Let's Connect
Follow Us
Watch Us
(+385) 1 2380 262
journal.prometfpz.unizg.hr
Promet - Traffic&Transportation journal

Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

Login Submit

PDF
PUBLISHED
13.03.2025
DOI
https://doi.org/10.7307/ptt.v37i2.777
ISSUE
2025 (Vol 37), Issue 2
LICENSE
Copyright (c) 2025 Xiaoyu CAI, Zimu LI, Wufeng QIAO, Xiling CHENG, Bo PENG, Dong ZHANG
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Research on Road Traffic Safety Risk Assessment Based on the Data of Radar Video Integrated Sensors

Authors:Xiaoyu CAI, Zimu LI, Wufeng QIAO, Xiling CHENG, Bo PENG, Dong ZHANG

Abstract

To accurately prevent and warn of traffic accidents, this article proposes a method for predicting urban road traffic safety risks based on vehicle driving behaviour data and information entropy theory. This method uses data from radar video-integrated sensors to calibrate the thresholds for identifying unsafe driving behaviour, introduces recognition principles and algorithms, and analyses spatiotemporal distribution patterns. By incorporating entropy theory, an evaluation system with traffic safety entropy as the primary indicator and the unsafe driving behaviour rate as the secondary indicator is established. Clustering algorithms determine the classification number and threshold of traffic safety entropy, constructing a tunnel traffic safety risk assessment model, which is validated with road accident data. Using 13 days of data from the left lane of Qingdao Jiaozhou Bay Tunnel, the model divides traffic operation risk into high and low categories based on K-means clustering results of accident and safety entropy data. The study finds that when the safety entropy classification threshold is 0.0507, the classification accuracy is the highest at 92%. These results provide technical support for identifying road traffic safety risk points and preventing accidents.

Keywords:traffic engineering, risk estimate, information entropy, unsafe driving behaviour, entropy weight method

