260
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

Investigating the impacts of driver’s risky driving behavior on traffic crash risk detection model

, , ORCID Icon, , &

References

  • Abdel-Aty, M. A., Hassan, H. M., Ahmed, M., & Al-Ghamdi, A. S. (2012). Real-time prediction of visibility related crashes. Transportation Research Part C: Emerging Technologies, 24, 288–298. doi:10.1016/j.trc.2012.04.001
  • Abdel-Aty, M., Uddin, N., Pande, A., Abdalla, M. F., & Hsia, L. (2004). Predicting freeway crashes from loop detector data by matched case-control logistic regression. Transportation Research Record: Journal of the Transportation Research Board, 1897(1), 88–95. doi:10.3141/1897-12
  • Adanu, E. K., Jones, S., Abhay, L., & Rahman, M. (2022). Examining the who, what, and how of risky driving related crashes in residential areas. Journal of Urban Mobility, 2, 100024. doi:10.1016/j.urbmob.2022.100024
  • Adanu, E. K., Penmetsa, P., Wood, D., & Jones, S. L. (2019). Incorporating systems thinking approach in a multilevel framework for human-centered crash analysis. Transportation Research Interdisciplinary Perspectives, 2, 100031. doi:10.1016/j.trip.2019.100031
  • Basso, F., Basso, L. J., Bravo, F., & Pezoa, R. (2018). Real-time crash prediction in an urban expressway using disaggregated data. Transportation Research Part C: Emerging Technologies, 86, 202–219. doi:10.1016/j.trc.2017.11.014
  • Cai, Q., Abdel-Aty, M., Yuan, J., Lee, J., & Wu, Y. (2020). Real-time crash prediction on expressways using deep generative models. Transportation Research Part C: Emerging Technologies, 117, 102697. doi:10.1016/j.trc.2020.102697
  • Chen, F., Chen, S., & Ma, X. (2018). Analysis of hourly crash likelihood using unbalanced panel data mixed logit model and real-time driving environmental big data. Journal of Safety Research, 65, 153–159. doi:10.1016/j.jsr.2018.02.010
  • Chen, Z., & Park, B. B. (2020). Preceding vehicle identification for cooperative adaptive cruise control platoon forming. IEEE Transactions on Intelligent Transportation Systems, 21(1), 308–320. doi:10.1109/TITS.2019.2891353
  • Dewulf, B., Neutens, T., Vanlommel, M., Logghe, S., De Maeyer, P., Witlox, F., De Weerdt, Y., & Van de Weghe, N. (2015). Examining commuting patterns using Floating Car Data and circular statistics: Exploring the use of new methods and visualizations to study travel times. Journal of Transport Geography, 48, 41–51. doi:10.1016/j.jtrangeo.2015.08.006
  • Du, X., Shen, Y., Chang, R., & Ma, J. (2018). The exceptionists of Chinese roads: The effect of road situations and ethical positions on driver aggression. Transportation Research Part F: Traffic Psychology and Behaviour, 58, 719–729. doi:10.1016/j.trf.2018.07.008
  • Dutta, N., & Fontaine, M. D. (2019). Improving freeway segment crash prediction models by including disaggregate speed data from different sources. Accident Analysis and Prevention, 132(August), 105253. doi:10.1016/j.aap.2019.07.029
  • Fu, T., Miranda-Moreno, L., & Saunier, N. (2018). A novel framework to evaluate pedestrian safety at non-signalized locations. Accident Analysis and Prevention, 111(March 2017), 23–33. doi:10.1016/j.aap.2017.11.015
  • Fu, T., Yu, X., Xiong, B., Jiang, C., Wang, J., Shangguan, Q., & Xu, W. (2022). A method in modeling interactive pedestrian crossing and driver yielding decisions during their interactions at intersections. Transportation Research Part F: Traffic Psychology and Behaviour, 88(May), 37–53. doi:10.1016/j.trf.2022.05.005
  • Guo, M., Zhao, X., Yao, Y., Bi, C., & Su, Y. (2022). Application of risky driving behavior in crash detection and analysis. Physica A: Statistical Mechanics and Its Applications, 591, 126808. doi:10.1016/j.physa.2021.126808
  • Guo, M., Zhao, X., Yao, Y., Yan, P., Su, Y., Bi, C., & Wu, D. (2021). A study of freeway crash risk prediction and interpretation based on risky driving behavior and traffic flow data. Accident Analysis and Prevention, 160(August), 106328. doi:10.1016/j.aap.2021.106328
  • Haleem, K., & Gan, A. (2015). Contributing factors of crash injury severity at public highway-railroad grade crossings in the U.S. Journal of Safety Research, 53, 23–29. doi:10.1016/j.jsr.2015.03.005
  • Hao, H., Li, Y., Medina, A., Gibbons, R. B., & Wang, L. (2020). Understanding crashes involving roadway objects with SHRP 2 naturalistic driving study data. Journal of Safety Research, 73, 199–209. doi:10.1016/j.jsr.2020.03.005
