Journal of Transportation Research

Journal of Transportation Research

Traffic Crash Analysis and Evaluation Using Association Rules Mining among Vulnerable Users in Urban Streets

Document Type : Original Article

Authors
Navid Nadimi 1
SeyedSaber NaserAlavi 2
Vahid Khalifeh 3
Mehdi Aminian 4
1 Associate Professor, Civil Engineering Department, Shahid Bahonar University of Kerman, Kerman, Iran.
2 Assistant Professor, Civil Engineering Department, Shahid Bahonar University of Kerman, Kerman, Iran.
3 Assistant Professor, Faculty of Civil Engineering, Sirjan University of Technology, Sirjan, Iran.
4 M.Sc., Grad., Civil Engineering Department, Shahid Bahonar University of Kerman, Kerman, Iran.
10.22034/tri.2024.430837.3216
Abstract
In urban streets, severe traffic crashes mostly relate to vulnerable users such as pedestrians, and cyclists. Because of not being protected like vehicles for their occupants, the severity of such crashes is high. Now, in the world sustainable transportation (walking and bicycle) is highlighted, thus it is important to regard the safety of this group of users more. For this purpose, it is necessary to gain an insight from previous crashes of vulnerable users. Therefore it is possible to propose countermeasures to reduce the crashes and prevent future incidents. In this paper, association rules s used as a machine learning method to find the frequent patterns and rules in the crashes of pedestrians, and cyclists. Tehran (capital of Iran) as a city with high frequency of fatal crashes in the period of 2020 to 2021 is selected as the case study. The results indicated that most fatal crashes among vulnerable road users relate to pedestrians and motorcycles (43 and 42 percent). Most of those who died in pedestrian crashes were foreigners, homeless, and municipal workers (support 0.926). Most of the injuries related to the crash of vehicles with motorcycles (support 0.661). In injury crashes of motorcycles, road and environment factors did not have a great role. Nevertheless, human factor in the form of
non-necessary agility and acceleration was an effective variable (support 0.661). Engineering, education, regulation and enforcement can be the most effective countermeasures to reduce the severe crashes among vulnerable road users in Tehran.
Keywords
Vulnerable Users
Crash
Association Rule
Safety
Sustainable Transportation
Subjects
-شیخ فرد، عباس و حقیقی، فرشیدرضا. (1397). تحلیل عوامل بالقوۀ مؤثر در احتمال برخورد وسیلۀ‌نقلیه با عابران پیاده در محیط شهری. فصلنامه علمی راهور، 1397(24)، 198-179.
-صفارزاده، محمود، بروجردیان، امین میرزا و صاحبی، سینا (1393). ارائۀ مدل پیش بینی شدت جراحت ناشی از تصادفات موتورسواران در را­ه­های برو­ن شهری. فصلنامه علمی راهور، (11)، 161-139.
-قطب الدینی، مهدیه و ندیمی، نوید. (1400). ارزیابی عوامل مؤثر بر ایمنی موتورسیکلت‌سواران 1.  فصلنامه علمی راهور، 1400(37)، 35-9.
-Ahmed Hossain, Xiaoduan Sun, Raju Thapa, Md. Mahmud Hossain, Subasish Das, (2023). Exploring association of contributing factors to pedestrian fatal and severe injury crashes under dark-no-streetlight condition, IATSS Research, Vol. 47, Issue 2, 214-224.doi.org/10.1016/j.iatssr.2023.03.002.
-Aldred, R., García-Herrero, S., Anaya, E., Herrera, S., & Mariscal, M. Á. (2020). Cyclist injury severity in Spain: a Bayesian analysis of police road injury data focusing on involved vehicles and route environment. International Journal of Environmental Research and Public Health, 17(1), 96.
-Aldred, R., Goodman, A., Gulliver, J., & Woodcock, J. (2018). Cycling injury risk in London: A case-control study exploring the impact of cycle volumes, motor vehicle volumes, and road characteristics including speed limits. Accident Analysis & Prevention, 117, 75-84.
-Amoh-Gyimah, R., Saberi, M., & Sarvi, M. (2016). Macroscopic modeling of pedestrian and bicycle crashes: A cross-comparison of estimation methods. Accident Analysis & Prevention, 93, 147-159.
-Aziz, H. A., Ukkusuri, S. V., & Hasan, S. (2013). Exploring the determinants of pedestrian–vehicle crash severity in New York City. Accident Analysis & Prevention, 50, 1298–1309.
-Bíl, M., Bílová, M., & Müller, I. (2010). Critical factors in fatal collisions of adult cyclists with automobiles. Accident Analysis & Prevention, 42(6), 1632-1636.
-Boufous, S., de Rome, L., Senserrick, T., & Ivers, R. (2012). Risk factors for severe injury in cyclists involved in traffic crashes in Victoria, Australia. Accident Analysis & Prevention, 49, 404-409.
