INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue X October 2025  
Developing A Causal Loop Diagram to Reduce Road Congestion in  
Kuala Lumpur, Malaysia: A System Dynamics Approach  
Masriah Mansur1, Ahmad Afif Ahmarofi 2*, Zetty Ain Kamaruzzaman3  
1,3Faculty of Industrial Management, Universiti Malaysia Pahang Al-Sultan Abdullah, 26300 Gambang,  
Pahang, Malaysia.  
2*Integrated Simulation & Visualization (i-SiVi) Group, Faculty of Computer and Mathematical  
Sciences, Universiti Teknologi MARA (UiTM) Kedah, 08400 Merbok Kedah, Malaysia.  
DOI: https://dx.doi.org/10.47772/IJRISS.2025.910000617  
Received: 28 October 2025; Accepted: 04 November 2025; Published: 19 November 2025  
ABSTRACT  
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.  
Keywords—Road pricing; Causal loop diagram (CLD); System dynamics; Traffic congestion  
INTRODUCTION  
Traffic congestion refers to the condition when the number of trips made by vehicles exceeds the existing  
roadway facilities (Ahmarofi et al., 2021). This situation overwhelmed Kuala Lumpur, the capital city of  
Malaysia, for a few decades. It is recorded that the number of vehicles on the road in Kuala Lumpur has reached  
almost seven million in 2020, which constitutes almost 90 percent of the private vehicles’ trips per day (Ministry  
of Transport Malaysia, 2021). Furthermore, Kuala Lumpur experiences heavy traffic congestion, with vehicle  
ownership reaching nearly 900 per 1,000 residents and millions of vehicles entering the city daily (Asian  
Transport Observatory, 2023; Massachusetts Institute of Technology, n.d.; New Straits Times, 2023).  
Consequently, this phenomenon increases the congestion index in the city as well. The congestion index is the  
ratio of the number of vehicles on the road divided by the number of vehicle capacity on the road (Moyo, Mbatha,  
Aderibigbe, Gumbo, & Musonda, 2022). The bigger the ratio of the congestion index, i.e., approaches 1.00, the  
more congested the road condition would be.  
To attain a sustainable transportation system and congestion index reduction in Kuala Lumpur, the government  
has set a vision to achieve a mode share ratio of 75:25 for private vehicles to public transportation (Abidin,  
Karim, Rahman, & Alwi, 2022). To fulfil that, a road pricing strategy is proposed to be implemented, as it is one  
of the most effective solutions to tackle the congestion issue.  
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Road pricing is the term used to describe a direct charge levied against drivers who use the road network during  
peak hours. In urban areas, the goal of road pricing is to reduce the number of private vehicles that circulate on  
the roads during peak hours.  
Furthermore, road pricing is one of the Travel Demand Management (TDM) strategies that have been  
reformulated in response to the rapid recovery of traffic from 1998 until 2000 during the national economic crisis  
(Lessan & Fu, 2022). However, although road pricing has been proven to reduce traffic congestion effectively  
in cities such as Singapore, Stockholm, and New York, to name a few, policymakers remain sceptical of its  
success rate if implemented in Kuala Lumpur.  
In this regard, the objective of this study is to develop a dynamic road pricing model for reducing road congestion  
in the city. To fulfil the objective, the system dynamics (SD) method is considered in this research for its  
capability to view a complex system holistically in terms of cause and effect relationships, i.e., causal  
relationships. A causal loop diagram (CLD) based on SD principles is developed to investigate how one variable  
affects another variable. Moreover, CLD serves as a working theory model to investigate the causal relationship  
among the identified variables (Ahmarofi et al., 2021).  
In the subsequent section, previous works are reviewed to highlight the implementation of road pricing and the  
SD method in reducing road congestion. After that, the development of CLD is presented in the following  
sections. The results from the developed CLD are discussed in the subsequent section, followed by the  
conclusion and future work.  
