Evaluation of traffic congestion mitigation techniques using an entropy-TOPSIS integrated method

Hussain, Daniyal; Jamal, Arshad; Farooq, Asim; Almoshaogeh, Meshal; Alharbi, Fawaz; Farooq, Danish · 2026 · Crossref

DOI: 10.1038/s41598-026-35814-w

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Summary

This study addresses the severe traffic congestion in Peshawar, Pakistan, a city experiencing rapid population growth and a shift toward road-based freight transport. The research aims to identify critical congestion hotspots along a primary 16 km corridor and evaluate mitigation strategies using a novel integrated approach. The motivation stems from the lack of GIS-based Important Zones Analysis for ranking congestion in Pakistani urban contexts and the need for data-driven frameworks to improve sustainable urban mobility. The methodology combines Geographic Information Systems (GIS) with Multi-Criteria Decision-Making (MCDM) techniques. First, ArcGIS was used to perform an Important Zones Analysis, assigning weights to factors such as built-up areas, educational facilities, and markets to identify high-priority traffic zones. Within the selected corridor, four major hotspots were identified through field observations and digital analysis. Traffic performance was evaluated using volume-to-capacity ratios, speed studies, and Passenger Car Unit (PCU) calculations based on Highway Capacity Manual standards. To rank the severity of congestion, the study integrated the Shannon Entropy method, which objectively determines criterion weights, with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). This integration allowed for the ranking of locations based on multiple traffic parameters without subjective expert bias. The results indicate that all four studied locations—Amin Hotel, PC Hotel, Army Stadium, and Jalil Kabab House—exceeded their capacity limits during peak hours. PCU analysis showed significant overloads, with Amin Hotel reaching 8,650 PCU/hr against a limit of 6,000. The integrated Entropy-TOPSIS analysis ranked Amin Hotel as the most congested location (Rank 1, closeness coefficient $C_i = 0.85$), followed by PC Hotel, Army Stadium, and Jalil Kabab House. Key causes identified include BRT corridor narrowing, inadequate parking, poor lane markings, improperly placed police checkpoints, and roadside encroachments. The study also highlighted that motorcycles and cars dominate traffic volume, with peak congestion occurring between 9:00 AM and 12:00 PM. The significance of this work lies in its introduction of a replicable, GIS-integrated MCDM framework for traffic assessment in developing countries. By providing a benchmark for ranking congestion severity, the findings support targeted policy interventions and infrastructure improvements. The study demonstrates that combining spatial analysis with objective decision-making tools can effectively guide urban mobility planning, offering a model for other cities facing similar challenges of rapid urbanization and inadequate infrastructure.

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• traffic density