Mitigating the Traffic Congestion in the Urban Area Using the Integration of System Dynamics and Genetic Algorithm Approaches

Abstract

Urban traffic congestion has worsened in many countries today. This problem is getting worse for most urban areas globally, including Kuala Lumpur, Malaysia’s capital city. It is predicted that the demand for mobility will increase tremendously as the city grows at a faster rate in terms of population, infrastructure, and economic activities in the next ten years. This paper aims to develop an integration of system dynamics (SD) with genetic algorithm (GA) approaches known as SD-GA model aiming to optimise the congestion index and mode share of transportation values in the year 2030 in Malaysia. The developed SD-GA model results show that the best level of congestion index is 0.41367 while the percentage of mode share is 78.41% in 2030. From all the tested travel demand variables, bus fare subsidies and bus route expansion rate emerged as the two highest increment percentages in achieving the best minimal value of mode share and congestion index. From the managerial perspective, this research contributes to the transportation industry by suggesting strategies to mitigate the high congestion index and optimise mode share in Kuala Lumpur.