LITERATURE REVIEW  
Traffic congestion has risen dramatically as early as the 1920s. This scenario stimulated the curiosity of scholars  
to address this issue facing urban areas. Accordingly, economists such as Pigou in 1920 and Knight in 1924  
proposed road pricing as an effective solution for reducing congested road traffic (Zefreh & Torok, 2021). They  
highlighted that the implementation of road pricing charges could discourage road users from using road  
infrastructure during peak times.  
In terms of practicality, several cities are identified in implementing road pricing policies, including Singapore,  
London, Stockholm, and Milan (Rotaris, Danielis, Marcucci, & Massiani, 2010). Despite encountering some  
obstacles before the implementation of the road pricing strategy, its implementation has been demonstrated to  
be an effective approach to reducing traffic congestion in these urban areas.  
Moreover, previous researchers have implemented various methods to evaluate the effectiveness of road pricing  
strategies. Among the methods, SD emerged as the prominent approach in evaluating the effectiveness of road  
pricing due to the method can correlate various causal relationships in a holistic view (Ahmarofi, Zainal Abidin,  
& Mahadzir, 2022). Based on previous works, Wang, Lu, and Peng (2008) developed an SD model to create  
scenarios for urban development and transportation to increase the system’s sustainability.  
Furthermore, Walch, Neubauer, Schildorfer, & Schirrer (2024) proposed an SD model approach using CLD to  
illustrate the relationship between vehicles and infrastructure in addressing traffic challenges such as efficiency,  
sustainability, and safety. In addition, Fontoura, Radzicki, & Ribeiro (2024) developed an SD model to verify  
the effects of sustainable transport policies, focusing on congestion and air pollution.  
Besides, Jia & Zhu (2025) constructed an SD model for the urban road transport system that aims to reduce  
emissions and pollution. Subsequently, Jia, Yan, Shen, and Zheng (2017) utilized an SD model for traffic  
congestion pricing that considers both environmental and social benefits. Additionally, Nunes, Ferreira,  
Govindan, & Pereira (2021) developed a cognitive mapping and the SD approach to find which factors foster  
smart city success, as well as the cause-and-effect relationships for sustainable transportation.  
Based on the capability of SD that has been proven by the previous works, SD is considered in this research for  
developing a working theory related to the road pricing strategy in Kuala Lumpur. To fulfil that, CLD is  
implemented as a tool based on the SD principles for developing a working theory. The research methodology  
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is further explained in the subsequent section.  
RESEARCH METHODOLOGY  
CLD is a qualitative tool for developing a working theory related to an articulated problem. Figure 1 exhibits  
the flow of developing CLD based on the principles of the SD method.  
Start  
Articulate road congestion problem  
Identify cause and effect variables  
Correlate identified cause and effect variables through an arrow to form causal  
relationship  
Determine the sign (positive or negative) of the causal relationship  
Construct feedback loop (reinforcing or balancing loop)  
Determine the theme of the feedback loop  
End  
Fig. 1 The development of a causal loop diagram  
Before the working theory can be developed, several factors and effects are identified to understand the behavior  
of the problem. Subsequently, the identified factors and effects are connected through causal relationships to  
correlate how a variable affects another variable.  
In developing a causal relationship, a curved line with an arrow will be 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. The positive sign indicates that a change in the variable’s parameter will produce an  
identical direct change in the other variable. Figure 2 demonstrates an example of how a road pricing charge  
variable affects the trip cost variable.  
+
Road pricing  
charge  
Trip cost  
Fig. 2 The positive causal relationship  
If the road pricing charge variable increases, it will contribute to an increase in the trip cost variable as well.  
Similarly, if the road pricing charge variable decreases, the trip cost variable will also decrease. Since both  
variables are changing in the same direction, the relationship polarity between these variables is indicated as a  
positive relationship.  
In contrast, if an increase in a variable results in a decrease in another variable or vice versa, the relationship  
between these variables is indicated by a negative sign. Figure 3 presents the negative relationship between the  
trip cost variable and the trips per day variable.  
