Driving Behavior at Signalized Intersections Operating under Disordered Traffic Conditions

Abstract

A higher degree of heterogeneity in vehicle class and drivers, coupled with non-lane-based driving habits, creates several challenges in traffic flow analysis. This study investigates vehicles’ microscopic driving behavior at signalized intersections operating under weak lane discipline with mixed traffic (disordered) conditions. For this purpose, a comprehensive vehicular trajectory data set is developed from field-recorded video footage using a semi-automated tool for data extraction. Microscopic parameters such as relative velocity, spacing between vehicles, following time, lane preference, longitudinal and lateral speed profile, hysteresis evidence, and lateral movement of different vehicle classes during different traffic phases are presented in the study. The data is then segregated into three flow conditions: stopped flow, saturated flow, and unaffected flow. It is found that smaller vehicles prefer near-side lanes over far-side lanes. Motorized three-wheeler (3W) and motorized two-wheeler (2W) vehicle classes exhibit the greatest lateral velocity, lateral movement, and aggressiveness. This results in several interactions between vehicles as a function of different leader–follower vehicle pairs. Signalized intersections with more heterogeneity in traffic composition, especially higher composition of 2W and 3W vehicle classes, exhibit higher levels of aggressive driving behavior that might lower safety standards. As a practical application, ranges of various driving behavior parameter values for different leader–follower combinations and traffic conditions are quantified in the study. The observations and results are expected to help better understand prevailing driving behavior in disordered traffic and contribute toward robust calibration of microscopic traffic flow models for better replicating disordered traffic conditions at signalized intersections.

Video Analysis and Rule-Based Reasoning for Driving Maneuver Classification at Intersections

“…Driving behavior at road intersections is a well-studied topic. It has been used for driver profiling and macro analysis, including trajectory prediction, turning recognition, and turning forecast [5], [6], [8]- [15].…”

“…The study in [5] focused on vehicles' driving behavior at intersections operating under mixed traffic conditions. The study was accomplished by extracting data using a semi-automated tool from field-recorded video.…”

Critical Gap Estimation and Its Implication on Capacity and Safety of High-Speed Un-Signalised T-Intersection under Heterogeneous Traffic Conditions

“…The Generalized Pareto (GP) and Generalised Extreme Value (GEV) are the two most popular extreme value distributions used by researchers to model the variation of different traffic conflict indicators. Past studies have reported that the best-fitted distribution to model the variation in the risk is the generalized extreme value (GEV) distribution [28][29][30][31][32][33][34][35][36].The safety of an un-signalized intersection has been evaluated using the EVT. The technique has shown potential for use in safety-related studies and is now widely used in conflict and crash-related studies.…”

Challenges in Rear-end Conflict-based Safety Assessment of Highly Disordered Traffic Conditions

“…To avoid this issue, studies of disordered traffic conditions still rely on semi-automated or manual tools to extract the vehicular trajectories, where the user needs to manually trace the movement of the target vehicle through the study sections ( 17 ). The method of extracting the vehicular trajectories using semi-automated or manual methods has proven effective for microscopic traffic behavior and safety assessment studies ( 6 , 18 , 19 ). However, this method is highly arduous, and therefore the available studies provide results from limited sample points.…”