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Abstract

Two-thirds of vehicle accidents in Malaysia occurred at the straight type of roads, followed by intersection-type roads. Despite the deployment of traffic lights on the road, accidents still occur which are caused by illegal maneuvers, speeding or misjudgment of other’s actions. Hence, motivated by the lack of previous research regarding causes of accidents on intersectional roads, this study aims to observe the pattern of the vehicles’ speed and turning angle during the right turn after the traffic stop at the intersection road. To obtain these parameters, video samples of vehicles at two types of intersections were obtained and analyzed via YOLOV7 and DeepSORT. The two road intersections researched are four-legged intersection and three-legged intersection. 153 and 35 vehicle samples were collected from these types of road intersections, respectively. It was observed that 78 and 75 vehicles exit towards the nearest and furthest lanes at four-leg controlled crossings on divided roads. While, at a single-lane to a dual carriageway road intersection, 26 and 9 vehicles exit towards the nearest and furthest lanes, respectively. From the research, 16.52 - 17.53 km/h and 67.57°-73.33° are the most optimal turning speeds and angles respectively for vehicles at four-leg controlled crossings. Whereas 14.48 - 15.51 km/h and 144.77° - 154.403° are the most optimal turning speeds and angles respectively for vehicles at a single-lane to a dual carriageway road intersection.

Keywords

Vehicle accidents Intersection roads analysis Turning behavior Vehicle trajectory patterns YOLOV7