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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
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-
Trips per day  
Trip cost  
Fig. 3 The negative causal relationship  
If the trip cost variable increases, the trips per day variable will decrease since the rate of utilization of roads is  
higher than usual. Alternatively, if the trip cost variable decreases, the trips per day variable will increase. Thus,  
the polarity between these variables is denoted as a negative relationship.  
Subsequently, the feedback loop is constructed based on the causal relationships. The feedback loop is  
categorized into two types: either the reinforcing loop (labelled by ‘R’) or the balancing loop (labelled by ‘B’)  
by counting the number of negative polarities. If the number of negative polarities is even, then the feedback  
loop is considered a reinforcing loop. However, if the number of negative polarities is odd, the feedback loop is  
considered a balancing loop. Table 1 demonstrates the description of these two feedback loops.  
Table 1: Description of the feedback process in a causal loop diagram  
Example of a loop Type of feedback loop Label  
Indication  
A
Reinforcing loop  
The number of negative polarities is zero or even.  
R
+
+
B
R
D
+
+
C
A
Balancing loop  
The number of negative polarities is odd.  
B
-
+
B
B
D
+
+
C
Both of the feedback loops have an impact on the system. If the feedback loop is categorized as the reinforcing  
loop, it often leads the system into two different behaviors drastically, either exponential growth or rapid decline.  
In this regard, as the name implies, the reinforcing loop must be carefully observed since it will force the system  
into either an extreme situation. On the other hand, the feedback loop is normally characterized as a balancing  
loop, where the loop often serves as a stabilizer loop, i.e., controls the system to achieve an equilibrium state.  
DISCUSSION  
The constructed CLD conceptualizes the behavior of the road pricing system by incorporating four feedback  
loops, namely road congestion, driving attraction, road pricing, and public transport. The CLD model based on  
the SD method is presented in Figure 4.  
Fig. 4 Causal loop diagram regarding road congestion in Kuala Lumpur  
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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
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Each of the feedback loops contains identified variables, those that should be prioritized when managing road  
pricing. The elaboration of each feedback loop is further explained in the following Figures 5, 6, 7, and 8,  
respectively.  
Fig. 5 Road congestion loop  
As illustrated in Figure 5, the road congestion loop is considered a reinforcing loop since no negative relationship  
is formed. The high number of urban populations contributes to the increasing travel demand within Kuala  
Lumpur. Consequently, this condition affects the growth rate of vehicles as well as the number of vehicles.  
Besides, the vehicle sharing rate and the number of private vehicle owners have a direct impact on the number  
of vehicles. As a result, the degree of road congestion increases due to the increasing number of vehicles in the  
city.  
Moreover, the degree of road congestion is contributed through the attractiveness of driving private vehicles,  
travel time while driving on the road, and public transport users available in Kuala Lumpur, such as light rail  
transit (LRT), mass rapid transit (MRT), Keretapi Tanah Melayu Electric Train Service (KTM-ETS), and Rapid  
KL. Hence, the variables included in this loop should be scrutinized, as they will result in unforeseen changes to  
the road congestion system within a short time.  
Pressure of  
Government  
+
Degree of Road  
congestion  
-
B
+
Public Transport  
Loop  
Investment in Public  
Transport  
Public Transport  
Users  
+
+
Adequacy of Public  
Transport  
Fig. 6 Public transport loop  
The public transport loop in Figure 6 is considered a balancing loop since the number of negative relationships  
is odd. The higher the degree of road congestion, the higher the pressure on the government could be, since the  
responsibility to manage this issue is on the government side, more specifically, on the Ministry of  
Transportation. Subsequently, the government will increase the investment in public transport to curb the issue,  
such as widening the road lanes, constructing new lanes for LRT, MRT, and KTM-ETS, and increasing the  
number of Rapid KL buses. Consequently, the public transport adequacy could be improved. As a result, the  
number of people using public transportation will increase, thereby decreasing the degree of road congestion. In  
this regard, this loop acts as a stabilizer for the system since it can control the degree of road congestion.  
