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Shahin Mirbakhsh, Mahdi Azizi,
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- Student, Student Department of Civil and Environmental Engineering, Shahid Chamran University of Ahvaz., Department of Civil and Environmental Engineering, Shahid Chamran University of Ahvaz.
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Abstract
nAn essential aspect of enhancing road safety involves predicting road accidents and identifying risk
factors linked to severe injuries. This can be achieved through two distinct methods: machine learning
and statistical approaches. This study leverages machine learning techniques to assess the severity of
injuries pedestrians sustain in road traffic collisions. While statisticians aim to model and understand
connections between variables, machine learning tackles more complex datasets to create algorithms
capable of pattern recognition and prediction without explicit programming. Machine learning offers
advantages such as handling large datasets, adaptability to various data sources and tasks, and
automation of pattern recognition and prediction tasks, reducing manual involvement and enabling
rapid processing of vast amounts of data. Moreover, machine learning models can be continuously
updated and refined with new data to improve accuracy over time, unlike conventional statistical
techniques which may struggle with nonlinear interactions between variables. The study begins by
compiling a comprehensive list of features related to both accidents and environmental factors, with a
focus on those most influential on pedestrian injury severity. Selecting the best algorithm involves
evaluating its performance metrics, including accuracy, precision, recall, and F1 score. It’s crucial to
highlight that the developed model is not static; it requires regular updates and retraining using new
accident data to reflect changes in the road environment, driver behavior, and pedestrian conduct.
While the current model is based on Israeli data and predicts injury outcomes within Israel, for broader
applicability, it should undergo retraining and evaluation using traffic accident data specific to the
relevant country or region.
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Keywords: Safety of pedestrians, machine learning method, road safety, road accidents, identifying risk factors
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Browse Figures
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References
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1. PEDESTRIAN SAFETY, A road safety manual for decision-makers and practitioners, in:
Pedestrian Safety: a Road Safety Manual for Decision-Makers and Practitioners, second ed., World
Health Organization, Geneva, 2023. https://apps.who.int/iris/bitstream/
handle/10665/367419/978924 0072497-eng.pdf?sequence=1.
2. 27 Jan, Traffic Prediction: How Machine Learning Helps Forecast Congestions and Plan Optimal
Routes, 2022, https://www.altexsoft.com/.
3. S. Birfir, A. Elalouf, T. Resenbloom, Building machine-learning models for reducing the severity
of bicyclist road traffic injuries, Tranpostation engineering 12 (2023). June 2023.
4. NHTSA. Traffic Safety Facts: Pedestrians. National Highway Traffic Safety Administration,
Washington (DC). Report No.: DOT HS 812 681.
5. A. Behnood, F. Mannering, The temporal stability of factors affecting driver-injury severities in
single-vehicle crashes: Some empirical evidence (2015).
6. N. Ahmad, A. Ahmed, B. Wali, T.U. Saeed, Exploring factors associated with crash severity on
motorways in Pakistan, in: Proceedings of the Institution of Civil Engineers – Transport 04, 2019,
pp. 1–20, https://doi.org/10.1680/jtran.18.00032.
7. M. Taamneh, S. Alkheder, S. Taamneh, Data-mining techniques for traffic accident modeling and
prediction in the United Arab Emirates, J. Transport. Saf.Secur. 9 (2017) 146–166.
8. R. Mujalli, J. De Ona, A method for simplifying the analysis of traffic accidents injury severity on
two-lane highways using bayesian networks, J. Saf. Res. 42(2011) 317–326.
9. S. Dong, J. Zhou, A comparative study on drivers’ stop/go behavior at signalized intersections based
on decision tree classification model, J. Adv. Transport.(2020) 1–13.
10. Z. Liu, H. Chen, Y. Li, Q. Zhang, Taxi demand prediction based on a combination forecasting
model in hotspots, J. Adv. Transport. (2020) 1–13.
11. J. Abell′an, G. L′opez, J. De ̃Ona, Analysis of traffic accident severity using decision rules via
decision trees, Expert Syst. Appl. 40 (2013) 6047–6054.
12. N. Dong, H. Huang, L. Zheng, Support vector machine in crash prediction at the level of traffic
analysis zones: assessing the spatial proximity effects, Accid. Anal.Prev. 82 (2015) 192–198.
13. T. Anderson, Kernel density estimation and k-means clustering to profile road accident hotspots,
Accid. Anal. Prev. 41 (2009) 359–364.
14. Q. Zeng, H. Huang, A stable and optimized neural network model for crash injury severity
prediction, Accid. Anal. Prev. 73 (2014) 351–358.
