Real-Time Urban Congestion Monitoring in Jeddah, Saudi Arabia, Using the Google Maps API: A Data-Driven Framework for Middle Eastern Cities

Ghada Ragheb Elnaggar (), Shireen Al-Hourani and Rimal Abutaha
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Ghada Ragheb Elnaggar: Department of Industrial and Systems Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Shireen Al-Hourani: Department of Quantitative Studies, University Canada West, Vancouver, BC V6Z 0E5, Canada
Rimal Abutaha: Industrial Engineering Department, King Abdulaziz University, Jeddah 21589, Saudi Arabia

Sustainability, 2025, vol. 17, issue 18, 1-32

Abstract: Rapid urban growth in Middle Eastern cities has intensified congestion-related challenges, yet traffic data-based decision making remains limited. This study leverages crowd-sourced travel time data from the Google Maps API to evaluate temporal and spatial patterns of congestion across multiple strategic routes in Jeddah, Saudi Arabia, a coastal metropolis with a complex road network characterized by narrow, high-traffic corridors and limited public transit. A real-time Congestion Index quantifies traffic flow, incorporating free-flow speed benchmarking, dynamic profiling, and temporal classification to pinpoint congestion hotspots. The analysis identifies consistent peak congestion windows and route-specific delays that are critical for travel behavior modeling. In addition to congestion monitoring, the framework contributes to urban sustainability by supporting reductions in traffic-related emissions, enhancing mobility equity, and improving economic efficiency through data-driven transport management. To our knowledge, this is the first study to systematically use the validated, real-time Google Maps API to quantify route-specific congestion in a Middle Eastern urban context. The approach provides a scalable and replicable framework for evaluating urban mobility in other data-sparse cities, especially in contexts where traditional traffic sensors or GPS tracking are unavailable. The findings support evidence-based transport policy and demonstrate the utility of publicly accessible traffic data for smart city integration, real-time traffic monitoring, and assisting transport authorities in enhancing urban mobility.

Keywords: traffic management; congestion index; urban planning; Google Maps API (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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