A Multi-Level Risk Framework for Driving Safety Assessment Based on Vehicle Trajectory


  • Xiaoxia Xiong School of Automotive and Traffic Engineering, Jiangsu University
  • Yu He School of Automotive and Traffic Engineering, Jiangsu University
  • Xiang Gao School of Automotive and Traffic Engineering, Jiangsu University
  • Yeling Zhao School of Finance and Economics, Jiangsu University




traffic safety, multi-level risk, safety indictor, SEM, vehicle trajectory


Few existing research studies have explored the relationship of road section level, local area level and vehicle level risks within the highway traffic safety system, which can be important to the formation of an effective risk event prediction. This paper proposes a framework of multi-level risks described by a set of carefully selected or designed indicators. The interrelationship among these latent multi-level risks and their observable indicators are explored based on vehicle trajectory data using the structural equation model (SEM). The results show that there exists significant positive correlation between the latent risk constructs that each have adequate convergent validity, and it is difficult to completely separate the local traffic level risk from both the road section level risk and vehicle level risk. The local and road level indicators are also found to be of more importance when risk prediction time gets earlier based on feature importance scoring of the LightGBM. The proposed conceptual multi-level indicator based latent risk framework generally fits with the observed results and emphasises the importance of including multi-level indicators for risk event prediction in the future.


World Health Organization. Road Traffic Injuries. 2021. http://www.who.int/mediacentre/factsheets/fs358/en/.

Shunying Z, et al. Review of research on traffic conflict techniques. China Journal of Highway and Transport. 2020;33(2): 15-33.

Wang X, et al. Effect of daily car-following behaviors on urban roadway rear-end crashes and near-crashes: A naturalistic driving study. Accident Analysis and Prevention. 2022;164(November 2021): 106502. doi: 10.1016/j.aap.2021.106502.

Chen S, et al. Risky driving behavior recognition based on vehicle trajectory. International Journal of Environmental Research and Public Health. 2021;18(23). doi: 10.3390/ijerph182312373.

Bastos JT, et al. Naturalistic driving study in Brazil: An analysis of mobile phone use behavior while driving. International Journal of Environmental Research and Public Health. 2020;17(17): 1-14. doi: 10.3390/ijerph17176412.

Mahmud SMS et al. Application of proximal surrogate indicators for safety evaluation: A review of recent developments and research needs. IATSS Research. 2017;41(4): 153-163. doi: 10.1016/j.iatssr.2017.02.001.

Wang C, et al. A review of surrogate safety measures and their applications in connected and automated vehicles safety modeling. Accident Analysis and Prevention. 2021;157(May): 106157. doi: 10.1016/j.aap.2021.106157.

Hayward JC. Near-miss determination through use of a scale of danger. Highway Research Record. 1972;384: 24-34.

Michael PG, Leeming FC, Dwyer WO. Headway on urban streets: Observational data and an intervention to decrease tailgating. Transportation Research Part F: Traffic Psychology and Behaviour. 2000;3(2): 55-64. doi: 10.1016/S1369-8478(00)00015-2.

Allen BL, Shin BT, Cooper PJ. Analysis of traffic conflicts and collisions. Transportation Research Record. 1978;(667): 67-74.

Astarita V, et al. A new microsimulation model for the evaluation of traffic safety performances. Trasporti Europei. 2012;(51): 16.

Van Beinum A, et al. Critical assessment of methodologies for operations and safety evaluations of freeway turbulence. Transportation Research Record. 2016;(2556): 39-48.

Almqvist S, Hyden C, Risser R. Use of speed limiters in cars for increased safety and a better environment. Transportation Research Record. 1991;(1318): 34-39.

Park H, et al. Development of a lane change risk index using vehicle trajectory data. Accident Analysis and Prevention. 2018;110(October 2017): 1-8. doi: 10.1016/j.aap.2017.10.015.

Chen Q, et al. Modeling accident risks in different lane-changing behavioral patterns. Analytic Methods in Accident Research. 2021;30. doi: 10.1016/j.amar.2021.100159.

Jiang R, et al. Determining an improved traffic conflict indicator for highway safety estimation based on vehicle trajectory data. Sustainability. 2021;13(16). doi: 10.3390/su13169278.

Yang D, et al. Fusing crash data and surrogate safety measures for safety assessment: Development of a structural equation model with conditional autoregressive spatial effect and random parameters. Accident Analysis and Prevention. 2021;152. doi: 10.1016/j.aap.2021.105971.