15. J.K. Kim, G.F. Ulfarsson, V.N. Shankar, S. Kim, Age and pedestrian injury severity in motor-
vehicle crashes: a heteroskedastic logit analysis, Accid. Anal. Prev.40 (5) (2008) 1695–1702.
16. L.F. Beck, L.J. Paulozzi, S.C. Davidson, Pedestrian fatalities, Atlanta metropolitan statistical area
and United States, 2000–2004, J. Saf. Res. 38 (6) (2007)613–616.
17. D.F. Preusser, J.K. Wells, A.F. Williams, H.B. Weinstein, Pedestrian crashes in Washington DC
and baltimore, Accid. Anal. Prev. 34 (5) (2002) 703–710.
18. M.M. Mabunda, L.A. Swart, M. Seedat, Magnitude and categories of pedestrian fatalities in South
Africa, Accid. Anal. Prev. 40 (2) (2008) 586–593.
19. M.F. Ballesteros, P.C. Dischinger, P. Langenberg, Pedestrian injuries and vehicle type in
Maryland1995–1999, Accid. Anal. Prev. 36 (1) (2004) 73–81.
20. R.J. Schneider, R.M. Ryznar, A.J. Khattak, An accident waiting to happen: a spatial approach to
proactive pedestrian planning, Accid. Anal. Prev. 36 (2004)193–211.
21. C. Lee, M. Abdel-Aty, Comprehensive analysis of vehicle-pedestrian crashes at intersections in
Florida, Accid. Anal. Prev. 37 (2005) 775–786.
22. Dipanjan Mukherjee, Sudeshna Mitra, A comprehensive study on identification of risk factors for
fatal pedestrian crashes at urban intersections in a developing country, Asian Transport Studies
(2020).
23. A.S. Al-Ghamdi, Pedestrian-vehicle crashes and analytical techniques for stratified contingency
tables, Accid. Anal. Prev. 34 (2002) 205–214.
24. A. Wazana, V.L. Rynard, P. Raina, P. Krueger, Are child pedestrians at increased risk of injury on
one-way compared to two-way streets? Can. J. Public Health 91(2000) 201–206.
25. Alexander Ganichev, Oksana Batishcheva, Evaluating the conflicts between vehicles and
pedestrians, Transport. Res. Procedia 50 (2020) (2020) 145–151.
26. Abbas Sheykhfardab, Farshidreza Haghig, Analysis of the occurrence and severity of vehicle-
pedestrian conflicts in marked and unmarked crosswalks through naturalistic driving study,
Transport. Res. F Traffic Psychol. Behav. 76 (2021) 178–192.
27. Johan Vos, Haneen Farah, Speed Behavior upon Approaching Freeway Curves, vol. 159, Accident
Analysis and Prevention, 2021.
28. Jun Xua, Yibing Lia, Reconstruction model of vehicle impact speed in pedestrian–vehicle accident,
Int. J. Impact Eng. 36 (6) (2009) 783–788.
29. Seyedmirsajad Mokhtarimousavi, Jason C. Anderson, Factors affecting injury severity in vehicle-
pedestrian crashes: A day-of-week analysis using random parameter ordered response models and
Artificial Neural Networks International Journal of Transportation Science and Technology 9 (2)
(2020) 100–115.
30. Liu, L., Xue, J., Mahdi, M., Mirbakhsh, S., Du, Q. (2023). Enhanced Automated Intersection
Control for Platooning AVs: A Fusion Model with Significant Traffic, Safety, and Environmental
Improvements. J Electrical Electron Eng, 2(4), 359-367.
31. Kadkhodayi, A., Jabeli, M., Aghdam, H., Mirbakhsh, S. (2023). Artificial Intelligence-Based Real-
Time Traffic Management. J Electrical Electron Eng, 2(4), 368-373.
32. Mirbakhsh, Ardeshir, Joyoung Lee, and Dejan Besenski. “Spring–mass–damper-based platooning
logic for automated vehicles.” Transportation research record 2677.5 (2023): 1264-1274.
33. Mirbakhsh, Ardeshir. A Self-Learning Intersection Control System for Connected and Automated
Vehicles. Diss. New Jersey Institute of Technology, 2022.
34. Mirbakhsh, Ardeshir. “How hospitals response to disasters; a conceptual deep reinforcement
learning approach.” (2023).
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| Volume | 11 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 01 | |
| Received | May 15, 2024 | |
| Accepted | June 27, 2024 | |
| Published | June 28, 2024 |
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