Anticipated Collision Time (ACT): A two-dimensional surrogate safety indicator for trajectory-based proactive safety assessment

Abstract

Surrogate Safety Measures (SSMs) are widely used to assess potential crash risk proactively. Notably, most of the existing safety indicators are fundamentally designed to capture the rear-end collision risk. However, in reality, the traffic dynamics involve the simultaneous interaction of multiple vehicles on a 2-dimensional (2D) surface, which results in a broad spectrum of collision patterns, such as head-on, side-swipe, rear-end, and angled collisions. This study proposes a novel 2D surrogate safety indicator called Anticipated Collision Time (ACT) to capture the risk pattern corresponding to all collision types using a single measure. We also devised a procedure to automatically detect conflict situations and extract ACT profiles as well as crash-type information from the trajectory data. The primary inputs to the ACT estimation are the shortest distance between the vehicles and the closing-in rate. This study also introduces another safety indicator derived from the ACT profile called Time of Evasive Action (TEA). TEA primarily captures the time at which a vehicle commences to respond, in terms of deceleration, when it encounters an unsafe situation. Such a measure helps to understand the response pattern of different vehicles/drivers to a potential collision. We also derived Time Exposed ACT (TE-ACT) and Time Integrated ACT (TI-ACT) from the ACT profile to capture crash exposure and severity. To highlight the potential of ACT, the Powered Two Wheeler (PTW) safety in an urban environment was analyzed with trajectory data collected from a busy urban midblock section. The results emphasize the capabilities of ACT and the other derived indicators to capture crash risk proactively. It is incontrovertible from the analyses that the ACT is opening up a new avenue for a comprehensive investigation of the safety of various transport facilities, irrespective of geometry and traffic scenario.