Traffic Congestion Analysis and Route Optimization in an Urban Road Network Using Geographic Information System (GIS)

Abstract

Traffic congestion is a critical challenge in urban transportation systems leading to longer travel times, higher fuel consumption, and increased environmental pollution. This study examines traffic congestion along the major urban road corridor in Thiruvananthapuram, Kerala, India, by Geographic Information Systems (GIS). Travel time data were collected from Google Maps under varying conditions, including weekdays, weekends, holidays, festivals, in different weather conditions, and special events, to capture temporal and situational variations in traffic flow. The collected data were analyzed to identify congestion zones based on speed across different road segments. QGIS software was utilized to perform Optimal Route Analysis by Contraction Hierarchies (CH) within the study area using the OpenRouteService (ORS) Directions API with the driving car profile, based on OpenStreetMap road network data. The study demonstrates that GIS provides an effective framework for traffic congestion analysis, route optimization, supporting improved urban traffic management and planning.

Introduction

The text presents a GIS-based study of traffic congestion and route optimization along a major urban corridor from Chavadimukku Junction to Thampanoor in Thiruvananthapuram, Kerala. Rapid urbanization, increased vehicle ownership, and limited road capacity have led to severe congestion, causing delays, fuel wastage, pollution, and reduced urban mobility. Geographic Information Systems (GIS) are highlighted as an effective tool for integrating spatial and non-spatial data, analyzing traffic patterns, and visualizing congestion hotspots.

The literature review shows that previous studies successfully used Google Maps travel time data, OpenStreetMap (OSM), OpenRouteService (ORS), and GIS-based network analysis to identify congestion zones and optimal routes in cities such as Bhopal, Coimbatore, Attingal, Kolkata, and Indore. Speed and travel time are emphasized as key indicators of congestion, while travel-time-based routing is shown to be more effective than distance-based routing in congested urban networks.

The selected study corridor, approximately 10 km long, is a critical urban route with high traffic demand. Using Google Earth Pro, the road network was digitized and divided into 13 segments. Travel time data were collected from Google Maps for different days, time periods, weather conditions, and special events. Segment speeds were calculated and classified into congestion levels based on Google Maps color codes.

Results indicate that all road segments experienced severe congestion, with average speeds below 40 kmph, confirming persistent traffic delays throughout the corridor. Route optimization was performed using QGIS and OpenRouteService. The fastest route, based on minimum travel time, covered 10.391 km in 14.22 minutes, while the shortest route, based on minimum distance, was 9.973 km but took 20.4 minutes, demonstrating that the shortest path is not necessarily the fastest in congested urban conditions.

Overall, the study demonstrates that GIS-based traffic analysis and route optimization are effective for identifying congestion patterns and improving urban traffic management, highlighting the importance of travel-time-based routing for efficient mobility planning.

Conclusion

This study confirms the effectiveness of a GIS based approach for congestion analysis and route optimization along the corridor. The results indicate widespread congestion, with average speeds below 40 kmph across all road segments, and highlight critical congestion hotspots affecting travel efficiency. Optimal Route analysis using OpenRouteService shows clear differences between shortest and fastest routes where fastest route often reduce travel time by avoiding congested sections. These findings underscore the value of integrating congestion assessment with route optimization to support efficient urban traffic management and transportation planning.

References

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Copyright

Copyright © 2026 S S Nithya, Priya R. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.