Open Journal Systems

Investigating Aggressive Driving Behavior in Reducing Traffic Congestion on Bandung City

Ulfi Mutia, Manahan Siallagan, Utomo Sarjono Putro, Marsetyawan Marsetyawan, Dini Alamanda


Abstract. Traffic congestion reflects waste of time and energy that must be eliminated. Many methods have been employed by past studies to solve this problem. The approach utilized by those studies is mostly macroscopic that consider vehicles and drivers in aggregate. This study argues that a more microscopic approach is also required to depict and solve the congestion problem. Hence, agent-based simulation is brought forward to help identify the cause of congestion problem. In this study, drivers are assumed to have their own motives that might drive them to resort to aggressive behaviors that ultimately lead to traffic congestion. As a preliminary investigation, this study aims to discover type of aggressive driving behavior on Bandung City. The results demonstrate that aggressive dirivng behaviors on Bandung City can be categorized into five factors namely improper speed, inattentiveness, display of hostility, impatience, and disobedience of traffic sign/signals. This study also found that different composition of driving behaviors leads to different degree of congestion. Impatience behavior is found to be the factor that must be eliminate to remedy congestion on Bandung City.

Kata kunci: Driving behavior, traffic congestion, factor analysis, agent-based simulation, smart city

Abstrak. Kemacetan lalu lintas mencerminkan pemborosan energi dan waktu yang harus ditiadakan. Banyak metode telah digunakan dalam penelitian-penelitian masa lalu yang ditujukan untuk memecahkan permasalahan ini. Pendekatan yang digunakan oleh penelitian-penelitian tersebut untuk menggambarkan dan memecahkan masalah kemacetan pada umumnya bersifat makroskopik yang memandang kendaraan dan pengendara secara agregat. Studi ini berpendapat bahwa pendekatan yang lebih mikroskopis diperlukan dalam menggambarkan dan memecahkan permasalahan kemacetan. Dalam studi ini, pengemudi diasumsikan memiliki motif tersendiri saat berkendara yang dapat mendorong mereka untuk berperilaku agresif dan berujung pada kemacetan. Sebagai penyelidikan awal, studi ini bertujuan untuk mengetahui jenis perilaku mengemudi agresif di Kota Bandung. Hasil dari studi ini menunjukkan bahwa perilaku mengemudi agresif dapat dikategorikan menjadi lima faktor yaitu penggunaan kecepatan yang tidak sepantasnya, kurangnya perhatian saat berkendara, perilaku kekerasan dalam berkendara, ketidaksabaran dalam berkendara, dan ketidaktaatan pada tanda lalu lintas. Studi ini juga menemukan bahwa komposisi perilaku mengemudi agresif yang berbeda dapat menghasilkan perbedaan tingkat kemacetan lalu lintas. Ketidaksabaran dalam berkendara ditemukan sebagai factor yang paling harus dieliminasi untuk mengurangi kemacetan di Kota Bandung.

Keywords: Perilaku mengemudi, kemacetan lalu lintas, analisis factor, simulasi berbasis agen, smart city


driving behavior, traffic congestion, factor analysis, agent-based simulation, smart city

Full Text:



Bazzan, A. L. C., Wahle, J. & Klügl, F. (1999). Agents in traffic modelling: from reactive to social behavior. W. Bugard, T. Cristaller, & A. B. Cremers (Ed.), Proceeedings of the 23rd Annual German Conference on Artifical Intelligence (pp. 303–306). Doi: 10.1007/3-540-48238-5_28

Beck, K. H., Daughters, S. B., & Ali, B. (2013). Hurried driving: Relationship to distress tolerance, driver anger, aggressive and risky driving in college students. Accident Analysis & Prevention, 51, 51-55. Elsevier. doi: 10.1016/j.aap.2012.10.012

Burmeister, B., Haddadi, A., and Matylis, G. (1997). Application of multi-agent systems in traffic and transportation. IEE Proceedings – Software Engineering, vol. 144, issue 1 (pp. 51–60). doi: 10.1049/ip-sen:19971023.

Federal Highway Administration of United States. Cambridge Systematics, Inc. & Texas Transportation Institute. (2005). Traffic Congestion and Reliability: Trends and Advanced Strategies for Congestion Mitigation. Retrieved from:

Das, S. (2011). Cellular automata based traffic model that allows the cars to move with a small velocity during congestion. Chaos, Solitons & Fractals, 44(4), 185-190. Doi: 10.1016/j.chaos.2011.01.012.

