Estimation of crash risk for vehicles behind buses in mixed traffic

Kelvin Chun Keong Goh, Graham Victor Currie, Majid Sarvi, David Logan

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

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.
Original languageEnglish
Title of host publicationTRB 94th annual meeting compendium of papers
Place of PublicationWashington DC USA
PublisherUS National Research Council Transportation Research Board
Pages1 - 14
Number of pages14
Publication statusPublished - 2015
EventTransportation Research Board (USA) Annual Meeting 2015 - Washington, United States of America
Duration: 11 Jan 201515 Jan 2015
Conference number: 94

Conference

ConferenceTransportation Research Board (USA) Annual Meeting 2015
Abbreviated titleTRB 2015
CountryUnited States of America
CityWashington
Period11/01/1515/01/15

Keywords

  • Time-to-collision
  • Regression
  • Neural network
  • Monte carlo
  • Bus priority

Cite this

Goh, K. C. K., Currie, G. V., Sarvi, M., & Logan, D. (2015). Estimation of crash risk for vehicles behind buses in mixed traffic. In TRB 94th annual meeting compendium of papers (pp. 1 - 14). Washington DC USA: US National Research Council Transportation Research Board.
Goh, Kelvin Chun Keong ; Currie, Graham Victor ; Sarvi, Majid ; Logan, David. / Estimation of crash risk for vehicles behind buses in mixed traffic. TRB 94th annual meeting compendium of papers. Washington DC USA : US National Research Council Transportation Research Board, 2015. pp. 1 - 14
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title = "Estimation of crash risk for vehicles behind buses in mixed traffic",
abstract = "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.",
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Goh, KCK, Currie, GV, Sarvi, M & Logan, D 2015, Estimation of crash risk for vehicles behind buses in mixed traffic. in TRB 94th annual meeting compendium of papers. US National Research Council Transportation Research Board, Washington DC USA, pp. 1 - 14, Transportation Research Board (USA) Annual Meeting 2015, Washington, United States of America, 11/01/15.

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 proceedingConference PaperResearchpeer-review

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AU - Sarvi, Majid

AU - Logan, David

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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

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KW - Bus priority

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Goh KCK, Currie GV, Sarvi M, Logan D. Estimation of crash risk for vehicles behind buses in mixed traffic. In TRB 94th annual meeting compendium of papers. Washington DC USA: US National Research Council Transportation Research Board. 2015. p. 1 - 14