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References

  1. N. F. Nasarrudin and I. S. Mohd Razelan, “The trend of road traffic crashes at urban signalised intersection,” IOP Conference Series: Materials Science and Engineering, vol. 342, no. 1, p. 12015, 2018, doi: 10.1088/1757-899X/342/1/012015.
  2. S. I. Mohammed, “An Overview of Traffic Accident Investigation Using Different Techniques,” Automotive Experiences, vol. 6, no. 1, pp. 68–79, Jan. 2023, doi: 10.31603/ae.7913.
  3. A. R. Zubir, K. Hudha, Z. A. Kadir, and N. H. Amer, “Enhanced Modeling of Crumple Zone in Vehicle Crash Simulation Using Modified Kamal Model Optimized with Gravitational Search Algorithm,” Automotive Experiences, vol. 6, no. 2, pp. 372–383, Aug. 2023, doi: 10.31603/ae.9289.
  4. K. A. A. Kassim, L. Arokiasamy, M. H. M. Isa, and I. Osman, “Automotive Consumerism towards Car Safety in Malaysia,” Procedia - Social and Behavioral Sciences, vol. 219, pp. 424–430, 2016, doi: https://doi.org/10.1016/j.sbspro.2016.05.065.
  5. I. M. Zulhilmi, P. M. Heerwan, S. M. Asyraf, I. M. Sollehudin, and I. M. Ishak, “Experimental study on the effect of emergency braking without anti-lock braking system to vehicle dynamics behaviour,” International Journal of Automotive and Mechanical Engineering, vol. 17, no. 2, pp. 7832–7841, Apr. 2020, doi: https://doi.org/10.15282/ijame.17.2.2020.02.0583.
  6. N. Md Yusof et al., “Effect of Road Darkness on Young Driver Behaviour when Approaching Parked or Slow-moving Vehicles in Malaysia,” Automotive Experiences, vol. 6, no. 2, pp. 216–233, May 2023, doi: 10.31603/ae.8206.
  7. M. G. Masuri, N. A. Abdul Samad, A. Dahlan, and K. A. Md Isa, “Attitude towards Safe Driving and Internet Addiction among Young Adult in Malaysia,” Journal of ASIAN Behavioural Studies, vol. 4, no. 14 SE-Articles, pp. 1–15, Nov. 2019, doi: 10.21834/jabs.v4i14.335.
  8. A. Idris, H. Hamid, and L. Teik Hua, “Factors contributing to motorcycle accidents in Malaysia,” IOP Conference Series: Earth and Environmental Science, vol. 357, no. 1, p. 12039, 2019, doi: 10.1088/1755-1315/357/1/012039.
  9. M. N. Borhan, A. N. H. Ibrahim, A. Aziz, and M. R. M. Yazid, “The relationship between the demographic, personal, and social factors of Malaysian motorcyclists and risk taking behavior at signalized intersections,” Accident Analysis & Prevention, vol. 121, pp. 94–100, 2018, doi: https://doi.org/10.1016/j.aap.2018.09.004.
  10. P. Olszewski, B. Osińska, and A. Zielińska, “Pedestrian Safety at Traffic Signals in Warsaw,” Transportation Research Procedia, vol. 14, pp. 1174–1182, 2016, doi: https://doi.org/10.1016/j.trpro.2016.05.188.
  11. P. Nitsche, P. Thomas, R. Stuetz, and R. Welsh, “Pre-crash scenarios at road junctions: A clustering method for car crash data,” Accident Analysis & Prevention, vol. 107, pp. 137–151, 2017, doi: https://doi.org/10.1016/j.aap.2017.07.011.
  12. H. Jashami, J. C. Anderson, H. A. Mohammed, D. P. Cobb, and D. S. Hurwitz, “Contributing factors to right-turn crash severity at signalized intersections: An application of econometric modeling,” International Journal of Transportation Science and Technology, vol. 13, pp. 243–257, 2024, doi: https://doi.org/10.1016/j.ijtst.2023.02.004.
  13. N. Aznirahani et al., “Causation of Road Tra c Crashes along Malaysian Intra-Urban Expressways,” Selangor Darul Ehsan, 2022.
  14. W. A. Al Bargi, M. M. Rohani, B. D. Daniel, N. A. Khalifaa, M. I. M. Masirin, and J. Kironde, “Estimating of Critical Gaps at Uncontrolled Intersections under Heterogeneous Traffic Conditions,” Automotive Experiences, vol. 6, no. 2, pp. 429–437, 2023, doi: 10.31603/ae.9406.
  15. M. A. Shahrom and M. H. Peeie, “Development of Zero Turning Radius by using different independent torque,” in 2017 3rd International Conference on Control, Automation and Robotics (ICCAR), 2017, pp. 647–650, doi: 10.1109/ICCAR.2017.7942777.
  16. A. I. Petrov and A. V Pistsov, “Training and Applying Artificial Neural Networks in Traffic Light Control: Improving the Management and Safety of Road Traffic in Tyumen (Russia),” Automotive Experiences, vol. 6, no. 3, pp. 528–550, 2023, doi: 10.31603/ae.10025.
  17. A. Ahmed, A. F. M. Sadullah, and A. Shukri Yahya, “Field study on the behavior of right-turning vehicles in Malaysia and their contribution on the safety of unsignalized intersections,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 42, pp. 433–446, 2016, doi: https://doi.org/10.1016/j.trf.2015.03.006.
  18. O. G. Dela Cruz, J. A. Padilla, and A. N. Victoria, “Managing Road Traffic Accidents: A Review on Its Contributing Factors,” IOP Conference Series: Earth and Environmental Science, vol. 822, no. 1, p. 12015, 2021, doi: 10.1088/1755-1315/822/1/012015.
  19. S. Chan, Y. Jia, X. Zhou, C. Bai, S. Chen, and X. Zhang, “Online multiple object tracking using joint detection and embedding network,” Pattern Recognition, vol. 130, p. 108793, 2022, doi: https://doi.org/10.1016/j.patcog.2022.108793.
  20. Y. Liu, X. Li, T. Bai, K. Wang, and F.-Y. Wang, “Multi-object tracking with hard-soft attention network and group-based cost minimization,” Neurocomputing, vol. 447, pp. 80–91, 2021, doi: https://doi.org/10.1016/j.neucom.2021.02.084.
  21. N. Bumanis, “Multi-object Tracking for Urban and Multilane Traffic : Building Blocks for Real-World Application,” vol. 1, no. Iceis 2021, pp. 729–736, 2024, doi: 10.5220/0010467807290736.
  22. F. Yang, X. Zhang, and B. Liu, “Video object tracking based on YOLOv7 and DeepSORT,” Journal of Latex Class Files, vol. 14, no. 8, pp. 2015–2018, 2015, doi: 10.48550/arXiv.2207.12202.
  23. P. Jiang, D. Ergu, F. Liu, Y. Cai, and B. Ma, “A Review of Yolo Algorithm Developments,” Procedia Computer Science, vol. 199, pp. 1066–1073, 2022, doi: https://doi.org/10.1016/j.procs.2022.01.135.
  24. T. L. Dang, G. T. Nguyen, and T. Cao, “Object Tracking Using Improved Deep_Sort_YOLOv3 Architecture,” ICIC Express Letters, vol. 14, no. 10, pp. 961–969, 2020, doi: 10.24507/icicel.14.10.961.
  25. H. Yao et al., “City-Scale Multi-Camera Vehicle Tracking based on Space-Time-Appearance Features,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2022, pp. 3309–3317, doi: 10.1109/CVPRW56347.2022.00374.
  26. V. K. Yadav, P. Yadav, and S. Sharma, “An Efficient Yolov7 and Deep Sort are Used in a Deep Learning Model for Tracking Vehicle and Detection,” Journal of Xi’an Shiyou University, Natural Science Edition, vol. 18, no. 11, pp. 759–763, 2022.
  27. H. Rodríguez-Rangel, L. A. Morales-Rosales, R. Imperial-Rojo, M. A. Roman-Garay, G. E. Peralta-Peñuñuri, and M. Lobato-Báez, “Analysis of Statistical and Artificial Intelligence Algorithms for Real-Time Speed Estimation Based on Vehicle Detection with YOLO,” Applied Sciences, vol. 12, no. 6. 2022, doi: 10.3390/app12062907.
  28. K. Sangsuwan and M. Ekpanyapong, “Video-Based Vehicle Speed Estimation Using Speed Measurement Metrics,” IEEE Access, vol. 12, pp. 4845–4858, 2024, doi: 10.1109/ACCESS.2024.3350381.

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