This paper aims to address the urban mobility and traffic congestion problem under environmental dynamics to improve mobility and reduce traffic congestion using system dynamics (SD) simulation and scenarios.
SD simulation was used to analyze urban mobility and traffic congestion. Data were collected from the Transportation Department of Surabaya City. Several scenarios to improve urban mobility and reduce traffic congestion were developed by modifying the structures and parameters of the model.
Several factors influence urban mobility, including modal split, trip frequency, delay performance and the ratio of public transport supply and demand. Urban mobility, daily traffic and road capacity are some factors that affect traffic congestion. Scenarios can be designed based on the assumptions of the proposed strategy.
The study was conducted at Surabaya City, East Java, Indonesia, which is the fourth most-congested city in the world.
By implementing several strategies (mass rapid transit and bus rapid transit development and public transport delay reduction), mobility performance is projected to be improved by 70.34-92.96%. With this increased mobility, traffic congestion is projected to decline by 52.5-65.8%.
The novel contributions of this research are: formulating relationships between several variables; modeling dynamic behavior of urban mobility and traffic congestion; and building scenario models to improve mobility and reduce traffic congestion in Surabaya. With the increase in urban mobility and the decrease in average daily traffic, traffic congestion could be reduced by a minimum of 57.6% and a maximum of 69%.
This work was supported by ITS (Institut Teknologi Sepuluh Nopember) Research Center, Enterprise Systems Laboratory, City Transportation Office of Surabaya and Faculty of Information and Communication Technology of ITS.
Suryani, E., Hendrawan, R., Adipraja, P., Wibisono, A. and Dewi, L. (2020), "Urban mobility modeling to reduce traffic congestion in Surabaya: a system dynamics framework", Journal of Modelling in Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JM2-03-2019-0055Download as .RIS
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