Modeling Traffic Congestion in Developing Countries using Google Maps Data

TL;DR

This paper introduces a cost-effective method for collecting traffic data in developing countries using Google Maps, and demonstrates its effectiveness in predicting congestion with simple models, highlighting differences between weekdays and weekends.

Contribution

It presents a novel, low-cost traffic data collection approach from Google Maps and validates its usefulness for congestion forecasting in developing countries.

Findings

Google Maps traffic layer can be effectively used for traffic data collection.

Simple models like HA, SVR, and ARIMA can forecast congestion accurately.

Traffic patterns differ significantly between weekdays and weekends.

Abstract

Traffic congestion research is on the rise, thanks to urbanization, economic growth, and industrialization. Developed countries invest a lot of research money in collecting traffic data using Radio Frequency Identification (RFID), loop detectors, speed sensors, high-end traffic light, and GPS. However, these processes are expensive, infeasible, and non-scalable for developing countries with numerous non-motorized vehicles, proliferated ride-sharing services, and frequent pedestrians. This paper proposes a novel approach to collect traffic data from Google Map's traffic layer with minimal cost. We have implemented widely used models such as Historical Averages (HA), Support Vector Regression (SVR), Support Vector Regression with Graph (SVR-Graph), Auto-Regressive Integrated Moving Average (ARIMA) to show the efficacy of the collected traffic data in forecasting future congestion. We show that even with these simple models, we could predict the traffic congestion ahead of time. We also demonstrate that the traffic patterns are significantly different between weekdays and weekends.