Road Traffic Congestion Prediction Using Discrete Event Simulation And Regression Machine Learning Models

Singgih, Ivan Kristianto and Singgih, Moses Laksono and Nathaniel, Daniel (2025) Road Traffic Congestion Prediction Using Discrete Event Simulation And Regression Machine Learning Models. In: Proceedings of the 2025 Winter Simulation Conference, 07-10 December 2025, Seattle, WA, USA.

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Abstract

Road traffic management enters a new era with the automatic collection and analysis of big data. The traffic data could be collected continuously using various IoT sensors (light, video, etc.) and stored in the cloud. The collected data are then analyzed within a short time to make traffic control decisions, e.g., traffic redirection, traffic light duration change, and vehicle route recommendation. This study proposes (1) a traffic simulation considering a road network with several traffic lights and (2) regression machine learning models to understand the behavior of the vehicles based on the real-time characteristics of the traffic. The numerical experiment results show that (1) the best models are OrthogonalMatchingPursuitCV and the HuberRegressor, and (2) the road network behavior is affected by the condition of all intersections rather than only certain intersections or surrounding road segments.

Item Type:	Conference or Workshop Item (Paper)
Subjects:	Q Science > Q Science (General)
Divisions:	Faculty of Engineering > Department of Industrial Engineering
Depositing User:	IVAN KRISTIANTO SINGGIH
Date Deposited:	14 Apr 2026 02:00
Last Modified:	14 Apr 2026 02:00
URI:	http://repository.ubaya.ac.id/id/eprint/50532

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