An evasive action-based bivariate extreme value model for estimating pedestrian crash frequency using traffic conflicts

Abstract

Traditional models, employing extreme value theory for estimating pedestrian crashes from traffic conflicts, commonly utilise popular conflict measures, such as post encroachment time and gap time. Whilst these measures have proven useful, they are limited in identifying a vehicle–pedestrian conflict based on a fixed threshold value and depend on subjective graphical-based extreme identification methods, which neither fully capture the dynamic interactions between vehicles and pedestrians nor account for road user behaviour to identify conflicting events. This study proposes a bivariate extreme value modelling framework that analyses evasive action-based traffic conflicts by integrating risk force theory and artificial intelligence-based video analytics to estimate pedestrian crash frequency by severity. The methodological framework quantifies crash risk dynamically during vehicle–pedestrian interactions and identifies traffic conflict events based on evasive behaviours. Traffic conflicts are modelled using a Generalised Pareto distribution to capture the tail behaviour of high-risk conflicts. The proposed econometric modelling framework was validated using 72 h of traffic movement data from three signalised intersections in Queensland, Australia. Results demonstrate that the Generalised Pareto distributions effectively fit evasive action-based vehicle–pedestrian conflicts, with estimated total pedestrian frequency and severe crash frequency aligning closely with historical crash records, thereby supporting the validity of the proposed model. This study presents a scalable, behaviourally grounded methodology as an alternative to a subjective conflict identification approach, enabling continuous risk assessment for proactive pedestrian safety management and real-time safety analysis.