Defining Traffic Conflict in Nonlane-Based Traffic Conditions: An Extreme Value Approach

Abstract

Reliable crash data are not usually available in low- and middle-income countries (LMICs). In these regions, surrogate safety measures (SSMs) can be used as effective tools for quantifying road safety. Traffic conflicts are the most-used SSMs, primarily defined based on temporal or spatial proximity between vehicles. Time-to-collision (TTC), a conflict indicator and its derivatives, are commonly used to define conflicts based on 1D interactions may not be suitable for nonlane-based traffic where vehicular interactions are 2D (longitudinal and lateral). This study aims to propose a methodology to define conflicts considering 2D vehicle interactions. Traffic video data were recorded at four unsignalized T-intersections, identified as black spots on divided highways in India. A bivariate extreme value approach was used to define conflict in 2D vehicular interaction using TTC and lateral gap. The results show that incorporating lateral and longitudinal conflict indicators into the bivariate extreme value models can significantly improve conflict-based risk assessment. The proposed approach can be used to define safety-critical events required in vehicle warning systems for nonlane-based traffic.