtq8ODBys/PD+zz+XzKz89XVlZWkweH8EL+Ixv5B3MgspF/hJVmdttD2w8Gys3N1aRJkzRkyBANGzZMTzzxhI4dO6bJkycHIz6EGfIf2cg/mAORjfwjXDS3M+S2C/Lx48fr4MGDmjVrlkpKSjRw4EAtW7bspIs80DyR/8hG/sEciGzkH2HD5z+xWWnnAh6/P3Sr3cvLy09cZf3og7YeG3/BjHWOxjvnw3a2+xy+5IijsZzw5YfuavM9B+19Fr7jVdo1+WGVlZUpMTGxyeKonwNHtnaz9cjgnLSBjsbb+uxQ2316/GSDo7Gc+Prdrrb7XJ22xdFYbxX3tdW+7li11l33hyadA/X5z3juV4qKt34M6HZDoaPxti5wkP9pocv/7gft/zP/+5MecTTWzlp7OTxW4dWYATuCkv/L/3mrYlrbWFc8ap+zAfM72+/jdCwHts+92HafX1z5mqOxHl52ra32vqoq7b3vV0H7Dnjyo4vVqo31c4JLLzz1GvUz2bl4oO0+To83TjiZA075Wnutt/26Svtuz2vy/DeF+jk0PPvXimlx5u+RutoqrVkenu/l24J6lxUAAACgqXlk8T7kDl573rx5ysjIUFxcnDIzM7V+/fpG2y5atEgej6fBFhdn/YRTPQpyAAAAuEuQbnu4ZMkS5ebmKi8vT5s2bdKAAQOUk5OjAwcONNonMTFR+/fvD2x79uyx/XYoyAEAAOAqwXpS5+OPP66pU6dq8uTJ6t27t+bPn6/4+HgtXLiw8Vg8HqWmpgY2J9dUUJADAADAVTw+v+VNOrH2/NtbdXX1Sa9ZU1OjjRs3Kjs7O7AvKipK2dnZKigoaDSWyspKdenSRenp6fre976nf//737bfDwU5AAAA3MVnY5OUnp6upKSkwDZnzpyTXvLQoUPyer0nneFOSUlRSUnJKcPo2bOnFi5cqNdff11//etf5fP5NHz4cO3bZ+8Ccdu3PQQAAABM8vj98lhYH17fpqioqMFdVprqCbJZWVkNHow1fPhwXXjhhXr22Wf14IMPWn4dCnIAAAC4i9WncP6nTWJi4hlve5icnKzo6GiVlpY22F9aWqrUVGu33mzRooUGDRqk7du3W2pfjyUrAAAAcJcg3GWlZcuWGjx4sPLz8wP7fD6f8vPzG5wFPx2v16tPPvlEnTp1svV2OEMOAAAAV7F6BxW7d1nJzc3VpEmTNGTIEA0bNkxPPPGEjh07psmTJ0uSJk6cqHPPPTewBn327Nm6+OKL1b17dx09elSPPPKI9uzZo5tvvtnWuBTkAAAAcBerZ79t3od8/PjxOnjwoGbNmqWSkhINHDhQy5YtC1zouXfvXkVFfbPA5MiRI5o6dapKSkrUrl07DR48WGvWrFHv3r1tjUtBDgAAAFfx+E5sVtrZNWPGDM2YMeOUP1uxYkWDP8+dO1dz5861P8h/oSAHAACAuwTpDLkpFOQAAABwlW8/9OdM7dzASEF+/t0fKcbTwnL7nC3ljsZ5t6+jbiETNaooZGMN+bDSVvvaYzXaFaRYJOm6Hv1szYHiV+2txarX47oNjvqFSqsc+5/yv9Ta0Vilv7F2y6Z6vqoqR+NYkTHlE1v5V35nR+P0GBXe+c+4v/EnvzVm6v0jHI21fe7FttqfyP+vHI11RtcUSzbyv3XBUEfDhHv+u/9sre0+S39m7/e4nm+B1177KHvt7Xp1SEpIjgHdRhU66hcqTuaAU2lrEyy3rams0UtBjKVJcIYcAAAAMMivwFM4z9jOBSjIAQAA4Cp2n9QZ7ijIAQAA4C5+WVyyEvRImgQFOQAAANyFNeQAAACAQT5JHovtXICCHAAAAK7CGnIAAADAJJasAAAAAAb5fJLHwnoUnzvWrFCQAwAAwF1YQw4AAACYwxpyAAAAwCTWkAMAAAAG+fySx0Kx7aMgbzLv9k00HUJQbHsq03afC2asczTW54c72mrvPV7taJxgSbvuU9MhBMW+mcNt9+k8Z42jsVodsLLY7hveanvtg2rUPtMRBMX1n5XY7rP0wlRHY+0YP99W+/IKn9rd52ioJtdj2gbTIQTFwTd62u7TYcwXjsa6vP/nttrXVNboJUcjBUkzPQaEcg6seb+v5ba+qipHY4QUZ8gBAAAAkywW5KIgBwAAAJoeZ8gBAAAAg3x+WTr7zRpyAAAAIAh8Xklei+3CHwU5AAAA3IUz5AAAAIBBrCEHAAAADPLLYkEe9EiaBAU5AAAA3IUz5AAAAIBBPp8kn8V24Y+CHAAAAO7CGXIAAADAIApyAAAAwCBue4imEn0synafbU9lOhrrgjHrbLWv89c6Ggf21LW2f6AofrW3o7HSrltjq32dv1afOhoJVi0tHmK7z9DCPY7GykkbaKv9iWPATkdjwZojJYm2+wxYm+BorOKLK2y15zsgNJzMgU3FhY7Gykmz3rbOXxv2v/1+v09+/5nXh1tpEw4oyAEAAOAuPp9kpdimIAcAAACCwOeTPBTkAAAAgBl+i2vIuagTAAAAaHp+n09+C2fIWUMOAAAABANnyAEAAACDfH7JQ0EOAAAAmOH3S7JyUac7CnL7N8IGAAAADPL7/JY3u+bNm6eMjAzFxcUpMzNT69evP237v//97+rVq5fi4uLUr18/vf3227bHpCAHAACAu/h91jcblixZotzcXOXl5WnTpk0aMGCAcnJydODAgVO2X7NmjSZMmKApU6Zo8+bNuvbaa3Xttddqy5YttsalIAcAAICr1HqrVFtnYfNWSZLKy8sbbNXV1ad83ccff1xTp07V5MmT1bt3b82fP1/x8fFauHDhKds/+eSTuvLKK3XPPffowgsv1IMPPqiLLrpITz31lK33wxpyAAAAuELLli2Vmpqq1SXWl4W0adNG6enpDfbl5eXpgQceaLCvpqZGGzdu1MyZMwP7oqKilJ2drYKCglO+dkFBgXJzcxvsy8nJ0WuvvWY