Modeling Crossing Conflicts at Unsignalized T-Intersections under Heterogeneous Traffic Conditions. (4th October 2022)

• Record Type: Journal Article
• Authors: Goyani, Jaydip; Gore, Ninad; Arkatkar, Shriniwas
• Other Names: Guo Yanyong Academic Editor.
• Is Part Of: Journal of advanced transportation. Volume 2022(2022)
• Publication Date: 2022-10-04
• Subjects: Transportation -- Periodicals; 388.05
• DOI: 10.1155/2022/9965733
• Languages: English
• ISSNs: 0197-6729

Abstract

Abstract : The safety of unsignalized intersections is evaluated by correlating the number of crashes with traffic volume and intersection geometry. However, crash-based safety assessment has known drawbacks related to data quality and coverage. Further, the crash-based safety analysis does not account that not all vehicles interact unsafely. Therefore, the present study develops crossing conflict-based safety performance functions (C-SPFs) for eight urban unsignalized T-intersections with varying intersection geometry. Initially, the crossing conflicts were analyzed using post encroachment time (PET); based on that, they are bifurcated into critical and noncritical conflicts. The C-SPFs were modeled as a function of traffic volume and intersection geometry using the generalized estimating equations with the Tweedie distribution (GEE_TD) regression approach. The results revealed the time of the day, intersection geometry, vehicular composition, and traffic volume of both offending and conflicting approaches as significant variables influencing the number of critical and noncritical crossing conflicts. Further, to check the predictive power of the GEE_TD model, the model errors are compared with those obtained using the negative binomial (NB) model. The result revealed that for both critical and noncritical conflicts, the GEE_TD model has better predictivity (lesser error) than the NB model.