Galovski, T., & Blanchard, E.B. (2002). Psychological characteristics of agressive drivers with and without intermittent explosive disorder. Behavior Research & Therapy, 40(10), 1157-1168. Doi: 10.1016/S0005-7967(01)00083-3

Hager, K., Rauh, J. and Rid, W. (2015). Agent-based modeling of traffic behavior in growing metropolitan areas. Transportation Research Procedia, 10, 306-315. Doi: 10.1016/j.trpro.2015.09.080.

Jain, V., Sharma, A., & Subramanian, L. (2012). Road traffic congestion in the developing world. Proceedings of the 2nd ACM Symposium on Computing for Development. Doi: 10.1145/2160601.2160616.

James, L., & Nahl, D. (2000). Road rage and aggressive driving: steering clear of highway warfare. Amherst, New York: Prometheus Books.

Kaiser, S., Furian, G. and Sclembach, C. (2016). Aggressive behaviour in road traffic – findings from Austria. Transportation Research Procedia, 14, 4384-4392. Elsevier. Doi: 10.1016/j.trpro.2016.05.36

Khalesian, M., & Delavar, M. R. (2008). A multi-agent based traffic network micro-simulation using spatio-temporal GIS. Center of Excellence in Geomatics Eng. and Disaster Management, 10, 31-36. Retrieved from:

Ljubović, V. (2009). Traffic simulation using agent-based models. XXII International Symposium on Information, Communication and Automation Technologies (ICAT), 1-06. IEEE. Doi: 10.1109/ICAT.2009.5348417

Manley, E., Cheng, T., Penn, A., & Emmonds, A. (2014). A framework for simulating large scale complex urban traffic dynamics through hybrid agent-based modelling. Computers, Environment and Urban Systems, 44, 27–36. Elsevier. doi: 10.1016/j.compenvurbsys.2013.11.003

Macal, C. M., & North, M. J. (2010). Tutorial on agent-based modelling and simulation. Journal of simulation, 4(3), 151-161. Retrieved from:

Nagel & Schreckenberg. (1992). A cellular automaton model for freeway traf-fic, J. Phys. I, 2221-2229. Doi: 10.1051/jp1:1992277.

Naveteur, J., CÅ“ugnet, S., Charron, C., Dorn, L., & Anceaux, F. (2013). Impatience and time pressure: subjective reactions of drivers in situations forcing them to stop their car in the road. Transportation Research Part F: Traffic Psychology and Behaviour, 18, 58-71. Elsevier. doi: 10.1016/j.trf.2012.12.008

Organisational for Economic Co-operation and Development (OECD). (2007). Managing Urban Traffic Congestion. Retrieved from:

Rarestep Inc. Fleetio Drive [Web site]. (2018). Retrieved from:

Rossetti, R. J., Bampi, S., Liu, R., Van Vliet, D., & Cybis, H. B. B. (2000). An agent-based framework for the assessment of drivers' decision making. Intelligent Transportation Systems, 2000. Proceedings, IEEE (pp. 387-392). IEEE. doi: 10.1109/ITSC.2000.881094

Badan Pusat Statistik Provinsi Jawa Barat, Indonesia. (2016). Statistik Transportasi Jawa Barat 2016. (Catalog: 8301007.32). Retrieved from:

Wickens, C., Hollands, J., Banburry, S., & Parasuraman, R. (2013). Engineering Psychology and Human Performance. New York: Pearson Education Inc.

Wiyono, T. S., Saputra, R., & Sarwoko, E. A. (2012). Pengembangan Sistem Informasi Pendaftaran Surat Ijin Mengemudi Online. Journal of Informatics and Technology, 1(1), 52-62. Retrieved from:

Zhang, G., Zhao, M., Sun, D. H., Liu, W. N., & Li, H. M. (2016). Stabilization effect of multiple drivers' desired velocities in car-following theory. Physica A: Statistical Mechanics and its Applications, 442, 532-540. Doi: 10.1016/j.physa.2015.09.022

Zhou, Lin & Xi. (2012). A dynamic network partition method for heterogenous urban traffic networks. 15th International IEEE Conference on Intelligent Transportation Systems (ITSC). IEEE. Doi: 10.1109/ITSC.2012.6338712



  • There are currently no refbacks.