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

Ivan Kristianto Singgih; Moses Laksono Singgih; Daniel Nathaniel

doi:10.1109/WSC68292.2025.11338865

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

Ivan Kristianto Singgih
, Moses Laksono Singgih
, Daniel Nathaniel

University of Surabaya
Indonesia Artificial Intelligence Society
Kolaborasi Riset dan Inovasi Industri Kecerdasan Artifisial (KORIKA)
The Indonesian Researcher Association in South Korea (APIK)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	2025 Winter Simulation Conference, WSC 2025
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2730-2739
Number of pages	10
ISBN (Electronic)	9798331587260
DOIs	https://doi.org/10.1109/WSC68292.2025.11338865
Publication status	Published - 2025
Event	2025 Winter Simulation Conference, WSC 2025 - Seattle, United States Duration: 7 Dec 2025 → 10 Dec 2025

Publication series

Name	Proceedings - Winter Simulation Conference
ISSN (Print)	0891-7736

Conference

Conference	2025 Winter Simulation Conference, WSC 2025
Country/Territory	United States
City	Seattle
Period	7/12/25 → 10/12/25

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

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10.1109/WSC68292.2025.11338865

Cite this

Singgih, I. K., Singgih, M. L., & Nathaniel, D. (2025). Road Traffic Congestion Prediction Using Discrete Event Simulation and Regression Machine Learning Models. In 2025 Winter Simulation Conference, WSC 2025 (pp. 2730-2739). (Proceedings - Winter Simulation Conference). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WSC68292.2025.11338865

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title = "Road Traffic Congestion Prediction Using Discrete Event Simulation and Regression Machine Learning Models",

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.",

author = "Singgih, \{Ivan Kristianto\} and Singgih, \{Moses Laksono\} and Daniel Nathaniel",

note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 Winter Simulation Conference, WSC 2025 ; Conference date: 07-12-2025 Through 10-12-2025",

year = "2025",

doi = "10.1109/WSC68292.2025.11338865",

language = "English",

series = "Proceedings - Winter Simulation Conference",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "2025 Winter Simulation Conference, WSC 2025",

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Singgih, IK, Singgih, ML & Nathaniel, D 2025, Road Traffic Congestion Prediction Using Discrete Event Simulation and Regression Machine Learning Models. in 2025 Winter Simulation Conference, WSC 2025. Proceedings - Winter Simulation Conference, Institute of Electrical and Electronics Engineers Inc., pp. 2730-2739, 2025 Winter Simulation Conference, WSC 2025, Seattle, United States, 7/12/25. https://doi.org/10.1109/WSC68292.2025.11338865

Road Traffic Congestion Prediction Using Discrete Event Simulation and Regression Machine Learning Models. / Singgih, Ivan Kristianto; Singgih, Moses Laksono; Nathaniel, Daniel.
2025 Winter Simulation Conference, WSC 2025. Institute of Electrical and Electronics Engineers Inc., 2025. p. 2730-2739 (Proceedings - Winter Simulation Conference).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Road Traffic Congestion Prediction Using Discrete Event Simulation and Regression Machine Learning Models

AU - Singgih, Ivan Kristianto

AU - Singgih, Moses Laksono

AU - Nathaniel, Daniel

PY - 2025

Y1 - 2025

N2 - 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.

AB - 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.

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DO - 10.1109/WSC68292.2025.11338865

M3 - Conference contribution

AN - SCOPUS:105033155351

T3 - Proceedings - Winter Simulation Conference

SP - 2730

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BT - 2025 Winter Simulation Conference, WSC 2025

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2025 Winter Simulation Conference, WSC 2025

Y2 - 7 December 2025 through 10 December 2025

ER -

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

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