Traffic Order Analysis of Intersection Entrance Based on Aggressive Driving Behavior Data Using CatBoost and SHAP
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 149, Issue 6
Abstract
Analyzing road risks and developing targeted countermeasures are essential for a safe and orderly traffic flow. However, previous intersection safety analyses were conducted based on crash data. Little research has been conducted on surrogate safety measures based on risky driving behavior. In this study, categorical boosting (CatBoost) and Shapley additive explanation (SHAP) were used to analyze the impact of features on traffic order using a set of multisource data that include roadway geometry, signal control, and land use. The traffic data for intersection entrances in Beijing were collected from navigation systems, field investigations, and application programming interfaces. The model results showed that CatBoost exhibits a prediction accuracy of 83.5%, a recall of 83.5%, and an score of 81.1%. Moreover, the importance, total effects, main effects, and interaction effects of influence factors were analyzed by using SHAP. It was found that the congestion index (CI) has significant negative effects on traffic order. A larger number of lanes and more electronic traffic control were found to have a positive effect on traffic order. Intersection entrances with three-phase signals or off-peak intersection entrances helped increase traffic order. Moreover, a high green ratio for through vehicles can reduce the positive impact of CI on traffic order when the value of CI is 1.1–1.4, and the signal control scheme with a high left-turn green ratio would result in a safe and orderly traffic flow. The results from this study can be used for further studies on improving traffic safety at signalized intersections.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request. They are as follows:
All data of map information, behavior data, and congestion status, intersection attribute data, land-use data, and signal control data of the intersection entrances.
All models and code of CatBoost model, XGBoost model, and RF model.
Acknowledgments
This paper was supported by the National Natural Science Foundation of China (No. 52072012) and China Postdoctoral Science Foundation funded project (No. 2021M690272).
References
Abdel-Aty, M., and X. Wang. 2006. “Crash estimation at signalized intersections along corridors: Analyzing spatial effect and identifying significant factors.” Transp. Res. Rec. 1953 (1): 98–111. https://doi.org/10.1177/0361198106195300112.
AutoNavi Maps. 2021. “Amap.” Accessed June 20, 2021. https://www.amap.com/.
AutoNavi Maps. 2022. “Amap.” Accessed September 13, 2022. https://www.amap.com/.
Chen, K., T. Zhang, H. U. Yuxia, and X. Zheng. 2013. “Study on applicability of objective weighted model in the selection of engineering materials.” Mater. Rev. 2 (9): 104–106.
Dong, C., D. B. Clarke, X. Yan, A. Khattak, and B. Huang. 2014. “Multivariate random-parameters zero-inflated negative binomial regression model: An application to estimate crash frequencies at intersections.” Accid. Anal. Prev. 70 (Sep): 320–329. https://doi.org/10.1016/j.aap.2014.04.018.
Dorogush, A. V., V. Ershov, and A. Gulin. 2018. “CatBoost: Gradient boosting with categorical features support.” Preprint, submitted October 24, 2018. https://arxiv.org/abs/1810.11363.
Essa, M., and T. Sayed. 2019. “Full Bayesian conflict-based models for real time safety evaluation of signalized intersections.” Accid. Anal. Prev. 129 (Aug): 367–381. https://doi.org/10.1016/j.aap.2018.09.017.
Greibe, P. 2003. “Accident prediction models for urban roads.” Accid. Anal. Prev. 35 (2): 273–285. https://doi.org/10.1016/S0001-4575(02)00005-2.
Hwang, C.-L., Y.-J. Lai, and T.-Y. Liu. 1993. “A new approach for multiple objective decision making.” Comput. Oper. Res. 20 (8): 889–899. https://doi.org/10.1016/0305-0548(93)90109-V.
Jiang, X., M. Abdel-Aty, J. Hu, and L. Jeayoung. 2016. “Investigating macro-level hotzone identification and variable importance using big data: A random forest models approach.” Neurocomputing 181 (Mar): 53–63. https://doi.org/10.1016/j.neucom.2015.08.097.
