Identifying the vehicle accident models based on driving behavior factors using structural equation modeling

Authors

DOI:

https://doi.org/10.15587/1729-4061.2024.306781

Keywords:

vehicle accidents, driving behavior, structural equation modeling, traffic accidents, motor vehicle drivers, car driver behavior, driving characteristics

Abstract

The increase in population is accompanied by an increase in the number of vehicles. It is inevitable that the number of vehicle accidents will also increase, which can be caused by various factors. Driver factors reviewed in this study include socioeconomic characteristics, movement characteristics, accident characteristics, and driver behavior characteristics. the purpose of this study is to study the vehicle accident model using interviews and Driving Behavior questionnaires with a total of 307 motorist respondents who have experienced accidents. Driver factors reviewed in this study include socioeconomic characteristics, movement characteristics, accident characteristics, and driver behavior characteristics using interviews and Driving Behavior questionnaires with a total of 307 motorist respondents who have experienced accidents.

This investigate used SEM (Structural Equation Modeling) with SmartPLS computer software. Two-wheeled vehicle accident modeling results Y=–0.234 X1+0.153 X3+ei2; R2=0.102. The greatest influence occurs in the characteristics of driver behavior (X3), namely Ordinary Violation, and for four-wheeled vehicle accident modeling results, Y=–0.343 X1+0.284 X3+ei2; R2=0.217. The greatest influence occurs in driver behavior characteristics (X3), namely Ordinary Violation. Ordinary Violation is defined as a deliberate deviation from the rule of law.

Thus, from the research results, the most influential variable was the behavior of drivers who committed ordinary violations such as ignoring speed limits, breaking through intersections, and driving under the influence of alcohol. So, there needs to be collaboration between the police and related parties in tackling accidents and reducing the risk of traffic accidents, such as long as socialization or information through newspapers or electronic media to the public in Jayapura City regarding the importance of collective awareness of driving safety

Supporting Agency

  • I am deeply thankful to Dr. Ir. M. Zainul Arifin, MT, and Dr. Fauzul Rizal Sutikno for their invaluable guidance and insightful advice throughout my research. Additionally, I encompass my gratitude to my parents, siblings, and friends meant for their unwavering care, encouragement, and prayers through the writing process of this article.

