Yerevan Car Parking Patterns Analysis

Warning: Data folder is not provided due to privaet sensitive data belonging to Perigon AI

Welcome to the Yerevan Parking Pattern Analysis project!

This research investigates the parking patterns in various regions of Yerevan, the capital city of Armenia, leveraging data collected from the GPS sensors of the population's smartphones. The methodology involves the collection and analysis of anonymized GPS data obtained from smartphone applications used by residents and visitors. The research focuses on identifying and characterizing parking patterns, considering factors such as peak parking hours, average durations, and preferred locations.

Objectives and Significance

The primary objective is to unravel the mysteries surrounding parking behaviors, providing indispensable insights crucial for effective city planning and the optimization of parking infrastructure.This research aims to understand parking behavior within defined parking areas using only raw GPS data collected over time. Our objective is to discover parking behavior cues and irregularities that policymakers and urban planners might use to adjust and optimize the management of parking strategies. We are interested in determining the time of the day that parking is most used, how long parking events are on average, and how outside factors, such as the day of the week, the time of day, or even special occasions, affect parking behavior. Therefore, the main goal of this research is not to locate parking spots but to understand why, when, and how people are parking in specified areas.

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The analysis is performed based on the raw GPS mobile data collected from 2019 to 2022, covering the area of Yerevan, Armenia. We are interested in the red line parking lots in the Center and Arabkir regions of Yerevan. It is remarkable to note that data distribution may differ yearly due to changes in user behavior, device exposure, and data-gathering methods. Consequently, this provides both opportunities and challenges when processing the data to understand dynamics over time. However, our data is substantial enough to draw some conclusions about the dynamics around the red lines. However, before drawing conclusions, the raw GPS data has to undergo preprocessing stages and must be processed largely by applying mathematical modeling to become suitable for analysis.

Paper Overview

Inside the repository, at the root, you will find a detailed paper that elaborates on the methodologies, data processing techniques, analysis results, and key findings of this research. The paper provides an in-depth look into how the GPS data was collected, the preprocessing steps taken to ensure data quality, and the mathematical models applied to derive meaningful insights from the data. Additionally, it discusses the implications of the findings for urban planning and policy-making in Yerevan, providing recommendations based on the observed parking patterns. This document is essential for anyone interested in the technical details and the comprehensive results of the study.