Investigating the Impact of Driver and Vehicle Characteristics on the Risk of Red-Light Running Crashes

Document Type : Research Article

Authors

1 Associate professor, School of Civil Engineering, Iran University of Science & Technology, Tehran, Iran

2 Ph.D. candidate, School of Civil Engineering, Iran University of Science & Technology, Tehran, Iran

3 Master's degree, School of Civil Engineering, Iran University of Science & Technology, Tehran, Iran

Abstract

Red-light running is one of the prevalent sights at signalized intersections that vehicles pass without caring for the light. A red-light runner ventures not only his life but also the safety of other road users. This study aims to identify the driver and vehicle characteristics affecting red-light running crash occurrence risk at signalized intersections of Iran. The study's methodology was based on the quasi-induced exposure concept and logistic regression model for ten independent variables and one binary target variable of "driver status." The statistical population included 12445 red-light running crashes from 2012 to 2016. The results demonstrated that vehicle type, residence, license type, and education level affect drivers' fault status in these crashes. Based on the logistic regression model, truck and emergency vehicles, foreign drivers, and type 2 driving licenses increase the risk of drivers being at fault. However, the academic education level of drivers decreases at-fault risk. Finally, some countermeasures were suggested for reducing the risk of red-light running crashes.

Keywords

Main Subjects

References

 [1] I.R.a.T.A. Database", International Road and Traffic Accident Database (IRTAD), Road safety annual report, 2018.
[2] Road Traffic Injuries in Iran and their Prevention, A Worrying Picture, in.
[3] FHWA, in, federal highway administration, 2017.
[4] G. Zhang, Y. Tan, R.-C. Jou, Factors influencing traffic signal violations by car drivers, cyclists, and pedestrians: A case study from Guangdong, China, Transportation research part F: traffic psychology and behaviour, 42 (2016) 205-216.
[5] A. Jensupakarn, K. Kanitpong, Influences of motorcycle rider and driver characteristics and road environment on red light running behavior at signalized intersection, Accident Analysis and Prevention,  (2018).
[6] J. Wang, H. Huang, P. Xu, S. Xie, S. Wong, 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,  (2019) 1-20.
[7] N. Elmitiny, X. Yan, E. Radwan, C. Russo, D. Nashar, Classification analysis of driver’s stop/go decision and red-light running violation, Accident Analysis and Prevention,  (2009).
[8] M. Li, X. Chen, X. Lin, D. Xu, Y. Wang, Connected vehicle-based red-light running prediction for adaptive signalized intersections, Journal of Intelligent Transportation Systems, 22(3) (2018) 229-243.
[9] H. Park, N. Pugh, Generalized Estimating Equation Model Based Recursive Partitioning: Application to Distracted Driving, Journal of Advanced Transportation, 2018 (2018).
[10] Y. Zhang, X. Yan, X. Li, J.W. 3, V.V. Dixit, Red-Light-Running Crashes’ Classification, Comparison, and Risk Analysis Based on General Estimates System (GES) Crash Databas, Invironmental research and public health,  (2018).
[11] V. Sharifianjazi, H. Nassiri, Adverse effect of red light violation (RLV) in urban signalized intersections in Iran, International Journal of Civil Engineering, 15(8) (2017) 1107-1116.
[12] A.C. Wolfe, The concept of exposure to the risk of a road traffic accident and an overview of exposure data collection methods, Accident Analysis & Prevention, 14(5) (1982).
[13] D.J.T.R.R. Lord, Application of accident prediction models for computation of accident risk on transportation networks, 1784(1) (2002) 17-26.
[14] N. Stamatiadis, J. Deacon, Quasi induced exposure:Methodology and insight, Accident Analysis and Prevention 29 (1996).
[15] N. Stamatiadis, J.A. Deacon, Trends in highway safety: effects of an aging population on accident propensity, Accident Analysis & Prevention, 27(4) (1995) 443-459.
[16] H. Huang, H.C. Chin, Disaggregate Propensity Study on Red Light Running Crashes Using Quasi-Induced Exposure Method, Journal of transportation engineering (2009).
[17] S. Liew, R. Hamidun, N.F.M. Soid, Differences of driving experience and gender on traffic offences among Malaysian motorists, in:  MATEC Web of Conferences, EDP Sciences, 2017, pp. 08016.
[18] B.H. Al-Omari, H.R. Al-Masaeid, Red light violations at rural and suburban signalized intersections in Jordan, Traffic injury prevention, 4(2) (2003) 169-172.
[19] Z.R. Doerzaph, R. Bhagavathula, Identification of factors related to violation propensity: mining the data of the Franklin intersections, Virginia Tech Transportation Institute: Blacksburg,  (2011).
[20] S.S. Washburn, K.G. Courage, Investigation of Red Light Running Factors, Southeast Transportation Center The University of Tennessee Knoxville, Tennessee, Department of Civil and Coastal Engineering University of Florida, 2004.
[21] P. Intini, P. Colonna, E.O. Ryeng, Route familiarity in road safety: A literature review and an identification proposal, Transportation research part F: traffic psychology and behaviour, 62 (2019) 651-671.
[22] P. Intini, N. Berloco, P. Colonna, V. Ranieri, E. Ryeng, Exploring the relationships between drivers’ familiarity and two-lane rural road accidents. A multi-level study, Accident Analysis & Prevention, 111 (2018) 280-296.
[23] J. Wu, H. Xu, The influence of road familiarity on distracted driving activities and driving operation using naturalistic driving study data, Transportation research part F: traffic psychology and behaviour, 52 (2018) 75-85.
[24] C. Liu, T.J. Ye, Run-off-road crashes: An on-scene perspective, 2011.
[25] G. Yannis, J. Golias, E. Papadimitriou, Accident risk of foreign drivers in various road environments, Journal of safety research, 38(4) (2007) 471-480.
[26] K. Yoh, T. Okamoto, H. Inoi, K. Doi, Comparative study on foreign drivers' characteristics using traffic violation and accident statistics in Japan, IATSS research, 41(2) (2017) 94-105.
[27] A.H. Young, A.K. Mackenzie, R.L. Davies, D. Crundall, Familiarity breeds contempt for the road ahead: The real-world effects of route repetition on visual attention in an expert driver, Transportation research part F: traffic psychology and behaviour, 57 (2018) 4-9.
[28] W. Jin, Y. Deng, H. Jiang, Q. Xie, W. Shen, W. Han, Latent class analysis of accident risks in usage-based insurance: Evidence from Beijing, Accident Analysis & Prevention, 115 (2018) 79-88.
[29] X. Yan, E. Radwan, E. Birriel, Analysis of Red Light Running Crashes Based on Quasi-Induced Exposure and Multiple Logistic Regression Method, Transportation Research Board,  (2005).
[30] V. Martínez-Ruiza, P. Lardelli-Clareta, E. Jiménez-Mejíasa, C. Amezcua-Prietoa, J.J. Jiménez-Moleóna, J.d.D.L.d. Castillo, Risk factors for causing road crashes involving cyclists: An application of a quasi-induced exposure method, Accident Analysis and Prevention,  (2012).
[31] M. Vujanić, D. Pešić, B. Antić, N. Marković, selection and assessment of the relevant data for reducing the number of red-light running,  (2016).
[32] H. heydari, M. khosravi, Investigating the relationship between socioeconomic status and respect for traffic rules, Rahvar, 13(35) (1395) 129-155.
[33] Global status report on road safety, world health organization, World Health Organization, 2018.
 
 
Amirkabir Journal of Civil Engineering
Volume 53, Issue 1
April 2021
Pages 89-106

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