Real-Time Simulation Framework with Traffic Incident Prediction: A Singapore Case Study

Muhammad Shalihin Bin Othman, Remya K. Padinjarapat, Chengxin Wang, Nimal R. Arunachalam, Gary Tan

Abstract: Traffic management centres worldwide are facing unprecedented challenges from traffic incidents, impacting congestion, delays, pollution, and costs. Congested roads during peak hours are prone to accidents, causing significant disruptions and delays. Non-recurrent congestion such as accidents, occur unexpectedly and are major contributors to delays while disruptions in public transportation lead to increased demand for alternative modes thereby causing severe congestion. Understanding the impact of these incidents on multi-modal traffic networks is crucial for developing effective response plans. This study addresses the limitations of using only historical data to run simulations in advance. The proposed architecture enables optimized simulations with real-time inputs and accurate parameter prediction for improved response planning. This work provides a foundational proof of concept for utilizing minimal real-time data for accurate simulations of dynamic traffic behaviours through a Singapore case study. Future research can focus on comparative analysis methods, optimization strategies, and incorporating pedestrian effects. These advancements will aid transport authorities in making informed decisions for infrastructure and route planning.