References

  1. [1] Tang ZZ, Zhang TJ, He Y. Road Traffic Safety Evaluation. Beijing, China: People’s Communications Press; 2008.
  2. [2] Nguyen TH, Lu DN, Nguyen DN, Nguyen HN. Dynamic basic activity sequence matching method in abnormal driving pattern detection using smartphone sensors. Electronics. 2020;9(2):217. DOI: 10.3390/electronics9020217.
  3. [3] Mcgehee DV, et al. Extending parental mentoring using an event-triggered video intervention in rural teen drivers. Journal of Safety Research. 2007;38(2):215–227. DOI: 10.1016/j.jsr.2007.02.009.
  4. [4] Li F, Zhang H, Che H, Qiu X. Dangerous driving behavior detection using smartphone sensors. IEEE 19th international conference on intelligent transportation systems (ITSC). 1-4 Nov. 2016, Rio de Janeiro, Brazil. 2016. p. 1902-1907. DOI:10.1109/ITSC.2016.7795864.
  5. [5] Lu J, Wang K, Jiang YM. Real-time identification method of abnormal road driving behavior based on vehicle driving trajectory. Journal of Traffic and Transportation Engineering. 2020;20(6):227-235. DOI: 10.19818/j.cnki.1671-1637.2020.06.020.
  6. [6] Ma Y, et al. On-line aggressive driving identification based on in-vehicle kinematic parameters under naturalistic driving conditions. Transportation research part C: emerging technologies. 2020;114:554-571. DOI: 10.1016/j.trc.2020.02.028.
  7. [7] Li C, Liu Y. Abnormal driving behavior detection based on covariance Manifold and Logitboost. Laser & Optoelectronics Progress. 2018;55(11):338-345. DOI: 10.3788/LOP55.111503.
  8. [8] Shahverdy M, Fathy M, Berangi R, Sabokrou M. Driver behavior detection and classification using deep convolutional neural networks. Expert Systems with Applications. 2020;149:113240. DOI: 10.1016/j.eswa.2020.113240.
  9. [9] Hu J, et al. Abnormal driving detection based on normalized driving behavior. IEEE Transactions on Vehicular Technology. 2017;66(8):6645-6652. DOI: 10.1109/TVT.2017.2660497.
  10. [10] Hu J, Zhang X, Maybank S. Abnormal driving detection with normalized driving behavior data: A deep learning approach. IEEE transactions on vehicular technology. 2020;69(7):6943-6951. DOI:10.1109/TVT.2020.2993247.
  11. [11] Huang X, Sun J, Sun J. A car-following model considering asymmetric driving behavior based on long short-term memory neural networks. Transportation Research Part C: Emerging Technologies. 2018;95:346–362. DOI: 10.1016/j.trc.2018.07.022.
  12. [12] Liu J, et al. One-dimensional convolutional neural network model for abnormal driving behaviors detection using smartphone sensors. International Conference on Networking Systems of AI (INSAI). 19-20 Nov. 2021, Shanghai, China. 2021.p. 143-150. DOI:10.1109/INSAI54028.2021.00035.
  13. [13] Chen S, et al. Vehicles driving behavior recognition based on transfer learning. Expert Systems with Applications. 2023;213:119254. DOI:10.1016/j.eswa.2022.119254.
  14. [14] Xiao W, et al. FDAN: Fuzzy deep attention networks for driver behavior recognition. Journal of Systems Architecture. 2024;147:103063. DOI:10.1016/j.sysarc.2023.103063.
  15. [15] Darsono AM, et al. Utilizing LSTM networks for the prediction of driver behavior. Przeglad Elektrotechniczny. 2024;100(04):182-185. DOI: 10.15199/48.2024.04.34.
  16. [16] Eren H, Makinist S, Akin E, Yilmaz A. Estimating driving behavior by a smartphone. IEEE 2012 IEEE Intelligent Vehicles Symposium (IV),3-7 Jun. 2012, Madrid, Spain. 2012. p. 234–239. DOI: 10.1109/IVS.2012.6232298.
  17. [17] Chen F, Wang J, Deng Y. Road safety risk evaluation by means of improved entropy TOPSIS–RSR. Safety science. 2015;79:39-54. DOI: 10.1016/j.ssci.2015.05.006.
  18. [18] Yan Y, et al. Driving risk assessment using driving behavior data under continuous tunnel environment. Traffic injury prevention. 2019;20(8):807-812. DOI:10.1080/15389588.2019.1675154.
  19. [19] Chen J, Wu ZC, Zhang J. Driving safety risk prediction using cost-sensitive with nonnegativity-constrained autoencoders based on imbalanced naturalistic driving data. IEEE transactions on intelligent transportation systems. 2019;20(12):4450-4465. DOI:10.1109/TITS.2018.2886280.
  20. [20] Cai X, et al. Road traffic safety risk estimation based on driving behavior and information entropy. China J. Highw. Transp. 2020;33(06):190-201. DOI:10.1155/2020/3024101.
  21. [21] Wang T, et al. Traffic risk assessment based on warning data. Journal of Advanced Transportation. 2022;2022(9):1-11. DOI:10.1155/2022/1191239.
  22. [22] Yang H, Zhao X, Luan S, Chai S. A traffic dynamic operation risk assessment method using driving behaviors and traffic flow data: An empirical analysis. Expert Systems with Applications. 2024;249:123619. DOI:10.1016/j.eswa.2024.123619.
Show more

SPECIAL ISSUE IS OUT

Innovation and New Technologies in Transport and Logistics

Guest Editor: Eleonora Papadimitriou, PhD

Editors: Marko Matulin, PhD; Dario Babić, PhD; Marko Ševrović, PhD.

RECENT PAPERS

Browse through the selection of our recent research
Analysis of the Effect of Tunnel Safety Measures on Vehicle Speed Based on the Analytic Hierarchy ProcessResearch on Road Traffic Safety Risk Assessment Based on the Data of Radar Video Integrated SensorsA Discrete-Event Simulation System for Estimating Passenger Flow in Urban Rail TransitUnderstanding the Walk Intention of Older Adults in China with the Theory of Planned Behaviour and the Prototype Willingness Model

Accelerating Discoveries in Traffic Science |
2024 © Promet - Traffic&Transportation journal