5PomCHAAAAC4RFxenXbt2qaamxnIfv98vj8fTYF9sbOxJ7Q4dOiSv16uUlJQG+1NSUvT555+f8rVLSkpO2b6kpMRyfBIFOQAAAFwkLi5OcXFxpsNoUkYK8oNTMxXd0voH2fHpNUGMpqGDb/S03efIoQRHY/X48an/+eN09uYNdzRWuDl/Raxatmlhuf22oade6xUMX7/b1XafI8dbORor4zr7c2DyF3scjfW8ujjqFwzXfVSqVm2sH36WXpgaxGgacnIMeG3gc47GmnreCNt9un520NFYGxS6z/BMdj/XT1Hx1r8Dut1QGLxg/svMHR+HbKw559vv83xxoaOxcjTQUb9gCec5sH3uxbb7dO+/z9FYPUZtsN0nJ///ORpLchZjJElOTlZ0dLRKS0sb7C8tLVVq6qmPoampqbbaN4aLOgEAABDxWrZsqcGDBys/Pz+wz+fzKT8/X1lZWafsk5WV1aC9JL3//vuNtm8MS1YAAAAASbm5uZo0aZKGDBmiYcOG6YknntCxY8c0efJkSdLEiRN17rnnas6cOZKkO+64Q5dddpkee+wxXX311XrppZf00UcfacGCBbbGtXWG/IEHHpDH42mw9erVy9aAcC/yD+ZAZCP/kY38IxKMHz9ejz76qGbNmqWBAweqsLBQy5YtC1y4uXfvXu3fvz/Qfvjw4Vq8eLEWLFigAQMG6OWXX9Zrr72mvn372hrX9hnyPn36aPny5d+8QAwn2SMJ+QdzILKR/8hG/hEJZsyYoRkzZpzyZytWrDhp37hx4zRu3LizGtP2b1JMTIzthepoPsg/mAORjfxHNvIPBIftizq3bdumtLQ0devWTTfeeKP27t3baNvq6uqTnowEd7OTf4k50BxxDIhs5D+y8R0ABIetgjwzM1OLFi3SsmXL9Mwzz2jXrl0aOXKkKioqTtl+zpw5SkpKCmz//ZQkuIvd/EvMgeaGY0BkI/+Rje8AIHhsFeSjR4/WuHHj1L9/f+Xk5Ojtt9/W0aNHtXTp0lO2nzlzpsrKygJbUVFRkwQNM+zmX2IONDccAyIb+Y9sfAcAwXNWV2O0bdtWPXr00Pbt20/589jY2FM+mhTNw5nyLzEHmjuOAZGN/Ec2vgOApnNWDwaqrKzUjh071KlTp6aKBy5C/sEciGzkP7KRf6Dp2CrI7777bn3wwQfavXu31qxZo+uuu07R0dGaMGFCsOJDGCH/YA5ENvIf2cg/EDy2lqzs27dPEyZM0OHDh9WhQweNGDFCa9euVYcOHYIVH8II+QdzILKR/8hG/oHg8fj9fn+oBisvL1dSUpKObO2mxATrJ+d7rJzoaLyuP/zYUT+7zvmwnaN+m9+70Haf8369xtFYdtX5a7VCr6usrEyJiYlN9rr1c+B/N12k+DbRlvvtqUl2NN6/+rV21M+u//nkmKN+//vxCNt9zr9xs6Ox7ArGHKjPf+6H/0+xbVpY7tc19qCj8ZZeGJr7JUe3c3YM2Davi+0+3W4odDSWXcHM/8efdlSCje+AvOLRjsYrvrjxu380pbS1CY76jUjaZrtPqOZ0sL8D7B4D3t7b29F4HcZ84aifXQff6OmoX0VlK9t9QnEMCFb+0bizWkMOAAAA4OxQkAMAAAAGUZADAAAABlGQAwAAAAZRkAMAAAAGUZADAAAABlGQAwAAAAZRkAMAAAAGUZADAAAABlGQAwAAAAZRkAMAAAAGUZADAAAABlGQAwAAAAbFmBj0uh79FONpYbl93VOtHI3jy0+33Wd0yr9t93m3r+0ukqTztMZZx2bgxYvSbc2BWTs3ORrnhZkzbPc55/L9tvv8q98u230k6XxtdtTP7TZdEq0YT7Tl9ksWX+loHN/cOEf97Or+s7WO+nW74UgTR+IOt/fOsvX7/27xKkfj/Kqwn+0+Gw53sd2n+OJ9tvtI0lKlOurXHNg9BlQsdlYHlM292Haf7v3t57PDqC9s95GkDo56oTniDDkAAABgEAU5AAAAYBAFOQAAAGAQBTkAAABgEAU5AAAAYBAFOQAAAGAQBTkAAABgEAU5AAAAYBAFOQAAAGAQBTkAAABgEAU5AAAAYFBMKAfz+/2SpDrVSn7r/XxfVzkar+5Yte0+VZV19sfx19ruE+7qdOI91eesqTidA8cqfI7G81bbnztO5g1zwBrHx4Djzo4BPmfdbCP/1jjNf7nD3//qSvt5cfL7L/JvmRuOAcyB4OUfjfP4Q/hp79u3T+np6aEaDk2gqKhInTt3brLXYw64T1POAfLvPuQ/svEdENmaOv9oXEgLcp/Pp+LiYiUkJMjj8TT4WXl5udLT01VUVKTExMRQhRSWwuGz8Pv9qqioUFpamqKimm5lU2NzIBzec7gIl88iGHOAY4A14fBZkH9zwuGz4DvAnHD4LIKVfzQupEtWoqKizvg3rcTExIj/Zaxn+rNISkpq8tc80xww/Z7DSTh8Fk09BzgG2GP6syD/Zpn+LPgOMMv0ZxGM/KNx/LUHAAAAMIiCHAAAADAobAry2NhY5eXlKTY21nQoxkXiZxGJ77kxkfpZROr7PpVI/Cwi8T03JhI/i0h8z43hs4hMIb2oEwAAAEBDYXOGHAAAAIhEFOQAAACAQRTkAAAAgEEU5AAAAIBBFOQAAACAQWFTkM+bN08ZGRmKi4tTZmam1q9fbzqkkHvggQfk8XgabL169TIdVkiQf/JP/sk/+Y/M/EvMAYk5EOnCoiBfsmSJcnNzlZeXp02bNmnAgAHKycnRgQMHTIcWcn369NH+/fsD2+rVq02HFHTk/xvkn/yTf/IfSfmXmAPfFqlzAGFSkD/++OOaOnWqJk+erN69e2v+/PmKj4/XwoULTYcWcjExMUpNTQ1sycnJpkMKOvL/DfJP/sk/+Y+k/EvMgW+L1DmAMCjIa2pqtHHjRmVnZwf2RUVFKTs7WwUFBQYjM2Pbtm1KS0tTt27ddOONN2rv3r2mQwoq8t8Q+Sf/5J/8R0r+JebAf4vEOYATjBfkhw4dktfrVUpKSoP9KSkpKikpMRSVGZmZmVq0aJGWLVumZ555Rrt27dLIkSNVUVFhOrSgIf/fIP/fIP/kn/w3//xLzIFvi9Q5gBNiTAeAb4wePTrw//3791dmZqa6dOmipUuXasqUKQYjQyiQ/8hG/iMb+QdzILIZP0OenJys6OholZaWNthfWlqq1NRUQ1GFh7Zt26pHjx7avn276VCChvw3jvyTf/IfuSIh/xJz4HQiZQ7gBOMFecuWLTV48GDl5+cH9vl8PuXn5ysrK8tgZOZVVlZqx44d6tSpk+lQgob8N478k3/yH7kiIf8Sc+B0ImUO4D/8YeCll17yx8bG+hctWuT/9NNP/dOmTfO3bdvWX1JSYjq0kLrrrrv8K1as8O/atcv/4Ycf+rOzs/3Jycn+AwcOmA4tqMj/CeSf/JN/8h9p+ff7mQP1InkOwO8PizXk48eP18GDBzVr1iyVlJRo4MCBWrZs2UkXeTR3+/bt04QJE3T48GF16NBBI0aM0Nq1a9WhQwfToQUV+T+B/JN/8k/+Iy3/EnOgXiTPAUgev9/vNx0EAAAAEKmMryEHAAAAIhkFOQAAAGAQBTkAAABgEAU5AAAAYBAFOQAAAGAQBTkAAABgEAU5AAAAYBAFOQAAAGAQBTkAAABgEAU5AAAAYBAFOQAAAGDQ/wdnImwBmjg3ZQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "show_images(V_motorcycle, \"Flow Motorcycle\")"
      ],
      "metadata": {
        "id": "xM0UGu03PwzT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "outputId": "6071dc2a-ebe8-47ee-c954-a802cb1b7183"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 11 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "show_images(truck_traversable_distance, \"Truck Traversable Distance\")"
      ],
      "metadata": {
        "id": "RhdBTOsiuUCn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "outputId": "88c26325-ca40-47ac-cef5-7597b95080a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 11 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "show_images(car_traversable_distance, \"Car Traversable Distance\")"
      ],
      "metadata": {
        "id": "La0hE9rgzzoQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "outputId": "836bd10e-bff8-4cb6-e051-7a1b0e38675d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 11 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "show_images(motorcycle_traversable_distance, \"Motorcycle Traversable Distance\")\n",
        "\n"
      ],
      "metadata": {
        "id": "_8GOmf382OLF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "outputId": "cf316662-94e9-493e-9345-76cfbcdca8c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 11 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "show_images(bus_traversable_distance, \"Bus Traversable Distance\")"
      ],
      "metadata": {
        "id": "8vb02enR2uo-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "outputId": "380d0226-566c-4a94-cf3f-03525729f91f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 11 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Sample multiclass data (replace this with your actual dataset)\n",
        "# Assume the data is a 2D array where each row represents a sample and the last column represents the class label\n",
        "data = np.random.randn(500, 2)\n",
        "class_labels = np.random.randint(0, 3, 500)\n",
        "\n",
        "# Convert the data to a Pandas DataFrame\n",
        "import pandas as pd\n",
        "df = pd.DataFrame(data, columns=['feature_1', 'feature_2'])\n",
