Rural Road Safety Monitoring Using Crash Severity Predictive Models: A Case Study of Khorasan Razavi Province in Iran

Document Type : Research Article

Authors

1 MSc of transportation planning, Tarbiat Modares University, Tehran, Iran

2 Transportation Planning, Civil engineering department, Tarbiat Modares University, Tehran, Iran

3 Associate Professor of Transportation Engineering, Tarbiat Modares University, Tehran, Iran,

Abstract

The high severity of crashes caused by high-speed vehicles is one of the drawbacks of intercity transportation. Heavy costs are linked to severe crashes, including death, injuries, and damage to the road, road equipment, and vehicles, as well as major psychological effects. This study uses predictable traffic characteristics to forecast the severity of crashes on suburban highways using logit family models. As a result, the traffic data from the traffic detectors on the roadways is integrated with the crash data in the first stage before being evaluated and modeled. Spatial-temporal scenarios are combined with these two datasets. In this investigation, the ordered logit (OL) and multinominal logit (MNL) models were used. The data, which refers to the roads in Iran's north-eastern province of Khorasan Razavi, was collected over a four-year period. Results indicate that the MNL model performs better than the OL model with more significant traffic parameters.

Keywords

Main Subjects


[1] E. Rahimi, A. Shamshiripour, A. Samimi, A.K. Mohammadian, Investigating the injury severity of single-vehicle truck crashes in a developing country, Accident Analysis & Prevention, 137 (2020) 105444.
[2] A. Rasaizadi, M. Askari, Effect of family structure on urban areas modal split by using the life cycle concept, Int. J. Hum. Capital Urban Manage, 5(2) (2020) 165-174.
[3] L. Eboli, C. Forciniti, G. Mazzulla, Factors influencing accident severity: an analysis by road accident type, Transportation research procedia, 47 (2020) 449-456.
[4] A.M. Amiri, N. Nadimi, M. Askari, M. Shams, Developing an Accident Severity Model Based on Related Crash Type: Comparison of Four Commonly Used Discrete Choice Models, 2021.
[5] A. Iranitalab, A. Khattak, Comparison of four statistical and machine learning methods for crash severity prediction, Accident Analysis & Prevention, 108 (2017) 27-36.
[6] A. Rasaizadi, E. Sherafat, S. Seyedabrishami, Short-term prediction of traffic state, statistical approach versus machine learning approach.
[7] A. Rasaizadi, A. Ardestani, S. Seyedabrishami, Traffic management via traffic parameters prediction by using machine learning algorithms, International Journal of Human Capital in Urban Management, 6(1) (2021) 57-68.
[8] G. Azimi, A. Rahimi, H. Asgari, X. Jin, Severity analysis for large truck rollover crashes using a random parameter ordered logit model, Accident Analysis & Prevention, 135 (2020) 105355.
[9] J. Bao, Z. Yang, W. Zeng, X. Shi, Exploring the spatial impacts of human activities on urban traffic crashes using multi-source big data, Journal of Transport Geography, 94 (2021) 103118.
[10] K. Chebanyuk, O. Prasolenko, D. Burko, A. Galkin, O. Lobashov, A. Shevchenko, D.S. Usami, L. Persia, Pedestrians influence on the traffic flow parameters and road safety indicators at the pedestrian crossing, Transportation research procedia, 45 (2020) 858-865.
[11] H.M. Hammad, M. Ashraf, F. Abbas, H.F. Bakhat, S.A. Qaisrani, M. Mubeen, S. Fahad, M. Awais, Environmental factors affecting the frequency of road traffic accidents: a case study of sub-urban area of Pakistan, Environmental Science and Pollution Research, 26(12) (2019) 11674-11685.
[12] L. Komackova, M. Poliak, Factors affecting the road safety, Journal of Communication and Computer, 13 (2016) 146-152.
[13] J.W. Park, K.C. Lee, S.H. Sim, H.J. Jung, B.F. Spencer Jr, Traffic safety evaluation for railway bridges using expanded multisensor data fusion, Computerā€Aided Civil and Infrastructure Engineering, 31(10) (2016) 749-760.
