Using crowd-sourced traffic data and open-source tools for urban congestion analysis

Traffic congestion in urban areas poses significant challenges to city dwellers and consultants advising government. This study explores innovative methods to monitor and control traffic congestion, focusing on Al Ain city in the United Arab Emirates. Using the R Programming language and harnessing crowdsourced traffic information from HERE and Google Maps, the research delves into spatial data analysis. The methodology employed in this study builds on the previously applied congestion modeling methods for cities like Windsor, Toronto, and New York. The study focuses on Al Ain, addressing the scarcity of crowdsourced information-based congestion modeling research in the Middle East. The study details how to obtain and deploy crowdsourced traffic data, speed and jam factors, for a comprehensive visualization of the urban traffic congestion. For example, in the case of Al Ain, analysis showed an average traffic speed of 43 km per hour in Al Ain, where infrastructure could otherwise allow an average traffic speed of up to 51 km per hour under free flow conditions. The study findings highlight how traffic conditions, rather than speed limits, cause traffic flow disruptions in the city, which can inform traffic regulations. The study's high-confidence real-time data emphasizes the reliability of crowdsourced traffic flow data. This research demonstrates the applicability of open-source traffic information for congestion modeling in the UAE, and establishes a replicable methodology for other urban areas worldwide, contributing significantly to the modeling methods.