Beyond Human Error: How Road Design and Urban Environmental Factors Drive Major Accidents in Jakarta
DOI:
https://doi.org/10.5614/jpwk.2026.37.2.1Keywords:
road safety, major traffic accidents, environmental factors, JakartaAbstract
Jakarta, Indonesia's capital and a major megacity, faces significant road safety challenges due to its dense population, high mobility, and complex urban environment. While human error is often cited as the primary cause of traffic accidents, growing evidence suggests that environmental factors?such as road design, land use, and weather conditions?also play a critical role. This study examines the influence of these factors on major road accidents, defined as those resulting in serious injuries or fatalities, in Jakarta from 2019 to 2023. Using a binary logistic regression model and a dataset of over 19,000 accidents across 4,654 road segments, this research identifies key predictors of accident severity. The findings reveal that weekends, peak traffic hours, adverse weather, and specific road types (e.g., one-way four-lane roads) significantly increase the likelihood of major accidents. Furthermore, industrial land use emerges as a high-risk factor, while government and public service zones are associated with reduced accident risks. These results highlight the urgent need to integrate environmental factors into urban road safety strategies. The study offers actionable insights for policymakers and urban planners, including recommendations for road design improvements, better land use planning, and targeted traffic enforcement. While focused on Jakarta, the findings provide valuable lessons for other rapidly urbanising cities worldwide, particularly in developing regions where similar challenges persist.
Downloads
References
Adeyemi, O. J., A. Arif, and R. Paul (2021) Exploring the Relationship of Rush Hour Period and Fatal and Non-Fatal Crash Injuries in The U.S.: A Systematic Review and Meta-Analysis. https://doi.org/10.1101/2021.08.03.21261572
Ashley, W. S., S. Strader, D. C. Dziubla, and A. Haberlie (2015) Driving Blind: Weather-Related Vision Hazards and Fatal Motor Vehicle Crashes. Bulletin of the American Meteorological Society 96(5), 755-778. https://doi.org/10.1175/BAMS-D-14-00026.1
Bonney, G. E. (1987) Logistic Regression for Dependent Binary Observations. Biometrics 43(4), 951. https://doi.org/10.2307/2531548
Brooks, C. A., and G. E. Bonney (1989) A simulation study of properties of a regressive logistic model. Journal of Statistical Computation and Simulation 32(1-2), 31-43. https://doi.org/10.1080/00949658908811151
Canning, W., R. Morrison, and M. Eldridge (1938) Report of Committee on Traffic Regulation in Municipalities - One-Way Streets.
Chen, S., M. Kuhn, K. Prettner, and D. E. Bloom (2019) The global macroeconomic burden of road injuries: Estimates and projections for 166 countries. The Lancet Planetary Health 3(9), e390-e398. https://doi.org/10.1016/S2542-5196(19)30170-6
Chung, H., Q. Duan, Z. Chen, and Y. Yang (2023) Investigating the effects of POI-based land use on traffic accidents in Suzhou Industrial Park, China. Case Studies on Transport Policy 12, 100933. https://doi.org/10.1016/j.cstp.2022.100933
FitzGerald, P. E. B., and M. W. Knuiman (1998) Interpretation of Regressive Logistic Regression Coefficients in Analyses of Familial Data. Biometrics 54(3), 909. https://doi.org/10.2307/2533845
Godthelp, H. (2023) Towards a safe system in low- and middle-income countries: Vehicles that guide drivers on self-explaining roads. Safety Science Digest 2(1), 1-18. https://doi.org/10.55329/avnw4364
Hashimoto, S., S. Yoshiki, R. Saeki, Y. Mimura, R. Ando, and S. Nanba (2016) Development and application of traffic accident density estimation models using kernel density estimation. Journal of Traffic and Transportation Engineering (English Edition) 3(3), 262-270. https://doi.org/10.1016/j.jtte.2016.01.005