Jilla, R. J. 1974. Effect of bicycle lanes on traffic flow. West Lafayette, IN: Purdue Univ. School of Civil Engineering.
Kidando, E., A. E. Kitali, B. Kutela, M. Ghorbanzadeh, A. Karaer, M. Koloushani, R. Moses, E. E. Ozguven, and T. Sando. 2021. “Prediction of vehicle occupants injury at signalized intersections using real-time traffic and signal data.” Accid. Anal. Prev. 149 (Jan): 105869. https://doi.org/10.1016/j.aap.2020.105869.
Lee, J., M. Abdel-Aty, and Q. Cai. 2017. “Intersection crash prediction modeling with macro-level data from various geographic units.” Accid. Anal. Prev. 102 (May): 213–226. https://doi.org/10.1016/j.aap.2017.03.009.
Li, X., D. Lord, Y. Zhang, and Y. Xie. 2008. “Predicting motor vehicle crashes using support vector machine models.” Accid. Anal. Prev. 40 (4): 1611–1618. https://doi.org/10.1016/j.aap.2008.04.010.
Lu, J., and Z. Cheng. 2019. “Research and development of road traffic network security risk identification.” J. Southeast Univ. (Nat. Sci. Ed.) 49 (2): 404–412. https://doi.org/10.3969/j.issn.1001-0505.2019.02.029.
Luan, S., M. Li, X. Li, and X. Ma. 2020. “Effects of built environment on bicycle wrong Way riding behavior: A data-driven approach.” Accid. Anal. Prev. 144 (Sep): 105613. https://doi.org/10.1016/j.aap.2020.105613.
Lundberg, S. M., and S.-I. Lee. 2017. “A unified approach to interpreting model predictions.” In Vol. 30 of Advances in neural information processing systems, 4765–4774. New York: Association for Computing Machinery.
Ma, Z., H. Zhang, I. Chien, J. Wang, and C. Dong. 2017. “Predicting expressway crash frequency using a random effect negative binomial model: A case study in China.” Accid. Anal. Prev. 98 (Jan): 214–222. https://doi.org/10.1016/j.aap.2016.10.012.
Machiani, S. G., and M. Abbas. 2016. “Safety surrogate histograms (SSH): A novel real-time safety assessment of dilemma zone related conflicts at signalized intersections.” Accid. Anal. Prev. 96 (Nov): 361–370. https://doi.org/10.1016/j.aap.2015.04.024.
Mannering, F., C. R. Bhat, V. Shankar, and M. Abdel-Aty. 2020. “Big data, traditional data and the tradeoffs between prediction and causality in highway-safety analysis.” Anal. Methods Accid. Res. 25 (Mar): 100113. https://doi.org/10.1016/j.amar.2020.100113.
Mihaita, A. S., Z. Liu, C. Cai, and M. A. Rizoiu. 2019. “Arterial incident duration prediction using a bi-level framework of extreme gradient-tree boosting.” Preprint, Submitted May 29, 2019. https://doi.org/10.48550/arXiv.1905.12254.
National Academies. 2013. Frontiers in massive data analysis. Washington, DC: National Academies Press.
Noland, R. B., and M. A. Quddus. 2005. “Congestion and safety: A spatial analysis of London.” Transp. Res. Part A: Policy Pract. 39 (7–9): 737–754. https://doi.org/10.1016/j.tra.2005.02.022.
Parsa, A. B., A. Movahedi, H. Taghipour, S. Derrible, and A. Moahammadian. 2020. “Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis.” Accid. Anal. Prev. 136 (Mar): 105405. https://doi.org/10.1016/j.aap.2019.105405.
Poch, M., and F. Mannering. 1996. “Negative binomial analysis of intersection-accident frequencies.” J. Transp. Eng. 122 (2): 105–113. https://doi.org/10.1061/(ASCE)0733-947X(1996)122:2(105).