[14] H. Sun, Q. Wang, P. Zhang, Y. Zhong, X. Yue, Spatialtemporal characteristics of tunnel traffic accidents in China from 2001 to present, Advances in Civil Engineering, 2019 (2019).
[15] M. Waseem, A. Ahmed, T.U. Saeed, Factors affecting motorcyclists’ injury severities: An empirical assessment using random parameters logit model with heterogeneity in means and variances, Accident Analysis & Prevention, 123 (2019) 12-19.
[16] Q. Zeng, W. Gu, X. Zhang, H. Wen, J. Lee, W. Hao, Analyzing freeway crash severity using a Bayesian spatial generalized ordered logit model with conditional autoregressive priors, Accident Analysis & Prevention, 127 (2019) 87-95.
[17] P.C. Anastasopoulos, F.L. Mannering, An empirical assessment of fixed and random parameter logit models using crash-and non-crash-specific injury data, Accident Analysis & Prevention, 43(3) (2011) 1140-1147.
[18] J.C. Milton, V.N. Shankar, F.L. Mannering, Highway accident severities and the mixed logit model: an exploratory empirical analysis, Accident Analysis & Prevention, 40(1) (2008) 260-266.
[19] Q. Wu, F. Chen, G. Zhang, X.C. Liu, H. Wang, S.M. Bogus, Mixed logit model-based driver injury severity investigations in single-and multi-vehicle crashes on rural two-lane highways, Accident Analysis & Prevention, 72 (2014) 105-115.
[20] X. Pei, S. Wong, N.-N. Sze, A joint-probability approach to crash prediction models, Accident Analysis & Prevention, 43(3) (2011) 1160-1166.
[21] D.A. Hensher, W.H. Greene, The mixed logit model: The state of practice and warnings for the unwary, Citeseer, 2002.
[22] A. Taheri, A. Rasaizadi, S. Seyedabrishami, Spatial-Temporal Analysis of Crash Severity: Multisource Data Fusion Approach, Discrete Dynamics in Nature and Society, 2022 (2022).
[23] A. Rasaizadi, M. Kermanshah, Mode choice and number of non-work stops during the commute: Application of a copula-based joint model, Scientia Iranica, 25(3) (2018) 1039-1047.
[24] R. Williams, Understanding and interpreting generalized ordered logit models, The Journal of Mathematical Sociology, 40(1) (2016) 7-20.
[25] L. Grilli, C. Rampichini, Ordered logit model, Encyclopedia of quality of life and well-being research,  (2014) 4510-4513.
[26] F. Jafari Shahdani, A. Rasaizadi, S. Seyedabrishami, The interaction between activity choice and duration: Application of Copula-based and Nested-logit models, Scientia Iranica,  (2020).
[27] J. Hausman, D. McFadden, Specification tests for the multinomial logit model, Econometrica: Journal of the econometric society,  (1984) 1219-1240.
[28] S. Seyedabrishami, A.R. Izadi, A Copula-Based Joint Model to Capture the Interaction between Mode and Departure Time Choices in Urban Trips, Transportation Research Procedia, 41 (2019) 722-730.
[29] S. Seyedabrishami, A.R. Izadi, H.S. Rayaprolu, R. Moeckel, Car ownership: A joint model for number of cars and fuel types, Transportation Research Procedia, 41 (2019).
[30] M. Zhu, X. Wang, J. Hu, Impact on car following behavior of a forward collision warning system with headway monitoring, Transportation research part C: emerging technologies, 111 (2020) 226-244.
[31] M. Rezapour, S.S. Wulff, K. Ksaibati, Examination of the severity of two-lane highway traffic barrier crashes using the mixed logit model, Journal of safety research, 70 (2019) 223-232.
[32] F. Chen, S. Chen, X. Ma, Analysis of hourly crash likelihood using unbalanced panel data mixed logit model and real-time driving environmental big data, Journal of safety research, 65 (2018) 153-159.
[33] N.A. Khan, N. Jhanjhi, S.N. Brohi, R.S.A. Usmani, A. Nayyar, Smart traffic monitoring system using unmanned aerial vehicles (UAVs), Computer Communications, 157 (2020) 434-443.
[34] L.S. Iyer, AI enabled applications towards intelligent transportation, Transportation Engineering, 5 (2021) 100083.