        "df['class_label'] = class_labels\n",
        "\n",
        "# Create the density plot\n",
        "sns.kdeplot(data=df, x='feature_1', y='feature_2', hue='class_label', fill=True, cmap='viridis', levels=5)\n",
        "\n",
        "# Add a legend\n",
        "plt.legend(title='Class Label', loc='upper right')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "plrdQsNthvHc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "outputId": "193601a9-f607-471b-ecbc-3ae29c7ea324"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/seaborn/distributions.py:1138: UserWarning: cmap parameter ignored when using hue mapping.\n",
            "  warnings.warn(msg, UserWarning)\n",
            "WARNING:matplotlib.legend:No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def find_shortest_paths(graph, start_point):\n",
        "    # initialize graphs to track if a point is visited,\n",
        "    # current calculated distance from start to point,\n",
        "    # and previous point taken to get to current point\n",
        "    visited = [[False for col in row] for row in graph]\n",
        "    distance = [[float('inf') for col in row] for row in graph]\n",
        "    distance[start_point[0]][start_point[1]] = 0\n",
        "    prev_point = [[None for col in row] for row in graph]\n",
        "    n, m = len(graph), len(graph[0])\n",
        "    number_of_points, visited_count = n * m, 0\n",
        "    directions = [(0, 1), (1, 0), (-1, 0), (0, -1)]\n",
        "    min_heap = []\n",
        "\n",
        "    # min_heap item format:\n",
        "    # (pt's dist from start on this path, pt's row, pt's col)\n",
        "    heapq.heappush(min_heap, (distance[start_point[0]][start_point[1]], start_point[0], start_point[1]))\n",
        "\n",
        "    while visited_count < number_of_points:\n",
        "        current_point = heapq.heappop(min_heap)\n",
        "        distance_from_start, row, col = current_point\n",
        "        for direction in directions:\n",
        "            new_row, new_col = row + direction[0], col + direction[1]\n",
        "            if -1 < new_row < n and -1 < new_col < m and not visited[new_row][new_col]:\n",
        "                dist_to_new_point = distance_from_start + graph[new_row][new_col]\n",
        "                if dist_to_new_point < distance[new_row][new_col]:\n",
        "                    distance[new_row][new_col] = dist_to_new_point\n",
        "                    prev_point[new_row][new_col] = (row, col)\n",
        "                    heapq.heappush(min_heap, (dist_to_new_point, new_row, new_col))\n",
        "        visited[row][col] = True\n",
        "        visited_count += 1\n",
        "\n",
        "    return distance, prev_point"
      ],
      "metadata": {
        "id": "2AyVjEcJT_m-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "graph = [\n",
        "    [1, 1, 6, 3, 7, 5, 1, 7, 4, 2],\n",