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INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)  
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Fig. 7 Road pricing loop  
Figure 7 exhibits the road pricing loop. An increase in the degree of road congestion contributes to the pressure  
on the government to manage the problem. Consequently, the charge for road pricing will be imposed during  
peak hours to resolve the road congestion. Increases in the road pricing charge will increase the trip cost, thus  
the number of trips per day via private vehicle will decrease in turn. The reason is that road users tend to reduce  
trips per day to save money. Reduced daily trips result in decreased traffic volume, and travel time could be  
shorter. As a result, road congestion will also decrease. Due to the even number of negative relationships, the  
road pricing loop is categorized as a reinforcing loop. This loop tends to create exponential growth or rapid  
decline in the degree of road congestion. As a result, this loop must be regularly observed by the government  
and stakeholders due to unexpected changes at any time.  
Attractiveness of  
Driving  
-
-
Trips per Day  
B
Driving Attraction  
Loop  
-
+
Degree of Road  
Traffic Volume  
-
congestion  
-
Travel Time  
Fig. 8 Driving attraction loop  
As illustrated in Figure 8, the driving attraction loop requires balancing. The balancing loop has the effect of  
stabilizing the variables. As a result, all variables will contribute to changes in their effect to suit the condition,  
respectively (increase or decrease). As the level of road congestion increases, the attraction of driving reduces.  
Consequently, the number of trips per day decreases as the attractiveness of driving declines. The number of  
trips made per day affects the volume of traffic. A decrease in daily trips parallels a decrease in traffic volume.  
As a result, travel times could be decreased. As travel time decreases, the degree of road congestion decreases  
as well.  
CONCLUSION  
In this paper, four feedback loops are constructed towards the road congestion in Kuala Lumpur by implementing  
CLD. The significant loops, namely road congestion, driving attraction, road pricing, and public transport, were  
constructed based on the causal relationships among related variables. CLD serves as a working theory model  
for conceptualizing road pricing management. Three reinforcing loops, i.e., road congestion, driving attraction,  
and road pricing, and one balancing loop, i.e., public transport, are identified. Based on the findings, authorities  
should closely monitor road congestion, road pricing, and driving attraction since these loops are categorized as  
reinforcing loops, i.e., influential loops. When one variable in these loops increases, other variables typically  
increase as well, thus creating a system of exponentially increasing or rapidly decreasing values. In this regard,  
road pricing could be the recommended policy for reducing road congestion effectively. In contrast, it is found  
that the public transport loop turns into a balancing loop. This loop will have a balancing effect on the associated  
variables, i.e., all of the related variables will increase or decrease to achieve an equilibrium state among the  
loops in the system. Furthermore, CLD proved its capability in identifying the causes and effects related to road  
pricing implementation to reduce road congestion in Kuala Lumpur from a holistic view from various  
perspectives. For further study, the constructed CLD could be extended to the development of a stock flow  
diagram (SFD) for quantifying the impact of each related variable on road congestion.  
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ACKNOWLEDGEMENT  
The authors would like to express their sincere gratitude to the Kedah State Research Committee, UiTM Kedah  
Branch, for the generous funding provided under the Tabung Penyelidikan Am. This support was crucial in  
facilitating the research and ensuring the successful publication of this article.  
REFERENCE  
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.  
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.  
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.  
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  
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.  
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.  
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  
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.  
9. Massachusetts Institute of Technology. (n.d.). Kuala Lumpur. Malaysia Sustainable Cities Program.  
Retrieved October 30, 2025, from https://malaysiacities.mit.edu/kualalumpur  
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.  
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  
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.  
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  
14. Statistik Pengangkutan Jalan 2020, Ministry of Transportation Malaysia 2020. Retrieved from  
https://www.mot.gov.my/en/Statistik%20Tahunan%20Pengangkutan/Transport%20Statistics%20Mala  
ysia%202020.pdf  
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.  
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  
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