Investigating the influential factors in changing the likelihood of involving pedestrians in dangerous situations

Document Type : Research Article


1 1. M.Sc., Department of Civil and Environment Engineering, Tarbiat Modares University, Tehran, Iran.

2 2. Assitant Professor, Department of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran


Analyzing the pedestrian safety without using the accident data has become common in the recent years. Investigating the pedestrian safety based on the conflict idea is becoming popular. Therefore, this paper has investigated variables that cause interaction between vehicles and pedestrians for pedestrians, to be potentially dangerous and possibly critical situation. Two measures were used in this study: Time to Collision (TTC) and Post-Encroachment Time (PET). First, an unsignalized intersection was chosen to launch this study. The site was filmed by camera for about 8 hours in two days. The different steps of this study were: identifying the conflict situations, tracking pedestrian and vehicles and then obtaining the PET, TTC, and the other data using MATLAB. Probit models have been developed for analyzing the desired variables. There are 488 and 519 observations in TTC and PET models, respectively. In models with TTC being the dependent variable, the mean pedestrian and vehicle speed and the direction of pedestrian movement were some variables that cause an interaction to be potentially dangerous for pedestrians. Furthermore, in models with PET being the dependent variable, mean vehicle and pedestrian speed, number of pedestrians, and the direction of vehicle movement were some factors that lead a conflict to possibly critical situation for pedestrians.