        "    [1, 3, 8, 1, 3, 7, 3, 6, 7, 2],\n",
        "    [2, 1, 3, 6, 5, 1, 1, 3, 2, 8],\n",
        "    [3, 6, 9, 4, 9, 3, 1, 5, 6, 9],\n",
        "    [7, 4, 6, 3, 4, 1, 7, 1, 1, 1],\n",
        "    [1, 3, 1, 9, 1, 2, 8, 1, 3, 7],\n",
        "    [1, 3, 5, 9, 9, 1, 2, 4, 2, 1],\n",
        "    [3, 1, 2, 5, 4, 2, 1, 6, 3, 9],\n",
        "    [1, 2, 9, 3, 1, 3, 8, 5, 2, 1],\n",
        "    [2, 3, 1, 1, 9, 4, 4, 5, 8, 1],\n",
        "]"
      ],
      "metadata": {
        "id": "fMfEKF_3UC6Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "distance, prev_point = find_shortest_paths(graph, (0, 0))"
      ],
      "metadata": {
        "id": "aHBasbmdUDYw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "distance"
      ],
      "metadata": {
        "id": "o0JwA07dUGti",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "85300427-5e9a-4107-9666-660ad89ab0d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[0, 1, 7, 10, 17, 22, 23, 30, 34, 36],\n",
              " [1, 4, 12, 11, 14, 21, 23, 29, 32, 34],\n",
              " [3, 4, 7, 13, 18, 19, 20, 23, 25, 33],\n",
              " [6, 10, 16, 17, 26, 22, 21, 26, 31, 38],\n",
              " [13, 14, 20, 20, 24, 23, 28, 27, 28, 29],\n",
              " [14, 17, 18, 27, 25, 25, 33, 28, 31, 36],\n",
              " [15, 18, 23, 32, 34, 26, 28, 32, 33, 34],\n",
              " [18, 19, 21, 26, 30, 28, 29, 35, 36, 43],\n",
              " [19, 21, 30, 29, 30, 31, 37, 40, 38, 39],\n",
              " [21, 24, 25, 26, 35, 35, 39, 44, 46, 40]]"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(prev_point)"
      ],
      "metadata": {
        "id": "CUyWlpEvUKlx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dcd5dbde-7eb6-416a-d2ee-18903a2fa738"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[None, (0, 0), (0, 1), (0, 2), (0, 3), (0, 4), (0, 5), (0, 6), (0, 7), (0, 8)], [(0, 0), (0, 1), (1, 1), (0, 3), (1, 3), (1, 4), (2, 6), (1, 6), (2, 8), (1, 8)], [(1, 0), (2, 0), (2, 1), (2, 2), (2, 3), (2, 4), (2, 5), (2, 6), (2, 7), (2, 8)], [(2, 0), (2, 1), (2, 2), (2, 3), (3, 3), (2, 5), (2, 6), (3, 6), (2, 8), (4, 9)], [(3, 0), (3, 1), (4, 1), (3, 3), (4, 3), (3, 5), (3, 6), (3, 7), (4, 7), (4, 8)], [(4, 0), (4, 1), (5, 1), (5, 2), (4, 4), (4, 5), (5, 5), (4, 7), (4, 8), (4, 9)], [(5, 0), (6, 0), (5, 2), (6, 2), (5, 4), (5, 5), (6, 5), (5, 7), (5, 8), (6, 8)], [(6, 0), (6, 1), (7, 1), (7, 2), (7, 3), (6, 5), (6, 6), (7, 6), (6, 8), (6, 9)], [(7, 0), (7, 1), (7, 2), (7, 3), (8, 3), (7, 5), (7, 6), (7, 7), (7, 8), (8, 8)], [(8, 0), (8, 1), (9, 1), (9, 2), (9, 3), (8, 5), (9, 5), (9, 6), (8, 8), (8, 9)]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "od_matrix = np.array([[0.0, 0.7873205159876687, 0.922666259977932, 1.2679910292122152, 0.6626354487834144, 0.973592367107364, 0.41510197095048307, 0.9335222094960481],\n",
        "                          [0.7873205159876687, 0.0, 0.5843166886812995, 0.5063732864052762, 0.7636246914674278, 0.4720322695197309, 0.8230001825128103, 0.41043804400440403],\n",
