Paving the Way for Evaluation of Connected and Autonomous Vehicles in Buses-Preliminary Analysis

This study intended to provide potential safety consideration that will pave the way for evaluation of connected and autonomous vehicles (CAV) in public buses. The geo-localized crash data of Las Vegas metropolitan area from 2014 to 2017 were collected, involving 27 arterials with 466 bus crash samples, and Chi-square Automatic Interaction Detection (CHAID) decision tree model was proposed to examine the effect of CAV technologies in bus crash severity so that the drivers' factors can be determined and controlled if CAV technologies were employed. Results suggest that contributory predictors of crash severity outcomes are from driver's action of vehicles with main responsibility (including going straight, making U-turn and passing other vehicles/racing), and crash type (angle and rear-end). If these factors are controlled by CAV technologies, it is suggested that severe crashes involving buses could be reduced significantly. The findings provide useful insights for CAV companies and policy makers to improve the driving state and traffic safety in public buses.