A Hierarchical Multinomial Logit model to examine the effects of signal strategies on right-turn crash risks by crash movement configuration

被引:5
|
作者
Islam, Sheikh Manirul [1 ]
Washington, Simon [2 ]
Kim, Jiwon [1 ]
Haque, Md Mazharul [3 ]
机构
[1] Univ Queensland, Fac Engn Architecture & Informat Tech, Sch Civil Engn, St Lucia 4072, Australia
[2] Adv Mobil Analyt Grp Pty Ltd, Brisbane, Australia
[3] Queensland Univ Technol, Fac Engn, Sch Civil & Environm Engn, Brisbane 4001, Australia
来源
关键词
Right-turn crashes; DCA code; Crash configuration; Signal strategies; Hierarchical Multinomial Logit model; Signalised intersections; INJURY SEVERITY; DRIVER INJURY; TRAFFIC CRASHES; ORDERED PROBIT; INTERSECTIONS; LEVEL; FREQUENCIES; PREDICTION; LOCATIONS; DESIGN;
D O I
10.1016/j.aap.2023.106993
中图分类号
TB18 [人体工程学];
学科分类号
1201 ;
摘要
Crash risk models relying on total crash counts are limited in their ability to extract meaningful insights regarding the context of crashes and to identify effective remedial measures. In addition to the typical classifi-cation of collisions noted in the literature (e.g., angle, head-on and rear-end), crashes can also be categorised according to vehicle movement configurations (Definitions for Coding Accidents or DCA codes in Australia). This classification presents an opportunity to extract useful insights into road traffic collision causes and contributing factors that are highly contextual. With this aim, this study develops crash-type models by DCA crash movement, with a focus on right-turn crashes (equivalent to left-turn crashes for right-hand traffic) at signalised intersections using a novel approach for linking crashes with signal control strategies. The modelling approach with contextual data enables quantification of the effect of signal control strategies on right-turn crashes, offering potentially unique and novel insights into right-turn crash causes and contributing factors. Crash-type models are estimated with the crash data of 218 signalised intersections in Queensland from 2012 to 2018. Multilevel (Hierarchical) Multinomial Logit Models with random intercepts are employed to capture the hierarchical influence of factors on crashes and unobserved heterogeneities. These models capture upper-level influences on crashes from intersection characteristics and lower-level influences from individual crash char-acteristics. The models specified in this way account for the correlation among crashes within intersections and influences on crashes across spatial scales. The model results reveal that the probabilities of the opposite approach crash type are significantly higher than the same direction and adjacent approach crash types for all right-turn signal control strategies at intersections except the split approach, for which the opposite is true. The results also suggest that the number of right-turning lanes and occupancy in conflicting lanes are positively associated with the likelihood of crashes for the same direction crash type.
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页数:18
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