Scenario-driven of system dynamics prediction trend for traffic congestion in urban area

• Published in: AIP Conference Proceedings
• Main Author: Abidin N.Z.; Alwi A.; Ahmarofi A.; Karim K.N.
• Format: Conference paper
• Language: English
• Published: American Institute of Physics Inc. 2023
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179827919&doi=10.1063%2f5.0177336&partnerID=40&md5=cdc373f5d3f8991ffc17a5cf55b31bcd

Accurate and reliable traffic congestion prediction plays an important role by providing information that helps to make better route choices and road connectivity. Such prediction can provide an early warning that indicates potential problems. Existing traffic congestion prediction using forecasting models are fragmented, especially regarding the consideration of interdependency between multidimensional factors. To deal with these limitations of prediction, the use of system dynamics (SD) model is capable to demonstrate the dynamic of simulated relationships among interdependent variables and forecasting future traffic congestion trends during a particular period. Specifically, this paper aims to predict the trends of congestion index and mode share in the urban area of Kuala Lumpur using SD modeling. SD approach highlighted the prediction of future congestion trends are derived from the behavioural construct of the supply and demand of transportation system compared to the statistical forecasting methods which are more data driven. Thus, our developed congestion model consists of four interrelated components namely; (1) travel demand, (2) mode share, (3) traffic supply and (4) traffic system that are linked in a holistic model of picture. Results show that the trends of traffic congestion index and mode share of private transportation in Kuala Lumpur will continue to grow if no countermeasure is taken. In a conclusion, this research advances through the creation of a model that provides more holistic and interdependent to represent the real scenario of traffic systems for better prediction of congestion in the urban area. © 2023 Author(s).