Author Biographies

Fadila Ardi Putri Damayanti, Brawijaya University

Master’s Student in Civil Engineering

Department of Civil Engineering

Muhammad Zainul Arifin, Brawijaya University

Doctor of Civil Engineering

Department of Civil Engineering

Fauzul Rizal Sutikno, Brawijaya University

Doctor of Urban Regional and Planning

Departmen Urban Regional and Planning

Muh Miftahulkhair, Universitas Sulawesi Barat

Master of Civil Engineering

Department of Civil Engineering

References

  1. Van Elslande, P., Elvik, R. (2012). Powered two-wheelers within the traffic system. Accident Analysis & Prevention, 49, 1–4. https://doi.org/10.1016/j.aap.2012.09.007
  2. Retallack, A. E., Ostendorf, B. (2020). Relationship Between Traffic Volume and Accident Frequency at Intersections. International Journal of Environmental Research and Public Health, 17 (4), 1393. https://doi.org/10.3390/ijerph17041393
  3. Pervez, A., Lee, J., Huang, H. (2021). Identifying Factors Contributing to the Motorcycle Crash Severity in Pakistan. Journal of Advanced Transportation, 2021, 1–10. https://doi.org/10.1155/2021/6636130
  4. Suraji, A., Djakfar, L., Wicaksono, A., Marjono, M., Putranto, L. S., Susilo, S. H. (2021). Analysis of intercity bus public transport safety perception modeling using conjoint. Eastern-European Journal of Enterprise Technologies, 4 (3 (112)), 36–42. https://doi.org/10.15587/1729-4061.2021.239255
  5. Najmy, A., Dewi, R. S., Ciptomulyono, U. (2018). Identifikasi Pengaruh Perilaku terhadap Tingkat Kecelakaan Lalu Lintas dengan Stuctural Equation Modeling (SEM). Internasional Riset & Teknologi Teknik (IJERT).
  6. Bathan, A., de Ocampo, J., Ong, J., Gutierrez, A. A., Seva, R. R., Mariano, R. S. (2018). A predictive model of motorcycle accident involvement using structural equation modeling considering driver personality and riding behavior in Metro Manila. Proceedings of the International Conference on Industrial Engineering and Operations Management, 1783–1804. Available at: https://animorepository.dlsu.edu.ph/faculty_research/442
  7. Putri, F., Arifin, M., Djakfar, L. (2022). Prediction model of motorcycle accident in economic and driving behaviour factors. Eastern-European Journal of Enterprise Technologies, 4 (3 (118)), 27–33. https://doi.org/10.15587/1729-4061.2022.263651
  8. Hukom, F. S., Djakfar, L., Arifin, M. Z. (2023). Model Prediksi Kecelakaan Kendaraan Sepeda Motor pada Ruas Jalan di Kota Ambon. Rekayasa Sipil, 17 (2), 217–222. https://doi.org/10.21776/ub.rekayasasipil.2023.017.02.14
  9. Miftahulkhair, M., Arifin, M. Z., Sutikno, F. R. (2024). Revealing the impact of losses on flexible pavement due to vehicle overloading. Engineering Technological Systems, 2 (1 (128)), 55–63. https://doi.org/10.15587/1729-4061.2024.299653
  10. Chavan, E., Roopa, M. (2020). Automatic crash guard for motorcycles. International Journal of Electrical Engineering and Technology (IJEET), 11 (2), 17–26. Available at: https://sdbindex.com/Documents/index/00000003/00000-04007
  11. Machsus, M., Sulistio, H., Wicaksono, A., Djakfar, L. (2014). Generalized linear and generalized additive models in studies of motorcycle accident prediction models for the north-south road corridor in Surabaya. The 17thFSTPT International Symposium, 976–986. Available at: https://jurnal.unej.ac.id/index.php/PFSTPT/article/view/2921/2347
  12. Shah, S., Ahmad, N., Shen, Y., Pirdavani, A., Basheer, M., Brijs, T. (2018). Road Safety Risk Assessment: An Analysis of Transport Policy and Management for Low-, Middle-, and High-Income Asian Countries. Sustainability, 10 (2), 389. https://doi.org/10.3390/su10020389
  13. Murphy, P., Morris, A. (2020). Quantifying accident risk and severity due to speed from the reaction point to the critical conflict in fatal motorcycle accidents. Accident Analysis & Prevention, 141, 105548. https://doi.org/10.1016/j.aap.2020.105548
  14. Mohamed, M., Bromfield, N. F. (2017). Attitudes, driving behavior, and accident involvement among young male drivers in Saudi Arabia. Transportation Research Part F: Traffic Psychology and Behaviour, 47, 59–71. https://doi.org/10.1016/j.trf.2017.04.009
  15. Zhang, G., Yau, K. K. W., Chen, G. (2013). Risk factors associated with traffic violations and accident severity in China. Accident Analysis & Prevention, 59, 18–25. https://doi.org/10.1016/j.aap.2013.05.004
  16. Fan, Y., Chen, J., Shirkey, G., John, R., Wu, S. R., Park, H., Shao, C. (2016). Applications of structural equation modeling (SEM) in ecological studies: an updated review. Ecological Processes, 5 (1). https://doi.org/10.1186/s13717-016-0063-3
  17. Hassan, H. M. (2015). Investigation of the self-reported aberrant driving behavior of young male Saudi drivers: A survey-based study. Journal of Transportation Safety & Security, 8 (2), 113–128. https://doi.org/10.1080/19439962.2015.1017782
  18. Yamin, S., Kurniawan, H. (2009). SPSS Complete: teknik analisis statistik terlengkap dengan software SPSS. Jakarta: Salemba Infotek, 328.
  19. Sholihin, M., Ratmono, D. (2021). Analisis SEM-PLS dengan WarpPLS 7.0: untuk hubungan nonlinier dalam penelitian sosial dan bisnis. Yogyakarta, 320.
  20. Hair Jr., J. F., Black, W. C., Babin, B. J., Anderson, R. E. (2010). Multivariate Data Analysis. Pearson. Available at: https://www.drnishikantjha.com/papersCollection/Multivariate%20Data%20Analysis.pdf
  21. Jung, S., Xiao, Q., Yoon, Y. (2013). Evaluation of motorcycle safety strategies using the severity of injuries. Accident Analysis & Prevention, 59, 357–364. https://doi.org/10.1016/j.aap.2013.06.030
  22. Shaheed, M. S., Gkritza, K. (2014). A latent class analysis of single-vehicle motorcycle crash severity outcomes. Analytic Methods in Accident Research, 2, 30–38. https://doi.org/10.1016/j.amar.2014.03.002
Identifying the vehicle accident models based on driving behavior factors using structural equation modeling

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Published

2024-06-29

How to Cite

Damayanti, F. A. P., Arifin, M. Z., Sutikno, F. R., & Miftahulkhair, M. (2024). Identifying the vehicle accident models based on driving behavior factors using structural equation modeling. Eastern-European Journal of Enterprise Technologies, 3(3 (129), 85–93. https://doi.org/10.15587/1729-4061.2024.306781

Issue

Vol. 3 No. 3 (129) (2024): Control processes

Section

Control processes

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Copyright (c) 2024 Fadila Ardi Putri Damayanti, Muhammad Zainul Arifin, Fauzul Rizal Sutikno, Muh Miftahulkhair

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