Leveraging Radar Technology for Traffic Flow Optimization: A Case Study on the Intra-Urban Expressway in Malaysia

Abstract

Intra-urban expressways in the Klang Valley, Malaysia, experience severe congestion, especially during peak hours. This study examines the relationship between traffic density, flow, and speed using advanced radar technology data collected via Speedlane Pro^TM Counter Classifier. The analysis revealed an inverse correlation between density and speed, with congestion reducing traffic flow beyond an optimal density point. Additionally, southbound lanes experienced more congestion and speed variations than northbound lanes due to access ramps and peak-hour demand differences. These findings emphasize the applicability of fundamental traffic flow models in intra-urban urban expressway settings and highlight the need for dynamic lane management. The study provides valuable insights for policymakers, transport planners, and traffic engineers to develop direction-specific traffic strategies, improve congestion management, and enhance overall intra-urban expressway efficiency.