Orsini F, et al. Large-scale road safety evaluation using extreme value theory. IET Intelligent Transport Systems. 2020;14(9): 1004-1012. doi: 10.1049/iet-its.2019.0633.

Yue Z, et al. Detecting unsafe driving patterns using discriminative learning. Proceedings of the 2007 IEEE International Conference on Multimedia and Expo, ICME 2007. 2007: p. 1431-1434. doi: 10.1109/ICME.2007.4284929.

Ning H, et al. A general framework to detect unsafe system states from multisensor data stream. IEEE Transactions on Intelligent Transportation Systems. 2010;11(1): 4-15. doi: 10.1109/TITS.2009.2026446.

Lee D, Yeo H. Real-time rear-end collision-warning system using a multilayer perceptron neural network. IEEE Transactions on Intelligent Transportation Systems. 2016;17(11): 3087-3097. doi: 10.1109/TITS.2016.2537878.

Fu Y, et al. Graded warning for rear-end collision: An artificial intelligence-aided algorithm. IEEE Transactions on Intelligent Transportation Systems. 2020;21(2): 565-579. doi: 10.1109/TITS.2019.2897687.

Yu R, Han L, Zhang H. Trajectory data based freeway high-risk events prediction and its influencing factors analyses. Accident Analysis & Prevention. 2021;154: 106085. doi: 10.1016/J.AAP.2021.106085.

Hu Y, et al. A high-resolution trajectory data driven method for real-time evaluation of traffic safety. Accident Analysis & Prevention. 2021;165. doi: 10.1016/j.aap.2021.106503.

Chen Q, et al. Using vehicular trajectory data to explore risky factors and unobserved heterogeneity during lane-changing. Accident Analysis & Prevention. 2021;151(July 2020). doi: 10.1016/j.aap.2020.105871.

Bollen KA. Structural Equations with Latent Variables. 2014. doi: 10.1002/9781118619179.

Liang M, et al. Learning lane graph representations for motion forecasting. In: Vedaldi A, Bischof H, Brox T, Frahm JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol 12347. Springer, Cham; 2020; p. 541-556. doi: 10.1007/978-3-030-58536-5_32.

Li Y, Chen Y. Driver vision based perception-response time prediction and assistance model on mountain highway curve. International Journal of Environmental Research and Public Health. 2016;14(1): 31. doi: 10.3390/IJERPH14010031.

Kemper A. Complex networks theory. Contributions to Management Science. 2010. p. 135-157. doi: 10.1007/978-3-7908-2367-7_10.

Hu H, et al. Cost-sensitive semi-supervised deep learning to assess driving risk by application of naturalistic vehicle trajectories. Expert Systems with Applications. 2021;178(April): 115041. doi: 10.1016/j.eswa.2021.115041.

Shannon CE. A Mathematical theory of communication. Bell System Technical Journal. 1948;27(3): 379-423. doi: 10.1002/J.1538-7305.1948.TB01338.X.

Javid MA, et al. Structural equation modeling of drivers’ speeding behavior in Lahore: Importance of attitudes, personality traits, behavioral control, and traffic awareness. Iranian Journal of Science and Technology - Transactions of Civil Engineering. 2022;46(2): 1607-1619. doi: 10.1007/S40996-021-00672-1/FIGURES/1.

Hau J, Wen Z, Cheng Z. Structural equation models and their applications (Rev. ed.). Beijing: Educational Science Publishing House; 2021. p. 29-60.

Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables. Journal of Marketing Research. 1981;XVIII(February): 39-50.

Krajewski R, et al. The highD dataset: A drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems. IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC. 2018;2018-November: 2118-2125. doi: 10.1109/ITSC.2018.8569552.

The highway drone (highD) dataset. RWTH Aachen University; 2018. https://www.highd-dataset.com.

Thakkar JJ. Applications of structural equation modelling with AMOS 21, IBM SPSS. Studies in Systems, Decision and Control. 2020;285: 35-89. doi: 10.1007/978-981-15-3793-6_4/FIGURES/55.

Morgan BJT, McNicol D, Freeman PR. A primer of signal detection theory. The Statistician. 1976;25(3): 231. doi: 10.2307/2987842




How to Cite

Xiong, X., He, Y., Gao, X., & Zhao, Y. (2022). A Multi-Level Risk Framework for Driving Safety Assessment Based on Vehicle Trajectory. Promet - Traffic&Transportation, 34(6), 959–973. https://doi.org/10.7307/ptt.v34i6.4154