Investigating hierarchical effects of adaptive signal control system on crash severity using random-parameter ordered regression models incorporating observed heterogeneity.

Adaptive Signal control system Crash severity Ordered regression model Safety Unobserved and observed heterogeneity

Journal

Accident; analysis and prevention
ISSN: 1879-2057
Titre abrégé: Accid Anal Prev
Pays: England
ID NLM: 1254476

Informations de publication

Date de publication:
Feb 2021
Historique:
pubmed: 12 12 2020
medline: 22 6 2021
entrez: 11 12 2020
Statut: ppublish

Résumé

By handling conflicting traffic movements and establishing dynamic coordination between intersections in real-time, the Adaptive Signal Control System (ASCS) can potentially improve the operation and safety of signalized intersections on a corridor. This study identifies the hierarchical effects of ASCS on the crash severity by exploring the heterogeneous effect of ASCS on the crash severity. Four different random-parameter ordered regression models (two ordered probit models, and two ordered logit models) are developed and compared. The analysis reveals that the random-parameter ordered probit and logit models (ROP and ROL) with observed heterogeneity perform better than the random-parameter ordered probit and logit models (RP and RL) without observed heterogeneity in terms of the Akaike information criteria and the goodness of fit of the model. The ROP model performs better than the ROL model in terms of classification model performance measures. The ROP model enables parameters (i.e., the coefficients of the explanatory variables) to vary as a function of explanatory variables as well as across observations, thus accounting for both observed (captured by available explanatory variables) and unobserved (not captured by available explanatory variables) heterogeneity. The analysis reveals that the presence of ASCS is associated with lower crash severity. In this study, observed heterogeneity of ASCS effects on the crash severity is captured by variables related to the intersection and corridor features. Other contributing factors besides ASCS, such as annual average daily traffic, speed limit, lighting, peak period, crash type (rear-end, angle), and pedestrian involvements, are also associated with the probability of crash severity. Unobserved heterogeneity of the effect of angle crash type on the crash severity is found to exist across the observations. The findings of this research have practical implications for establishing ASCS implementation guidelines in lowering the probability of higher crash severity.

Identifiants

pubmed: 33307479
pii: S0001-4575(20)31715-2
doi: 10.1016/j.aap.2020.105895
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

105895

Informations de copyright

Copyright © 2020 Elsevier Ltd. All rights reserved.

Auteurs

Weimin Jin (W)

Glenn Department of Civil Engineering, Clemson University, Clemson, SC, 29634, USA. Electronic address: weiminj@g.clemson.edu.

Mashrur Chowdhury (M)

Glenn Department of Civil Engineering, Clemson University, Clemson, SC, 29634, USA. Electronic address: mac@clemson.edu.

M Sabbir Salek (MS)

Glenn Department of Civil Engineering, Clemson University, Clemson, SC, 29634, USA. Electronic address: msalek@clemson.edu.

Sakib Mahmud Khan (SM)

Center for Connected Multimodal Mobility, Clemson University, Clemson, SC, 29634, USA. Electronic address: sakibk@clemson.edu.

Patrick Gerard (P)

School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC, 29634, USA. Electronic address: pgerard@clemson.edu.

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