Heydari, S., A. Hickford, R. McIlroy, J. Turner, and A. M. Bachani (2019) Road safety in low-income countries: State of knowledge and future directions. Sustainability 11(22), 6249. https://doi.org/10.3390/su11226249
Huang, H., H. C. Chin, and Md. M. Haque (2008) Severity of driver injury and vehicle damage in traffic crashes at intersections: A Bayesian hierarchical analysis. Accident Analysis & Prevention 40(1), 45-54. https://doi.org/10.1016/j.aap.2007.04.002
Huang, Y., X. Wang, and D. Patton (2018) Examining spatial relationships between crashes and the built environment: A geographically weighted regression approach. Journal of Transport Geography 69, 221-233. https://doi.org/10.1016/j.jtrangeo.2018.04.027
Johnsson, T. (1992) A procedure for stepwise regression analysis. Statistical Papers 33(1), 21-29. https://doi.org/10.1007/BF02925308
Jusuf, A., I. P. Nurprasetio, and A. Prihutama (2017) Macro data analysis of traffic accidents in Indonesia. Journal of Engineering and Technological Sciences 49(1), 132-143. https://doi.org/10.5614/j.eng.technol.sci.2017.49.1.8
Kaygisiz, , M. Senbil, and A. Yildiz (2017) Influence of urban built environment on traffic accidents: The case of Eskisehir (Turkey). Case Studies on Transport Policy 5(2), 306-313. https://doi.org/10.1016/j.cstp.2017.02.002
Koma?kov L., and M. Poliak (2016) Factors affecting the road safety. Journal of Communication and Computer 13(3), 143-148. https://doi.org/10.17265/1548-7709/2016.03.006
Larsson, P., and C. Tingvall (2013) The safe system approach - A road safety strategy based on human factors principles. In: Harris, D. (Ed.) Engineering Psychology and Cognitive Ergonomics: Applications and Services, pp. 19-28. Berlin: Springer. https://doi.org/10.1007/978-3-642-39354-9_3
Li, Z., Y. Ci, C. Chen, G. Zhang, Q. Wu, Z. Qian (Sean), P. D. Prevedouros, and D. T. Ma (2019) Investigation of driver injury severities in rural single-vehicle crashes under rain conditions using mixed logit and latent class models. Accident Analysis & Prevention 124, 219-229. https://doi.org/10.1016/j.aap.2018.12.020
Lie, A., and C. Tingvall (2023) Are crash causation studies the best way to understand system failures - Who can we blame? Accident Analysis & Prevention 197, 107432. https://doi.org/10.1016/j.aap.2023.107432
Mannering, F. L., V. Shankar, and C. R. Bhat (2016) Unobserved heterogeneity and the statistical analysis of highway accident data. Analytic Methods in Accident Research 11, 1-16. https://doi.org/10.1016/j.amar.2016.04.001
Ng, J. C. W., and T. Sayed (2004) Effect of geometric design consistency on road safety. Canadian Journal of Civil Engineering 31(2), 218-227. https://doi.org/10.1139/l03-090
O'Donnell, C. J., and D. H. Connor (1996) Predicting the severity of motor vehicle accident injuries using models of ordered multiple choice. Accident Analysis & Prevention 28(6), 739-753. https://doi.org/10.1016/S0001-4575(96)00050-4
Pljaki?, M., D. Jovanovi?, and B. Matovi? (2022) The influence of traffic-infrastructure factors on pedestrian accidents at the macro-level: The geographically weighted regression approach. Journal of Safety Research 83, 248-259. https://doi.org/10.1016/j.jsr.2022.08.021
Retallack, A. E., and B. Ostendorf (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
Rolison, J. J. (2020) Identifying the causes of road traffic collisions: Using police officers' expertise to improve the reporting of contributory factors data. Accident Analysis & Prevention 135, 105390. https://doi.org/10.1016/j.aap.2019.105390
Saadat, S., K. Rahmani, A. Moradi, S. ad D. Zaini, and F. Darabi (2019) Spatial analysis of driving accidents leading to deaths related to motorcyclists in Tehran. Chinese Journal of Traumatology - English Edition 22(3), 148-154. https://doi.org/10.1016/j.cjtee.2018.12.006
Sari, N., S. Malkhamah, and S. Budi (2024) Road traffic accidents factor on rural arterial roads. Journal of Applied Engineering Science 22(2), 470-482. https://doi.org/10.5937/jaes0-49183
