Developing A Causal Loop Diagram to Reduce Road Congestion in Kuala Lumpur, Malaysia: A System Dynamics Approach

Road congestion has long been an issue plaguing urban areas, including Kuala Lumpur, the capital city of Malaysia. Accordingly, one of the strategies to reduce road congestion, as suggested by the experts, is road pricing, which is successfully practiced in several big cities around the world. Road pricing is a direct charge levied against drivers to reduce the number of private vehicles during peak hours. However, policymakers remain doubtful of its success in effectively reducing road congestion in Kuala Lumpur. In this regard, this study aims to identify the causes and effects related to road pricing implementation to reduce road congestion. To fulfil that, a causal loop diagram (CLD) model based on the system dynamics (SD) approach is developed by considering four significant causal relationships, namely the degree of road congestion, driving attraction, public transport, and road pricing charge. In developing CLD, a curved line with an arrow is created to represent the causal relationship that links one variable to another variable. Every link in the diagram must be labelled with polarity, whether positive or negative. Subsequently, the feedback loop is indicated in two types: either the reinforcing loop or the feedback loop based on the number of negative polarities. As a result, road pricing, road congestion, and driving attraction turn into a reinforcing loop where the changes in these loops could drastically affect the whole system. From the managerial perspective, this research helps highway stakeholders in Malaysia make better decisions by having a holistic view of road pricing implementation for a better metropolitan lifestyle.

Road pricing; Causal loop diagram (CLD)

1. Abidin, N. Z., Karim, K. N., Rahman, R. A., & Alwi, A. (2022). Mitigating the Traffic Congestion in the Urban Area Using the Integration of System Dynamics and Genetic Algorithm Approaches. Civil Engineering and Architecture. [Google Scholar] [Crossref]

2. Ahmarofi, A. A., Kamaruzzaman, Z. A., Rahman, H. A., Masri, K. A., Rahim, N. A., Shariff, S. S. R., ... & Ahmad, A. B. (2021, December). Development of A Computer Simulation on Road Pricing Strategy to Reduce Congestion and Carbon Dioxide Emission: A System Dynamics Approach. In 2021 6th IEEE International Conference on Recent Advances and Innovations in Engineering (ICRAIE) (Vol. 6, pp. 1-5). IEEE. [Google Scholar] [Crossref]

3. Ahmarofi, A. A., Zainal Abidin, N., & Mahadzir, N. H. (2022). Simulating the Cycle Time Based on a Manpower Performance Through a System Dynamics Model: A Case Company in the Automotive Sector. In Human-Centered Technology for a Better Tomorrow (pp. 515-523). Springer, Singapore. [Google Scholar] [Crossref]

4. Asian Transport Observatory. (2023). Kuala Lumpur transport sector profile. Retrieved October 30, 2025, from https://asiantransportobservatory.org/documents/266/Kuala_Lumpur_transport_sector_profile.pdf [Google Scholar] [Crossref]

5. Fontoura, W. B., Radzicki, M. J., & Ribeiro, G. M. (2024). Using system dynamics to understand long-term impact of new mobility services and sustainable mobility policies: an analysis pre-and post-COVID-19 pandemic in Rio de Janeiro, Brazil. Transportation Letters, 16(7), 751-763. [Google Scholar] [Crossref]

6. Jia, S., & Zhu, W. (2025). Effects of carbon tax policy on vehicle pollution control and carbon reduction based on the availability heuristic and system dynamics. Sustainable Cities and Society, 118, 105990. [Google Scholar] [Crossref]

7. Jia, S., Yan, G., Shen, A., & Zheng, J. (2017). A System Dynamics Model for Determining the Traffic Congestion Charges and Subsidies. Arabian Journal for Science and Engineering, 42(12), 5291–5304. https://doi.org/10.1007/s13369-017-2637-5 [Google Scholar] [Crossref]

8. Lessan, J., & Fu, L. (2022). Credit-and permit-based travel demand management state-of-the-art methodological advances. Transportmetrica A: Transport Science, 18(1), 5-28. [Google Scholar] [Crossref]

9. Massachusetts Institute of Technology. (n.d.). Kuala Lumpur. Malaysia Sustainable Cities Program. Retrieved October 30, 2025, from https://malaysiacities.mit.edu/kualalumpur [Google Scholar] [Crossref]

10. Moyo, T., Mbatha, S., Aderibigbe, O. O., Gumbo, T., & Musonda, I. (2022). Assessing spatial variations of traffic congestion using traffic index data in a developing city: lessons from Johannesburg, South Africa. Sustainability, 14(14), 8809. [Google Scholar] [Crossref]

11. New Straits Times. (2023, December 7). 36.3 million vehicles in Malaysia. Retrieved October 30, 2025, from https://www.nst.com.my/news/nation/2023/12/987062/363-million-vehicles-malaysia [Google Scholar] [Crossref]

12. Nunes, S. A., Ferreira, F. A., Govindan, K., & Pereira, L. F. (2021). “Cities go smart!”: A system dynamics-based approach to smart city conceptualization. Journal of Cleaner Production, 313, 127683. [Google Scholar] [Crossref]

13. Rotaris, L., Danielis, R., Marcucci, E., & Massiani, J. (2010). The urban road pricing scheme to curb pollution in Milan, Italy: Description, impacts, and preliminary cost-benefit analysis assessment. Transportation Research Part A, 44(5), 359–375. https://doi.org/10.1016/j.tra.2010.03.008 [Google Scholar] [Crossref]

14. Statistik Pengangkutan Jalan 2020, Ministry of Transportation Malaysia 2020. Retrieved from https://www.mot.gov.my/en/Statistik%20Tahunan%20Pengangkutan/Transport%20Statistics%20Malaysia%202020.pdf [Google Scholar] [Crossref]

15. Walch, M., Neubauer, M., Schildorfer, W., & Schirrer, A. (2024). Modelling interrelations between C-ITS impact categories: a system-dynamics approach using causal loop diagrams. European Transport Research Review, 16(1), 60. [Google Scholar] [Crossref]

16. Zefreh, M. M., Esztergar-Kiss, D., & Torok, A. (2021). Implications of different road pricing schemes in urban areas: A case study for Budapest. Proceedings of the Institution of Civil Engineers: Transport, 174(7), 407–418. https://doi.org/10.1680/jtran.19.00094 [Google Scholar] [Crossref]

• The Indirect Effect of Liquidity and Activity on Company Value with Profitability as an Intervening Variable
• Effect of Financial Skills, Knowledge, and Attitude on The Financial Behaviour of Clergy
• A Decade of Review: Trends in Budget Execution and Financial Performance of Development Projects in Tanzania (2014/15-2023/24)
• The Influence of Pre-Project Planning on the Budget Absorption Rate of Public Funded Infrastructure Projects in Kenya a Comparative Case Study of Narok, Migori, and Kisii County Government Projects
• Assessment of Factors Influencing Digital Transformation in Hotels’ Facility Management in Abuja Metropolis, Nigeria