Bayesian Traffic Conflict Model Accounting for Heterogeneity

GUO Yan-yong, LIU Pan, WU Yao, LI Xiao-wei

China Journal of Highway and Transport ›› 2018, Vol. 31 ›› Issue (4) : 296-303.

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China Journal of Highway and Transport ›› 2018, Vol. 31 ›› Issue (4) : 296-303.

Bayesian Traffic Conflict Model Accounting for Heterogeneity

  • GUO Yan-yong1,2, LIU Pan1, WU Yao1, LI Xiao-wei1,3
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Abstract

To develop a traffic conflict model at signalized intersections, traffic conflict data were extracted from 260 h of video data at 29 signalized intersections. Accounting for the traffic conflict heterogeneity, the random parameters Poisson-lognormal (RP-PLN) traffic conflict model and the random effects Poisson-lognormal (RE-PLN) traffic conflict model were developed. The posterior distributions of the models' parameters were estimated by the Bayesian method and the Markov chain Monte Carlo (MCMC) simulation. Using the deviance information criterion and the residual standardization decision coefficient, the goodness-of-fit of the models were compared. The results show that both the traffic conflict models can handle the traffic conflict heterogeneity; however, the goodness-of-fit of the RP-PLN traffic conflict model performs the RE-PLN traffic conflict model. Given that the other variables remained unchanged, a 1% increase in the crossing-through volume could increase the right-turn & crossing-through (RC) traffic conflict by 0.54%; a 1% increase in the right-turn volume could increase the RC traffic conflict by 0.65%; a 1% increase in the proportion of large vehicles could increase the RC traffic conflict by 1.06%; the raised and pavement channelized islands could decrease the RC traffic conflict by 18.9% and 17.3%, respectively; the acceleration lane could decrease the RC traffic conflict by 22.9%; a 1% increase in the right-turn radius could decrease the RC traffic conflict by 10.5%; the installation of a right-turn yield sign could decrease the RC traffic conflict by 16.5%; the setting up of the protected right-turn phase could decrease the RC traffic conflict by 29.8%.

Key words

traffic engineering / traffic conflicts model / Bayesian method / RP-PLN model / RE-PLN model / heterogeneity

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GUO Yan-yong, LIU Pan, WU Yao, LI Xiao-wei. Bayesian Traffic Conflict Model Accounting for Heterogeneity[J]. China Journal of Highway and Transport, 2018, 31(4): 296-303

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