Abstract
| Original language | English |
|---|---|
| Title of host publication | TRB 94th annual meeting compendium of papers |
| Place of Publication | Washington DC USA |
| Publisher | US National Research Council Transportation Research Board |
| Pages | 1 - 14 |
| Number of pages | 14 |
| Publication status | Published - 2015 |
| Event | Transportation Research Board (USA) Annual Meeting 2015 - Washington, United States of America Duration: 11 Jan 2015 → 15 Jan 2015 Conference number: 94 |
Conference
| Conference | Transportation Research Board (USA) Annual Meeting 2015 |
|---|---|
| Abbreviated title | TRB 2015 |
| Country | United States of America |
| City | Washington |
| Period | 11/01/15 → 15/01/15 |
Keywords
- Time-to-collision
- Regression
- Neural network
- Monte carlo
- Bus priority
Cite this
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Estimation of crash risk for vehicles behind buses in mixed traffic. / Goh, Kelvin Chun Keong; Currie, Graham Victor; Sarvi, Majid; Logan, David.
TRB 94th annual meeting compendium of papers. Washington DC USA : US National Research Council Transportation Research Board, 2015. p. 1 - 14.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
TY - GEN
T1 - Estimation of crash risk for vehicles behind buses in mixed traffic
AU - Goh, Kelvin Chun Keong
AU - Currie, Graham Victor
AU - Sarvi, Majid
AU - Logan, David
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Time-to-collision
KW - Regression
KW - Neural network
KW - Monte carlo
KW - Bus priority
UR - http://docs.trb.org/prp/15-0634.pdf
M3 - Conference Paper
SP - 1
EP - 14
BT - TRB 94th annual meeting compendium of papers
PB - US National Research Council Transportation Research Board
CY - Washington DC USA
ER -