  • Haque, N., Hadiuzzaman, M., Rahman, F., & Siam, M. R. K. (2020). Real-time motion trajectory based head-on crash probability estimation on two-lane undivided highway. Journal of Transportation Safety & Security, 12(10), 1312–1337. doi:10.1080/19439962.2019.1597001
  • Hassan, H. M., & Abdel-Aty, M. A. (2013). Predicting reduced visibility related crashes on freeways using real-time traffic flow data. Journal of Safety Research, 45, 29–36. doi:10.1016/j.jsr.2012.12.004
  • Hossain, M., & Muromachi, Y. (2010). Development of a real-time crash prediction model for urban expressway. Journal of the Eastern Asia Society for Transportation Studies, 8(2003), 2092–2107.
  • Jiang, F., Yuen, K. K. R., & Lee, E. W. M. (2020). A long short-term memory-based framework for crash detection on freeways with traffic data of different temporal resolutions. Accident Analysis and Prevention, 141(March), 105520. doi:10.1016/j.aap.2020.105520
  • Khoda Bakhshi, A., & Ahmed, M. M. (2022). Real-time crash prediction for a long low-traffic volume corridor using corrected-impurity importance and semi-parametric generalized additive model. Journal of Transportation Safety & Security, 14(7), 1165–1200. doi:10.1080/19439962.2021.1898069
  • Kidando, E., Kitali, A. E., Kutela, B., Ghorbanzadeh, M., Karaer, A., Koloushani, M., Moses, R., Ozguven, E. E., & Sando, T. (2021). Prediction of vehicle occupants injury at signalized intersections using real-time traffic and signal data. Accident Analysis and Prevention, 149, 105869. doi:10.1016/j.aap.2020.105869
  • Li, G., Lai, W., Sui, X., Li, X., Qu, X., Zhang, T., & Li, Y. (2020). Influence of traffic congestion on driver behavior in post-congestion driving. Accident Analysis and Prevention, 141, 105508. doi:10.1016/j.aap.2020.105508
  • Li, P., & Abdel-Aty, M. (2022). A hybrid machine learning model for predicting Real-Time secondary crash likelihood. Accident Analysis and Prevention, 165, 106504. doi:10.1016/j.aap.2021.106504
  • Li, Z., Wang, W., Chen, R., Liu, P., & Xu, C. (2013). Evaluation of the impacts of speed variation on freeway traffic collisions in various traffic states. Traffic Injury Prevention, 14(8), 861–866. doi:10.1080/15389588.2013.775433
  • Liu, M., & Chen, Y. (2017). Predicting real-time crash risk for urban expressways in China. Mathematical Problems in Engineering, 2017, 1–10. doi:10.1155/2017/6263726
  • Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 2017-December, 4766–4775.
  • Ma, X., Lu, J., Liu, X., & Qu, W. (2022). A genetic programming approach for real-time crash prediction to solve trade-off between interpretability and accuracy. Journal of Transportation Safety & Security, doi:10.1080/19439962.2022.2076756
  • Oh, J. S., Oh, C., Ritchie, S. G., & Chang, M. (2005). Real-time estimation of accident likelihood for safety enhancement. Journal of Transportation Engineering, 131(5), 358–363. doi:10.1061/(ASCE)0733-947X(2005)131:5(358)
  • Precht, L., Keinath, A., & Krems, J. F. (2017). Identifying the main factors contributing to driving errors and traffic violations – Results from naturalistic driving data. Transportation Research Part F: Traffic Psychology and Behaviour, 49, 49–92. doi:10.1016/j.trf.2017.06.002
  • Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). Catboost: Unbiased boosting with categorical features. Advances in Neural Information Processing Systems, 2018-December(Section 4), 6638–6648.
  • Pu, Z., Li, Z., Ke, R., Hua, X., & Wang, Y. (2020). Evaluating the nonlinear correlation between vertical curve features and crash frequency on highways using random forests. Journal of Transportation Engineering, Part A: Systems, 146(10), 1–13. doi:10.1061/JTEPBS.0000410
  • Rahmani, M., Jenelius, E., & Koutsopoulos, H. N. (2014). Floating car and camera data fusion for non-parametric route travel time estimation. Procedia Computer Science, 37, 390–395. doi:10.1016/j.procs.2014.08.058
  • Rezapour, M., Moomen, M., & Ksaibati, K. (2019). Ordered logistic models of influencing factors on crash injury severity of single and multiple-vehicle downgrade crashes: A case study in Wyoming. Journal of Safety Research, 68, 107–118. doi:10.1016/j.jsr.2018.12.006
  • Shi, Q., & Abdel-Aty, M. (2015). Big Data applications in real-time traffic operation and safety monitoring and improvement on urban expressways. Transportation Research Part C: Emerging Technologies, 58, 380–394. doi:10.1016/j.trc.2015.02.022
  • Shi, X., Wong, Y. D., Li, M. Z. F., Palanisamy, C., & Chai, C. (2019). A feature learning approach based on XGBoost for driving assessment and risk prediction. Accident Analysis and Prevention, 129, 170–179. doi:10.1016/j.aap.2019.05.005
