01551822

Component

This paper summarises findings from a three-stage modelling approach used to estimate the crash risk of a vehicle that is behind a slowing or stationary bus in a mixed traffic configuration. This approach involves the development of regression and neural network models to represent drivers’ lane changing behaviour, followed by an establishment of crash risk probability and estimation of crash risk through a Monte Carlo simulation approach using time-to-collision and accident data. Through a case study of a road corridor, results showed that speed differences between the subject and lead vehicles in the current and adjacent lanes, distances between the subject and lead or lag vehicle in the adjacent lane as well as whether the bus is a lead vehicle were significant factors that influence lane change. The Monte Carlo simulation results revealed that average crash risk of vehicles that performed the lane change (LC) and those remained in the current lane (NLC) differ (0.0185% vs. 0.0062%). Overall crash risk was found to be 0.0154% (with a standard error of 0.0063%). The risk estimates serve as important findings for bus safety and bus priority research as well as policy–makers in road and transit agencies, as they provide new knowledge of the quantum of risk involved in designing bus stops in mixed traffic as well as benefits delivered by bus priority schemes that segregate buses from mainstream traffic.

This paper was sponsored by TRB committee AP050 Bus Transit Systems.

01550057

15-0634

English

14p

2015

Transportation Research Board 94th Annual Meeting

Digital/other

Figures; Photos; References; Tables

Bus priority; Crash risk forecasting; Lane changing; Monte Carlo method; Multiple vehicle crashes; Neural networks; Vehicle mix

Time-to-collision

Highways; Public Transportation; Safety and Human Factors; I83: Accidents and the Human Factor

Transportation Research Board Annual Meeting 2015 Paper #15-0634

PRP, TRIS, TRB, ATRI

Dec 30 2014 12:19PM