-Chang, D. (2008). National pedestrian crash report. Washington: National Center for Statistics and Analysis, National Highway Traffic Safety Administration, U.S. Department of. Transportation.
-Chang, L. Y., & Wang, H. W. (2006). Analysis of traffic injury severity: An application of non-parametric classification tree techniques. Accident Analysis & Prevention, 38(5), 1019-1027.
-Chenzhu Wang, Mohamed Abdel-Aty, Said M Easa, Fei Chen, Jianchuan Cheng, Arshad Jamal, (2024). Evaluating helmet-wearing of single-vehicle overspeeding motorcycle crashes: Insights from temporal instability in parsimonious pooled framework, Traffic Injury Prevention, Vol. 25, Issue 4, 2024, 623-630, doi.org/10.1080/15389588.2024.2331644
-Eluru, N., Bhat, C. R., & Hensher, D. A. (2008). A mixed generalized ordered response model for examining pedestrian and bicyclist injury severity level in traffic crashes. Accident Analysis & Prevention, 40(3), 1033-1054.
-Global Status Report on Road Safety (2018). World Health Organization, Paris.
-Jang, K., Park, S. H., Kang, S., Song, K. H., Kang, S., & Chung, S. (2013). Evaluation of pedestrian safety: Pedestrian crash hot spots and risk factors for injury severity. Transportation Research Record, 2393(1), 104–116.
Junhua Wang, Hao Song, Ting Fu, Molly Behan, Lei Jie, Yingxian He, Qiangqiang Shangguan (2022). Crash prediction for freeway work zones in real time: A comparison between Convolutional Neural Network and Binary Logistic Regression model, International Journal of Transportation Science and Technology, Vol. 11, Issue 3,484-495.
doi.org/10.1016/j.ijtst.2021.06.002
-Kaplan, S., Vavatsoulas, K., & Prato, C. G. (2014). Aggravating and mitigating factors associated with cyclist injury severity in Denmark. Journal of Safety Research, 50, 75-82.
-Khayesi, M. (2020). Vulnerable road users or vulnerable transport planning? Frontiers in Sustainable Cities, 2, 25
-Kunnawee Kanitpong, Auearree Jensupakarn, Pathumporn Dabsomsri, Kannika Issalakul, (2024). Characteristics of motorcycle crashes in Thailand and factors affecting crash severity: Evidence from in-depth crash investigation, Transportation Engineering, Vol. 16, 2024, 100227, doi.org/10.1016/j.treng.2024.100227
-Luciano Lalika, Angela E. Kitali, Henrick J. Haule, Emmanuel Kidando, Thobias Sando, Priyanka Alluri. (2022). What are the leading causes of fatal and severe injury crashes involving older pedestrian? Evidence from Bayesian network model, Journal of Safety Research, Vol.80, 281-292. doi.org/10.1016/j.jsr.2021.12.011
-Mabunda, M. M., Swart, L. A., & Seedat, M. (2008). Magnitude and categories of pedestrian fatalities in South Africa. Accident Analysis & Prevention, 40, 586–593.
-Manan, M. M. A., Várhelyi, A., Çelik, A. K., & Hashim, H. H. (2018). Road characteristics and environment factors associated with motorcycle fatal crashes in Malaysia. IATSS Research, 42(4), 207–220.
-Mohamed, M. G., Saunier, N., Miranda-Moreno, L. F., & Ukkusuri, S. V. (2013). A clustering regression approach: A comprehensive injury severity analysis of pedestrian–vehicle crashes in New York, US and Montreal, Canada. Safety Science, 54, 27-37.
-Moore, D. N., Schneider IV, W. H., Savolainen, P. T., & Farzaneh, M. (2011). Mixed logit analysis of bicyclist injury severity resulting from motor vehicle crashes at intersection and non-intersection locations. Accident Analysis & Prevention, 43(3), 621-630.
-Morrison, C. N., Thompson, J., Kondo, M. C., & Beck, B. (2019). On-road bicycle lane types, roadway characteristics, and risks for bicycle crashes. Accident Analysis & Prevention, 123, 123-131.
-Oikawa, S., Matsui, Y., Doi, T., & Sakurai, T. (2016). Relation between vehicle travel velocity and pedestrian injury risk in different age groups for the design of a pedestrian detection system. Safety Science, 82, 361–367.
-Pei-Fen Kuo, Umroh Dian Sulistyah, I Gede Brawiswa Putra, Dominique Lord.  (2023). Exploring the spatial relationship of e-bike and motorcycle crashes: Implications for risk reduction, Journal of Safety Research, Vol. 88, 199-216, doi.org/10.1016/j.jsr.2023.11.007
-Persaud, N., Coleman, E., Zwolakowski, D., Lauwers, B., & Cass, D. (2012). Nonuse of bicycle helmets and risk of fatal head injury: a proportional mortality, case–control study. Cmaj, 184(17), E921-E923.