Roshandel, S., Z. Zheng, and S. Washington. 2015. “Impact of real-time traffic characteristics on freeway crash occurrence: Systematic review and meta-analysis.” Accid. Anal. Prev. 79 (Jun): 198–211. https://doi.org/10.1016/j.aap.2015.03.013.
Shi, X. 2011. “Study on speed control and visual search of driving behavior at urban signal intersection.” Master’s thesis, Highway College, Chang’an Univ.
Stipancic, J., L. Miranda-Moreno, N. Saunier, and A. Labbe. 2019. “Network screening for large urban road networks: Using GPS data and surrogate measures to model crash frequency and severity.” Accid. Anal. Prev. 125 (Apr): 290–301. https://doi.org/10.1016/j.aap.2019.02.016.
Tang, J., J. Liang, C. Han, Z. Li, and H. Huang. 2019. “Crash injury severity analysis using a two-layer stacking framework.” Accid. Anal. Prev. 122 (Jan): 226–238. https://doi.org/10.1016/j.aap.2018.10.016.
Wang, X., J. Yuan, G. G. Schultz, and S. Fang. 2018. “Investigating the safety impact of roadway network features of suburban arterials in Shanghai.” Accid. Anal. Prev. 113 (Apr): 137–148. https://doi.org/10.1016/j.aap.2018.01.029.
Wu, K.-F., and P. P. Jovanis. 2012. “Crashes and crash-surrogate events: Exploratory modeling with naturalistic driving data.” Accid. Anal. Prev. 45 (Mar): 507–516. https://doi.org/10.1016/j.aap.2011.09.002.
Xie, K., X. Wang, H. Huang, and X. Chen. 2013. “Corridor-level signalized intersection safety analysis in Shanghai, China using Bayesian hierarchical models.” Accid. Anal. Prev. 50 (Jan): 25–33. https://doi.org/10.1016/j.aap.2012.10.003.
Yao, Y., X. Zhao, Y. Zhang, J. Ma, J. Rong, C. Bi, and Y. Wang. 2019. “Development of urban road order index based on driving behavior and speed variation.” Transp. Res. Rec. 2673 (7): 466–478. https://doi.org/10.1177/0361198119853576.
Yuan, J., and M. Abdel-Aty. 2018. “Approach-level real-time crash risk analysis for signalized intersections.” Accid. Anal. Prev. 119 (Oct): 274–289. https://doi.org/10.1016/j.aap.2018.07.031.
Zhao, X., Y. Yao, Y. Ding, J. Rong, Y. Su, and C. Bi. 2020. “Navigation-data-based risk evaluation method at intersection entrance.” J. Tongji Univ. (Nat. Sci.) 48 (12): 1733–1741.
Zheng, L., and T. Sayed. 2020. “A novel approach for real time crash prediction at signalized intersections.” Transp. Res. Part C: Emerging Technol. 117 (Aug): 102683. https://doi.org/10.1016/j.trc.2020.102683.
Zheng, Z., S. Ahn, and C. M. Monsere. 2010. “Impact of traffic oscillations on freeway crash occurrences.” Accid. Anal. Prev. 42 (2): 626–636. https://doi.org/10.1016/j.aap.2009.10.009.
Information & Authors
Information
Published In
Journal of Transportation Engineering, Part A: Systems
Volume 149 • Issue 6 • June 2023
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Feb 6, 2022
Accepted: Aug 1, 2022
Published online: Mar 25, 2023
Published in print: Jun 1, 2023
Discussion open until: Aug 25, 2023
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.
Cited by
- Hao Liu, Tao Wang, Wenyong Li, Xiaofei Ye, Quan Yuan, Lane-change intention recognition considering oncoming traffic: Novel insights revealed by advances in deep learning, Accident Analysis & Prevention, 10.1016/j.aap.2024.107476, 198, (107476), (2024).