        "                          [0.922666259977932, 0.5843166886812995, 0.0, 0.6549476256468965, 1.2413704270710904, 1.0562847468234913, 1.1802915951321742, 0.2121417138933306],\n",
        "                          [1.2679910292122152, 0.5063732864052762, 0.6549476256468965, 0.0, 1.1869642557305945, 1.1572393792516216, 1.170000416476491, 0.4784710672460683],\n",
        "                          [0.6626354487834144, 0.7636246914674278, 1.2413704270710904, 1.1869642557305945, 0.0, 0.6889484891197067, 0.27855153292863814, 0.980644444428607],\n",
        "                          [0.973592367107364, 0.4720322695197309, 1.0562847468234913, 1.1572393792516216, 0.6889484891197067, 0.0, 0.6060649351563587, 0.7993151802198004],\n",
        "                          [0.41510197095048307, 0.8230001825128103, 1.1802915951321742, 1.170000416476491, 0.27855153292863814, 0.6060649351563587, 0.0, 0.999638693560208],\n",
        "                          [0.9335222094960481, 0.41043804400440403, 0.2121417138933306, 0.4784710672460683, 0.980644444428607, 0.7993151802198004, 0.999638693560208, 0.0]])\n"
      ],
      "metadata": {
        "id": "5-0wG71XUPk4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "od_matrix[1]"
      ],
      "metadata": {
        "id": "3m5j6qRTU_4i",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d96980bd-1efd-4839-b0ad-d0d657a9afbb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.78732052, 0.        , 0.58431669, 0.50637329, 0.76362469,\n",
              "       0.47203227, 0.82300018, 0.41043804])"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "def make_adjacency_list(matrix):\n",
        "    \"\"\"\n",
        "    Creates an adjacency list from a matrix.\n",
        "\n",
        "    Args:\n",
        "        matrix: The matrix.\n",
        "\n",
        "    Returns:\n",
        "        The adjacency list.\n",
        "    \"\"\"\n",
        "\n",
        "    adjacency_list = []\n",
        "    for i in range(len(matrix)):\n",
        "        adjacency_list.append([])\n",
        "        for j in range(len(matrix[0])):\n",
        "            if matrix[i][j] > 0:\n",
        "                adjacency_list[i].append(j)\n",
        "    return adjacency_list\n",
        "\n",
        "matrix = np.array([[0, 1, 3], [0, 2, 2], [1, 0, 1], [1, 2, 4], [2, 0, 5], [2, 1, 2]])\n",
        "adjacency_list = make_adjacency_list(matrix)"
      ],
      "metadata": {
        "id": "lPzRBs9UVCK4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "adjacency_list"
      ],
      "metadata": {
        "id": "viObcT46V3QH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "90aeb866-3a50-47b2-ca9d-9312cd761dfa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[1, 2], [1, 2], [0, 2], [0, 1, 2], [0, 2], [0, 1, 2]]"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "make_adjacency_list(od_matrix)"