-Salum, J. H., Kitali, A. E., Bwire, H., Sando, T., & Alluri, P. (2019). Severity of motorcycle crashes in Dar es Salaam, Tanzania.
Traffic Injury Prevention, 20(2), 189–195.
-Sarkar, S., Tay, R., & Hunt, J. D. (2011). Logistic regression model of risk of fatality in vehicle–pedestrian crashes on national highways in Bangladesh. Transportation Research Record, 2264(1), 128–137.
-Senserrick, T., Boafous, S. D., Rome, L., Ivers, R., & Stevenson, M. (2014). Detailed analysis of pedestrian casualty collisions in Victoria, Australia. Traffic Injury Prevention, 15, 197–205.
-Seyed Alireza Samerei, Kayvan Aghabayk, Nirajan Shiwakoti, Amin Mohammadi  (2021). Using latent class clustering and binary logistic regression to model Australian cyclist injury severity in motor vehicle–bicycle crashes, Journal of Safety Research, Vol. 79, 246-256. doi.org/10.1016/j.jsr.2021.09.005
-Shaojie Liu, Yang Li, Wei, David, Fan, (2022). Mixed logit model based diagnostic analysis of bicycle-vehicle crashes at daytime and nighttime, International Journal of Transportation Science and Technology, Vol. 11, Issue 4, 738-751, ISSN 2046-0430. doi.org/10.1016/j.ijtst.2021.10.001
-Sivasankaran, S. K., & Balasubramanian, V. (2020). Exploring the severity of bicycle–vehicle crashes using latent class clustering approach in India. Journal of Safety Research, 72, 127-138.
-Sourav De, SandipDey, Surbhi Bhatia, Siddhartha BhattacharyyaChapter. (2022). An introduction to data mining in social networks, Hybrid Computational Intelligence for Pattern Analysis, Advanced Data Mining Tools and Methods for Social Computing, Academic Press, 1-25. ISBN 9780323857086. doi.org/10.1016/B978-0-32-385708-6.00008-4
-Sun, M., Sun, X., & Shan, D. (2019). Pedestrian crash analysis with latent class clustering method. Accident Analysis & Prevention, 124, 50-57.
-Sze, N. N., & Wong, S. C. (2007). Diagnostic analysis of the logistic model for pedestrian injury severity in traffic crashes. Accident Analysis & Prevention, 39(6), 1267-1278.
-Tay, R., Choi, J., Kattan, L., & Khan, A. (2011). A multinomial logit model of pedestrian–vehicle crash severity. International Journal of Sustainable Transportation, 5(4), 233–249.
-Tulu, G. S., Washington, S., Haque, M. M., & King, M. J. (2017). Injury severity of pedestrians involved in road traffic crashes in Addis Ababa, Ethiopia. Journal of Transportation Safety & Security, 9(sup1), 47-66.
-Wall, S. P., Lee, D. C., Frangos, S. G., Sethi, M., Heyer, J. H., Ayoung-Chee, P., & DiMaggio, C. J. (2016). The effect of sharrows, painted bicycle lanes and physically protected paths on the severity of bicycle injuries caused by motor vehicles. Safety, 2(4), 26.
-Wang, C., Lu, L., & Lu, J. (2015). Statistical analysis of bicyclists’ injury severity at unsignalized intersections. Traffic Injury Prevention, 16(5), 507-512.
-Wang, J., Huang, H., Xu, P., Xie, S., & Wong, S. C. (2020). Random parameter probit models to analyze pedestrian red-light violations and injury severity in pedestrian–motor vehicle crashes at signalized crossings. Journal of Transportation Safety & Security, 12(6), 818–837.
-Waseem, M., Ahmed, A., & Saeed, T. U. (2019). Factors affecting motorcyclists’ injury severities: An empirical assessment using random parameters logit model with heterogeneity in means and variances. Accident Analysis and Prevention, 123, 12–19.
-Wu, C., Yao, L., & Zhang, K. (2012). The red-light running behavior of electric bike riders and cyclists at urban intersections in China: an observational study. Accident Analysis & Prevention, 49, 186-192.
-Yan, X., Ma, M., Huang, H., Abdel-Aty, M., & Wu, C. (2011). Motor vehicle–bicycle crashes in Beijing: Irregular maneuvers, crash patterns, and injury severity. Accident Analysis & Prevention, 43(5), 1751-1758.
-Zhai, X., Huang, H., Sze, N. N., Song, Z., & KwongHon, K. (2019). Diagnostic analysis of the effects of weather condition on pedestrian crash severity. Accident Analysis & Prevention, 122, 318–324.