An improved pheromone-based vehicle rerouting system to reduce traffic congestion

Mun Chon Ho, Joanne Mun Yee Lim, Kian Lun Soon, Chun Yong Chong

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    8 Citations (Scopus)


    The growing number of vehicles necessitates the implementation of effective vehicle rerouting systems. Designing an effective vehicle rerouting system is challenging due to the dynamic nature of vehicular network. In this paper, a Proactive Travel-time based Pheromone Rerouting (PTPR) system is proposed. First, PTPR system predicts future congestion level using travel time and vehicle density information. Then, vehicles are distributed to multiple paths to balance the traffic load. Different from the existing pheromone-based rerouting systems, each ant (vehicle) in PTPR system can deposit its pheromone on multiple road segments away, instead of its direct adjacent road segment, based on its route. This new pheromone model aims to improve the performance of PTPR system. In addition, a localized dynamic k-shortest path (LDkSP) algorithm is proposed to reduce computational effort of PTPR system. Experiments were conducted on two different areas (i.e. suburban and urban) using Simulation of Urban Mobility (SUMO). Results show that the proposed PTPR system outperforms the existing rerouting system by reducing mean travel time, fuel consumption, and increasing number of arrive vehicles by 8.2%, 2%, and 15.1% respectively in Woodlands (suburban) and 28.7%, 17.2%, and 29.5% respectively in Novena (urban). The computation time used to reroute each vehicle is also reduced by 68.3% and 92.1% in suburban and urban area respectively using the proposed LDkSP. Finally, experiments over various usage rates and estimation errors showed that the proposed PTPR system is robust to usage rates ranging from 80% to 100% and is able to function properly with estimation error of up to 20%.

    Original languageEnglish
    Article number105702
    Number of pages13
    JournalApplied Soft Computing
    Publication statusPublished - Nov 2019


    • Long short-term memory
    • Pheromone
    • Traffic congestion
    • Traffic prediction
    • Travel time
    • Vehicle routing

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