      ],
      "metadata": {
        "id": "xP5yU9qeV5T7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f48e85d-c44c-4977-cd6d-b8707cf56f72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[1, 2, 3, 4, 5, 6, 7],\n",
              " [0, 2, 3, 4, 5, 6, 7],\n",
              " [0, 1, 3, 4, 5, 6, 7],\n",
              " [0, 1, 2, 4, 5, 6, 7],\n",
              " [0, 1, 2, 3, 5, 6, 7],\n",
              " [0, 1, 2, 3, 4, 6, 7],\n",
              " [0, 1, 2, 3, 4, 5, 7],\n",
              " [0, 1, 2, 3, 4, 5, 6]]"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import networkx as nx\n",
        "def make_adjacency_list(matrix):\n",
        "    \"\"\"\n",
        "    Creates an adjacency list from a distance matrix.\n",
        "\n",
        "    Args:\n",
        "        matrix: The distance matrix.\n",
        "\n",
        "    Returns:\n",
        "        The adjacency list.\n",
        "    \"\"\"\n",
        "    adjacency_list = {}\n",
        "    for i in range(len(matrix)):\n",
        "        adjacency_list[i] = []\n",
        "        for j in range(len(matrix[0])):\n",
        "            if matrix[i][j] > 0:\n",
        "                adjacency_list[i].append((j, matrix[i][j]))\n",
        "    return adjacency_list\n",
        "\n",
        "def shortest_path(adjacency_list, source, destination):\n",
        "    \"\"\"\n",
        "    Finds the shortest path from a source node to a destination node in an adjacency list.\n",
        "\n",
        "    Args:\n",
        "        adjacency_list: The adjacency list.\n",
        "        source: The source node.\n",
        "        destination: The destination node.\n",
        "\n",
        "    Returns:\n",
        "        The shortest path.\n",
        "    \"\"\"\n",
        "\n",
        "    path = []\n",
        "    visited = set()\n",
        "    queue = [(source, 0)]\n",
        "    while queue:\n",
        "        node, distance = queue.pop(0)\n",
        "        if node == destination:\n",
        "            return path\n",
        "        if node not in visited:\n",
        "            visited.add(node)\n",
        "            path.append(node)\n",
        "            for neighbor, weight in adjacency_list[node]:\n",
        "                if neighbor not in visited:\n",
        "                    queue.append((neighbor, distance + weight))\n",
        "    return None\n",
        "\n",
        "adjacency_list = make_adjacency_list(od_matrix)\n",
        "shortest_path = shortest_path(adjacency_list, 7, 5)\n",
        "print(shortest_path)\n"
      ],
      "metadata": {
        "id": "5okQQvtPWC8z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "65029950-1f5a-4052-bd93-3f72c5ea7c4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[7, 0, 1, 2, 3, 4]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "HFdVVppAORb2"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}