Bi-objective optimization models for mitigating traffic congestion in urban road networks

Abstract

Traffic congestion in road transportation networks is a persistent problem in major metropolitan cities around the world. In this context, this paper deals with exploiting underutilized road capacities in a network to lower the congestion on overutilized links while simultaneously satisfying the system optimal flow assignment for sustainable transportation. Four congestion mitigation strategies are identified based on deviation and relative deviation of link volume from the corresponding capacity. Consequently, four bi-objective mathematical programming optimal flow distribution (OFD) models are proposed. The case study results demonstrate that all the proposed models improve system performance and reduce congestion on high volume links by shifting flows to low volume-to-capacity links compared to UE and SO models. Among the models, the system optimality with minimal sum and maximum absolute relative-deviation models (SO-SAR and SO-MAR) showed superior results for different performance measures. The SO-SAR model yielded 50% and 30% fewer links at higher link utilization factors than UE and SO models, respectively. Also, it showed more than 25% improvement in path travel times compared to UE travel time for about 100 paths and resulted in the least network congestion index of 1.04 compared to the other OFD and UE models. Conversely, the SO-MAR model yielded the least total distance and total system travel time, resulting in lower fuel consumption and emissions, thus contributing to sustainability. The proposed models contribute towards efficient transportation infrastructure management and will be of interest to transportation planners and traffic managers.