Journal of Transportation Research

Journal of Transportation Research

Modeling the Impact of Geometric and Traffic Characteristics on Critical Conflicts at Signalized Four-Leg Intersections Using the Post-Encroachment Time Indicator

Document Type : Original Article

Authors
Kamran Sarvari 1
Aminmirza Boroujerdian 2
1 M‌.Sc., Stud., Faculty of Civil and Environmental Engineering, Tarbiat Modares, University, Tehran, Iran
2 Associate Professor, Faculty of Civil and Environmental Engineering, Tarbiat Modares University, Tehran, Iran.
10.22034/tri.2025.521674.3338
Abstract
With the significant increase in population in urban areas and the continuous growth in the use of vehicles, traffic infrastructure is more in need of enhancement and revision than ever before to improve safety levels. In this regard, urban intersections, as one of the most sensitive and congested parts of the urban transportation network, play a crucial role in ensuring the safe and smooth movement of both vehicles and pedestrians. Due to the concentration of high traffic volumes, these points are more prone to conflicts, reduced safety levels, and the occurrence of risky traffic behaviors compared to other parts of the network. The increase in traffic volume in these areas can further create conditions conducive to accidents and unsafe situations. Therefore, identifying the factors influencing the safety of these intersections is essential for enhancing the quality of urban transportation services. In this study, data related to the geometric and traffic characteristics of seven signalized urban intersections were collected and analyzed. To obtain accurate information, aerial imaging techniques were used to record video footage at each of these intersections. The recorded images were then analyzed using image processing software. Additionally, the number of critical conflicts and post-encroachment time (PET) events at these intersections were calculated. A total of 20,412 events were considered for this traffic indicator. The results of statistical analysis, obtained using a multiple linear regression model, show that an increase of one unit in traffic volume can lead to a 1.6% increase in the number of critical conflicts. Furthermore, the analyses indicate that an increase of one meter in the average entrance width of intersections can increase the number of critical conflicts by approximately 11 to 16%.
Keywords
Safety Four-Leg Signalized Intersections
post-encroachment time
Performance Analysis
Image Processing
Traffic Conflicts
Subjects
-Ali, Y., Sarkar, D. R., Rao, K. R., Chatterjee, N., & Bhaskar, A. (2024). Scrutinizing Data from Sky: An Examination of Its Veracity in Area Based Traffic Contexts. arXiv preprint arXiv:2404.17212.
-Allen, B. L., Shin, B. T., & Cooper, P. J. (1978). Analysis of Traffic Conflicts and Collisions .0361-1981 .
-Ansariyar, A., Ardeshiri, A., & Jeihani, M. (2023). Investigating the collected vehicle-pedestrian conflicts by a LIDAR sensor based on a new Post Encroachment Time Threshold (PET) classification at signalized intersections. Advances in Transportation Studies.
-Archer, J. (2005). Indicators for traffic safety assessment and prediction and their application in micro-simulation modelling: A Study of Urban and Suburban Intersections KTH.
-Arun, A., Haque, M. M., Bhaskar, A., Washington, S., & Sayed, T. (2021). A systematic mapping review of surrogate safety assessment using traffic conflict techniques. Accident Analysis & Prevention, 153, 106016.
-Billah, K., Adegbite, Q., Sharif, H. O., Dessouky, S., & Simcic, L. (2021). Analysis of intersection traffic safety in the city of San Antonio, 2013–2017. Sustainability, 13(9), 5296.
-Caliendo, C., & Guida, M. (2012). Microsimulation approach for predicting crashes at unsignalized intersections using traffic conflicts. Journal of Transportation Engineering, 138(12), 1453-1467.
-Chandler, B. E., Myers, M., Atkinson, J. E., Bryer, T., Retting, R., Smithline, J., Trim, J., Wojtkiewicz, P., Thomas, G. B., & Venglar, S. P. (2013). Signalized intersections Informational Guide.
-Chaudhari, A., Gore, N., Arkatkar, S., Joshi, G., & Pulugurtha, S. (2021). Exploring pedestrian surrogate safety measures by road geometry at midblock crosswalks: A perspective under mixed traffic conditions. IATSS Research, 45(1), 87-101.
-Essa, M., & Sayed, T. (2020). Comparison between surrogate safety assessment model and real-time safety models in predicting field-measured conflicts at signalized intersections. Transportation Research Record, 2674(3), 100-112.
-Hayward, J. C. (1972). Near miss determination through use of a scale of danger.
-Islam, Z., Abdel-Aty, M., Goswamy, A., Abdelraouf, A., & Zheng, O. (2022). Modelling the relationship between post encroachment time and signal timings using UAV video data. Arxiv Preprint arXiv:2210.05044.
-Kutner, M. H., Nachtsheim, C. J., Neter, J., & Li, W. (2005). Applied linear statistical models. McGraw-hill.
-Laureshyn, A. (2010). Application of automated video analysis to road user behaviour.
-Li, P., Abdel-Aty, M., & Yuan, J. (2021). Using bus critical driving events as surrogate safety measures for pedestrian and bicycle crashes based on GPS trajectory data. Accident Analysis & Prevention, 150, 105924.
-Organization, W. H. (2023). Global status report on road safety 2023: summary. World Health Organization.
-Paul, M., & Ghosh, I. (2018). Speed-based proximal indicator for right-turn crashes at unsignalized intersections in India. Journal of Transportation Engineering, Part A: Systems, 144(6), 04018024.
-Paul, M., & Ghosh, I. (2020). Post encroachment time threshold identification for right-turn related crashes at unsignalized intersections on intercity highways under mixed traffic. International Journal of Injury Control and Safety Promotion, 27(2), 121-135.
-Peesapati, L. N., Hunter, M. P., & Rodgers, M. O. (2013). Evaluation of postencroachment time as surrogate for opposing left-turn crashes. Transportation Research Record, 2386(1), 42-51.
-Prajongkha, P., Kanitpong, K., & Jensupakarn, A. (2023). Factors contributing to the severity of motorcycle rear-end crashes in Thailand. Traffic Injury Prevention, 24(1), 89-93.
-Stanojević, D., Stanojević, P., Jovanović, D., & Lipovac, K. (2020). Impact of riders’ lifestyle on their risky behavior and road traffic accident risk. Journal of Transportation Safety & Security, 12(3), 400-418.
-Stipancic, J., Miranda-Moreno, L., Saunier, N., & Labbe, A. (2018). Surrogate safety and network screening: Modelling crash frequency using GPS travel data and latent Gaussian Spatial Models. Accident Analysis & Prevention, 120, 174-187.­
-Tarko, A. P. (2018). Estimating the expected number of crashes with traffic conflicts and the Lomax Distribution–A theoretical and numerical exploration. Accident Analysis & Prevention, 113, 63-73.
-Vedagiri, P., & Killi, D. V.(2015). Traffic safety evaluation of uncontrolled intersections using surrogate safety measures under mixed traffic conditions. Transportation Research Record, 2512(1), 81-89.
-Wooldridge, J. M. (2016). Introductory Econometrics: A Modern Approach 6rd ed. Cengage learning.
-Zheng, L., Ismail, K., & Meng, X. (2014). Traffic conflict techniques for road safety analysis: open questions and some insights. Canadian Journal of Civil Engineering, 41(7), 633-641.