Shacham, M., and N. Brauner (2014) Application of stepwise regression for dynamic parameter estimation. Computers & Chemical Engineering 69, 26-38. https://doi.org/10.1016/j.compchemeng.2014.06.013
Shih, S. F., and W. F. P. Shih (1978) Use of dummy variables in water resources studies. Journal of Hydrology 38(3-4), 289-298. https://doi.org/10.1016/0022-1694(78)90075-6
Siregar, M. L., J. R. Sumabrata, A. Kusuma, O. B. Samosir, and S. N. Rudrokasworo (2019) Analyzing driving environment factors in pedestrian crashes injury levels in Jakarta and the surrounding cities. Journal of Applied Engineering Science 17(3), 361-368. https://doi.org/10.5937/jaes17-22121
Soehodho, S. (2017) Public transportation development and traffic accident prevention in Indonesia. IATSS Research 40(2), 76-80. https://doi.org/10.1016/j.iatssr.2016.05.001
Tamakloe, R., S. Lim, E. F. Sam, S. H. Park, and D. Park (2021) Investigating factors affecting bus/minibus accident severity in a developing country for different subgroup datasets characterised by time, pavement, and light conditions. Accident Analysis & Prevention 159, 106268. https://doi.org/10.1016/j.aap.2021.106268
Tang, Y., D. Zhong, X. Zha, and L. Na (2018) Principal Component Analysis of Fatal Traffic Accidents Based on Vehicle Condition Factors. 2018 11th International Conference on Intelligent Computation Technology and Automation (ICICTA), pp. 315-317. https://doi.org/10.1109/ICICTA.2018.00078
Theeuwes, J. (2021) Self-explaining roads: What does visual cognition tell us about designing safer roads? Cognitive Research: Principles and Implications 6(1), 15. https://doi.org/10.1186/s41235-021-00281-6
Wang, K., X. Feng, H. Li, and Y. Ren (2022) Exploring Influential Factors Affecting the Severity of Urban Expressway Collisions: A Study Based on Collision Data. International Journal of Environmental Research and Public Health 19(14), 8362. https://doi.org/10.3390/ijerph19148362
Wang, L., J. Zhang, and Y. Feng (2020) Study on Severity and Influencing Factors of Injury at Intersections. Proceedings of the 5th International Symposium on Social Science (ISSS 2019). https://doi.org/10.2991/assehr.k.200312.001
Washington, S., J. Metarko, I. Fomunung, R. Ross, F. Julian, and E. Moran (1999) An inter-regional comparison: Fatal crashes in the southeastern and non-southeastern United States: Preliminary findings. Accident Analysis & Prevention 31(1-2), 135-146. https://doi.org/10.1016/S0001-4575(98)00055-4
Wier, M., J. Weintraub, E. H. Humphreys, E. Seto, and R. Bhatia (2009) An area-level model of vehicle-pedestrian injury collisions with implications for land use and transportation planning. Accident Analysis & Prevention 41(1), 137-145. https://doi.org/10.1016/j.aap.2008.10.001
World Health Organization (2023) Global Status Report on Road Safety 2023. Geneva: World Health Organization. https://iris.who.int/handle/10665/375016
Zeng, Q., H. Wen, H. Huang, X. Pei, and S. C. Wong (2017) A multivariate random-parameters Tobit model for analyzing highway crash rates by injury severity. Accident Analysis & Prevention 99, 184-191. https://doi.org/10.1016/j.aap.2016.11.018
Zhang, J., B. Yu, Y. Chen, Y. Kong, and J. Gao (2022) Comparative Analysis of Influencing Factors on Crash Severity between Super Multi-Lane and Traditional Multi-Lane Freeways Considering Spatial Heterogeneity. International Journal of Environmental Research and Public Health 19(19), 12779. https://doi.org/10.3390/ijerph191912779
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Journal of Regional and City Planning

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Manuscript submitted to JRCP has to be an original work of the author(s), contains no element of plagiarism, and has never been published or is not being considered for publication in other journals. The author(s) retain the copyright of the content published in JRCP. There is no need for request or consultation for future re-use and re-publication of the content as long as the author and the source are cited properly.





