Segment-Based Vehicular Congestion Detection Methods Using Vehicle ID and Loss of Expected Time of Arrival

Abstract: Increasing number of vehicles and rapid urbanization are the significant causes of road traffic congestion. Road traffic congestion is the main issue facing world cities today. Congestion control and mitigation are necessary to mitigate the negative impacts of road traffic congestion, such as delays and increased fuel consumption, among others. There are many congestion detection methods published in the literature; some of these methods, such as the speed threshold, use a single congestion detection metric. Using a single parameter for traffic congestion detection might produce false and inaccurate results. Furthermore, many congestion detection techniques fall short in describing traffic congestion from the user's perspective and vision. To address this, this study develops a segment-based congestion detection method that uses vehicle ID and loss of expected time of arrival. The ID-based method considers both vehicle speed and density, whereas the loss of expected time of arrival focuses on the time loss. These methods are segment-based, where roads are divided into segments using vehicle trajectories. Using a speed threshold of 8.33 m/s, the road is segmented into segments of 8.33 m, 16.66 m, and 24.99 m in length. Vehicle speed and density are monitored using vehicle identification numbers (VINs). Experimental results reveal that the speed threshold and the Microscopic Congestion Detection Protocol recorded false congestion detection. The proposed ID-based congestion detection method is capable of identifying false congestion and accurately detecting real congestion. Moreover, the loss of expected time of arrival shows a promising result in terms of identifying congestion based on motorists’ feelings.

Mustapha Abubakar Ahmed and Azizul Rahman Mohd Shariff. “Segment-Based Vehicular Congestion Detection Methods Using Vehicle ID and Loss of Expected Time of Arrival”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.8 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0160834

@article{Ahmed2025,
title = {Segment-Based Vehicular Congestion Detection Methods Using Vehicle ID and Loss of Expected Time of Arrival},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160834},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160834},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {8},
author = {Mustapha Abubakar Ahmed and Azizul Rahman Mohd Shariff}
}