Main Subjects

1. WHO 2017, “ Global status report on road safety: supporting a decade of action.” Report.
2. Shin, H. S., Chen, G., & Holisko, G. (2011). Pedestrian safety programs in centers of large cities: Institutional settings and identified barriers. Transportation research record, 2264(1), 119-127.
3. Fu, T., Miranda-Moreno, L., & Saunier, N. (2018). A novel framework to evaluate pedestrian safety at non-signalized locations. Accident Analysis & Prevention, 111, 23-33.
4. Ni, Y., Wang, M., Sun, J., & Li, K. (2016). Evaluation of pedestrian safety at intersections: A theoretical framework based on pedestrian-vehicle interaction patterns. Accident Analysis & Prevention, 96, 118-129.
5. Iasmin, H., Kojima, A., & Kubota, H. (2015). Yielding behavior of left turning driver towards pedestrian/cyclist: Impact of intersection angle. Journal of the Eastern Asia Society for Transportation Studies, 11, 2146-2158.
6. Sander, U. (2017). Opportunities and limitations for intersection collision intervention—A study of real world ‘left turn across path’accidents. Accident Analysis & Prevention, 99, 342-355.
7. Chisvert Perales, M. J., López-de-Cózar, E., & Ballestar Tarín, M. L. (2007). Quality and representativity of the traffic accident data in urban areas: State of the Art.
8. Amoros, E., Martin, J. L., & Laumon, B. (2006). Under-reporting of road crash casualties in France. Accident Analysis & Prevention, 38(4), 627- 635.
9. Alsop, J., & Langley, J. (2001). Under-reporting of motor vehicle traffic crash victims in New Zealand. Accident Analysis & Prevention, 33(3), 353-359.
10. EC-European Commission. (2012). Commission staff working document on the implementation of national residue monitoring plans in the member states in 2009 (Council Directive 96/23/EC).
11. Loo, B. P., & Tsui, K. L. (2010). Bicycle crash casualties in a highly motorized city. Accident Analysis & Prevention, 42(6), 1902-1907.
12. Juhra, C., Wieskoetter, B., Chu, K., Trost, L., Weiss, U., Messerschmidt, M., ... & Raschke, M. (2012). Bicycle accidents–Do we only see the tip of the iceberg?: A prospective multi-centre study in a large German city combining medical and police data. Injury, 43(12), 2026-2034.
13. Austin, K. (1995). The identification of mistakes in road accident records: Part 2, casualty variables. Accident Analysis & Prevention, 27(2), 277- 282.
14. Chung, Y., & Chang, I. (2015). How accurate is accident data in road safety research? An application of vehicle black box data regarding pedestrian[1]to-taxi accidents in Korea. Accident Analysis & Prevention, 84, 1-8.
15. Conche, F., & Tight, M. (2006). Use of CCTV to determine road accident factors in urban areas. Accident Analysis & Prevention, 38(6), 1197- 1207.
16. Twisk, D. A. M., & Reurings, M. (2013). An epidemiological study of the risk of cycling in the dark: The role of visual perception, conspicuity and alcohol use. Accident Analysis & Prevention, 60, 134-140.
17. Dubos, N., Varin, B., & Bisson, O. (2016). A better knowledge of powered two wheelers accidents. Transportation research procedia, 14, 2274- 2283.
18. Fu, T., Miranda-Moreno, L., & Saunier, N. (2016). Pedestrian crosswalk safety at nonsignalized crossings during nighttime: use of thermal video data and surrogate safety measures. Transportation research record, 2586(1), 90-99.
19. St-Aubin, P., Miranda-Moreno, L., & Saunier, N. (2013). An automated surrogate safety analysis at protected highway ramps using cross[1]sectional and before–after video data. Transportation Research Part C: Emerging Technologies, 36, 284-295.
20. Saunier, N., Sayed, T., & Ismail, K. (2010). Large-scale automated analysis of vehicle interactions and collisions. Transportation Research Record, 2147(1), 42-50.
21. Laureshyn, A., Svensson, Å., & Hydén, C. (2010). Evaluation of traffic safety, based on micro-level behavioural data: Theoretical framework and first implementation. Accident Analysis & Prevention, 42(6), 1637- 1646.
22. Hannah, C., Spasić, I., & Corcoran, P. (2018). A computational model of pedestrian road safety: the long way round is the safe way home. Accident Analysis & Prevention, 121, 347-357.
23. Lee, J., Abdel-Aty, M., & Shah, I. (2018). Evaluation of surrogate measures for pedestrian trips at intersections and crash modeling. Accident Analysis & Prevention.
24. Almodfer, R., Xiong, S., Fang, Z., Kong, X., & Zheng, S. (2016). Quantitative analysis of lane-based pedestrian-vehicle conflict at a non[1]signalized marked crosswalk. Transportation research part F: traffic psychology and behaviour, 42, 468-478.
25. Lord, D. (1996). Analysis of pedestrian conflicts with left-turning traffic. Transportation Research Record, 1538(1), 61-67.
26. Akin, D., & Sisiopiku, V. P. (2007, January). Modeling Interactions Between Pedestrians and Turning Vehicles at Signalized Crosswalks Operating Under Combined Pedestrian–Vehicle Interval. In 86th Annual Meeting of the Transportation Research Board, Washington, DC.
27. Cheng, W., Zhang, N., Li, W., & Xi, J. (2014). Modeling and application of pedestrian safety conflict index at signalized intersections. Discrete Dynamics in Nature and Society, 2014.
28. Saulino, G., Persaud, B., & Bassani, M. (2015, January). Calibration and application of crash prediction models for safety assessment of roundabouts based on simulated conflicts. In Proceedings of the 94th Transportation Research Board (TRB) Annual Meeting, Washington, DC, USA (pp. 11-15).
29. Yagil, D. (2000). Beliefs, motives and situational factors related to pedestrians’ self-reported behavior at signal-controlled crossings. Transportation Research Part F: Traffic Psychology and Behaviour, 3(1), 1-13.
30. Greenwald, M. J., & Boarnet, M. G. (2001). Built environment as determinant of walking behavior: Analyzing nonwork pedestrian travel in Portland, Oregon. Transportation research record, 1780(1), 33-41.
31. Kim, W., Kim, G. J., & Lee, D. (2016). Estimating potential conflicts between right-turn-on-red vehicles and pedestrians at crosswalks. International Journal of Urban Sciences, 20(2), 226-240.
32. Olszewski, P., Buttler, I., Czajewski, W., Dąbkowski, P., Kraśkiewicz, C., Szagała, P., & Zielińska, A. (2016). Pedestrian safety assessment with video analysis. Transportation research procedia, 14, 2044-2053.
33. Ismail, K., Sayed, T., & Saunier, N. (2010). Automated analysis of pedestrian–vehicle conflicts: Context for before-and-after studies. Transportation research record, 2198(1), 52-64.
34. Muley, D., Kharbeche, M., Alhajyaseen, W., & Al-Salem, M. (2017). Pedestrians’ Crossing Behavior at Marked Crosswalks on Channelized Right-Turn Lanes at Intersections. Procedia computer science, 109, 233- 240.
35. Andersen, C. S., Kamaluddin, N. A., Várhelyi, A., Madsen, T. K. O., & Møller, K. M. (2016). Review of current study methods for VRU safety: Appendix 7–Systematic literature review: Self-reported accidents.
36. Tageldin, A., & Sayed, T. (2016). Developing evasive action‐based indicators for identifying pedestrian conflicts in less organized traffic environments. Journal of Advanced Transportation, 50(6), 1193- 1208.
37. Kraay, J. H., van der Horst, A. R. A., & Oppe, S. (2013). Manual conflict observation technique DOCTOR (Dutch Objective Conflict Technique for Operation and Research).
38. Van der Horst, R., & Kraay, J. (1986, September). The Dutch Conflict Observation Technique–DOCTOR. In Proceedings of the workshop “Traffic Conflicts and Other Intermediate Measures in Safety Evaluation”, Budapest, Hungary.
39. Mandrekar, J. N. (2010). Receiver operating characteristic curve in diagnostic test assessment. Journal of Thoracic Oncology, 5(9), 1315- 1316.
40. Wu, J., Yan, X., & Radwan, E. (2016). Discrepancy analysis of driving performance of taxi drivers and non-professional drivers for red[1]light running violation and crash avoidance at intersections. Accident Analysis & Prevention, 91, 1-9.
41. Ren, G., Zhou, Z., Wang, W., Zhang, Y., & Wang, W. (2011). Crossing behaviors of pedestrians at signalized intersections: observational study and survey in China. Transportation research record, 2264(1), 65-73.
42. Peters, D., Kim, L., Zaman, R., Haas, G., Cheng, J., & Ahmed, S. (2015). Pedestrian Crossing Behavior at Signalized Intersections in New York City (No. 15-5975).
43. Why left-turns are so deadly://