  • Sobreira, L. T. P., & Cunto, F. (2021). Disaggregated traffic conditions and road crashes in urban signalized intersections. Journal of Safety Research, 77, 202–211. doi:10.1016/j.jsr.2021.03.003
  • Song, Y., Kou, S., & Wang, C. (2021). Modeling crash severity by considering risk indicators of driver and roadway: A Bayesian network approach. Journal of Safety Research, 76, 64–72. doi:10.1016/j.jsr.2020.11.006
  • Stipancic, J., Miranda-Moreno, L., Saunier, N., & Labbe, A. (2019). Network screening for large urban road networks: Using GPS data and surrogate measures to model crash frequency and severity. Accident Analysis and Prevention, 125, 290–301. doi:10.1016/j.aap.2019.02.016
  • Sun, J., & Sun, J. (2015). A dynamic Bayesian network model for real-time crash prediction using traffic speed conditions data. Transportation Research Part C: Emerging Technologies, 54, 176–186. doi:10.1016/j.trc.2015.03.006
  • van Beinum, A., Farah, H., Wegman, F., & Hoogendoorn, S. (2018). Driving behaviour at motorway ramps and weaving segments based on empirical trajectory data. Transportation Research Part C: Emerging Technologies, 92, 426–441. doi:10.1016/j.trc.2018.05.018
  • Wang, C., Quddus, M. A., & Ison, S. G. (2009). Impact of traffic congestion on road accidents: A spatial analysis of the M25 motorway in England. Accident Analysis and Prevention, 41(4), 798–808. doi:10.1016/j.aap.2009.04.002
  • Wang, J., Xu, W., Fu, T., & Jiang, R. (2022). Recognition of trip-based aggressive driving: A system integrated with gaussian mixture model structured of factor-analysis, and hierarchical clustering. IEEE Transactions on Intelligent Transportation Systems, 23(11), 20442–20451. doi:10.1109/TITS.2022.3175528
  • Wang, X., Liu, Q., Guo, F., Fang, S., Xu, X., & Chen, X. (2022). Causation analysis of crashes and near crashes using naturalistic driving data. Accident Analysis and Prevention, 177, 106821. doi:10.1016/j.aap.2022.106821
  • Wen, X., Xie, Y., Wu, L., & Jiang, L. (2021). Quantifying and comparing the effects of key risk factors on various types of roadway segment crashes with LightGBM and SHAP. Accident Analysis and Prevention, 159, 106261. doi:10.1016/j.aap.2021.106261
  • Xu, C., Liu, P., Wang, W., & Li, Z. (2012). Evaluation of the impacts of traffic states on crash risks on freeways. Accident Analysis and Prevention, 47, 162–171. doi:10.1016/j.aap.2012.01.020
  • Xu, C., Wang, W., Liu, P., & Zhang, F. (2015). Development of a real-time crash risk prediction model incorporating the various crash mechanisms across different traffic states. Traffic Injury Prevention, 16(1), 28–35. doi:10.1080/15389588.2014.909036
  • Yang, C., Chen, M., & Yuan, Q. (2021). The application of XGBoost and SHAP to examining the factors in freight truck-related crashes: An exploratory analysis. Accident Analysis and Prevention, 158, 106153. doi:10.1016/j.aap.2021.106153
  • Yang, K., Wang, X., & Yu, R. (2018). A Bayesian dynamic updating approach for urban expressway real-time crash risk evaluation. Transportation Research Part C: Emerging Technologies, 96, 192–207. doi:10.1016/j.trc.2018.09.020
  • Yu, R., Wang, X., & Abdel-Aty, M. (2017). A hybrid latent class analysis modeling approach to analyze urban expressway crash risk. Accident Analysis and Prevention, 101, 37–43. doi:10.1016/j.aap.2017.02.002
  • Yuan, J., Abdel-Aty, M., Gong, Y., & Cai, Q. (2019). Real-time crash risk prediction using long short-term memory recurrent neural network. Transportation Research Record: Journal of the Transportation Research Board, 2673(4), 314–326. doi:10.1177/0361198119840611
  • Zhang, S., & Abdel-Aty, M. (2022). Real-time crash potential prediction on freeways using connected vehicle data. Analytic Methods in Accident Research, 36, 100239. doi:10.1016/j.amar.2022.100239
  • Zhang, W., Zhang, X., Feng, Z., Liu, J., Zhou, M., & Wang, K. (2018). The fitness-to-drive of shift-work taxi drivers with obstructive sleep apnea: An investigation of self-reported driver behavior and skill. Transportation Research Part F: Traffic Psychology and Behaviour, 59, 545–554. doi:10.1016/j.trf.2017.12.004
  • Zheng, M., Li, T., Zhu, R., Chen, J., Ma, Z., Tang, M., Cui, Z., & Wang, Z. (2019). Traffic accident’s severity prediction: A deep-learning approach-based CNN network. IEEE Access, 7, 39897–39910. doi:10.1109/ACCESS.2019.2903319

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.