Application of the Bayesian Model Averaging in Analyzing Freeway Traffic Incident Clearance Time for Emergency Management
Identifying the influential factors in incident duration is important for traffic management agency to mitigate the impact of traffic incidents on freeway operation. Previous studies have proposed a variety of approaches to determine the significant factors for traffic incident clearance time. These...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/6671983 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832568587437473792 |
|---|---|
| author | Yajie Zou Bo Lin Xiaoxue Yang Lingtao Wu Malik Muneeb Abid Jinjun Tang |
| author_facet | Yajie Zou Bo Lin Xiaoxue Yang Lingtao Wu Malik Muneeb Abid Jinjun Tang |
| author_sort | Yajie Zou |
| collection | DOAJ |
| description | Identifying the influential factors in incident duration is important for traffic management agency to mitigate the impact of traffic incidents on freeway operation. Previous studies have proposed a variety of approaches to determine the significant factors for traffic incident clearance time. These methods commonly select a single “true” model among a majority of alternative models based on some model selection criteria. However, the conventional methods generally neglect the uncertainty related to the choice of models. This paper proposes a Bayesian Model Averaging (BMA) model to account for model uncertainty by averaging all plausible models using posterior probability as the weight. The BMA model is used to analyze the 2,584 freeway incident records obtained from I-5 corridor in Seattle, WA, USA. The results show that the BMA approach has the capability of interpreting the causal relationship between explanatory variables and clearance time. In addition, the BMA approach can provide better prediction performance than the Cox proportional hazards model and the accelerated failure time models. Overall, the findings in this study can be useful for traffic emergency management agency to apply an alternative methodology for predicting traffic incident clearance time when model uncertainty is considered. |
| format | Article |
| id | doaj-art-99d866c5ed754b16a4fe2fe407fe7ee4 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-99d866c5ed754b16a4fe2fe407fe7ee42025-02-03T00:58:47ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/66719836671983Application of the Bayesian Model Averaging in Analyzing Freeway Traffic Incident Clearance Time for Emergency ManagementYajie Zou0Bo Lin1Xiaoxue Yang2Lingtao Wu3Malik Muneeb Abid4Jinjun Tang5Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai 201804, ChinaKey Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai 201804, ChinaKey Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai 201804, ChinaTexas A&M Transportation Institute, Texas A&M University System, 3135, TAMU, College Station, TX 77843-3135, USADepartment of Civil Engineering, College of Engineering and Technology, University of Sargodha, Sargodha, PakistanThe School of Traffic & Transportation Engineering, Central South University, Changsha 410075, ChinaIdentifying the influential factors in incident duration is important for traffic management agency to mitigate the impact of traffic incidents on freeway operation. Previous studies have proposed a variety of approaches to determine the significant factors for traffic incident clearance time. These methods commonly select a single “true” model among a majority of alternative models based on some model selection criteria. However, the conventional methods generally neglect the uncertainty related to the choice of models. This paper proposes a Bayesian Model Averaging (BMA) model to account for model uncertainty by averaging all plausible models using posterior probability as the weight. The BMA model is used to analyze the 2,584 freeway incident records obtained from I-5 corridor in Seattle, WA, USA. The results show that the BMA approach has the capability of interpreting the causal relationship between explanatory variables and clearance time. In addition, the BMA approach can provide better prediction performance than the Cox proportional hazards model and the accelerated failure time models. Overall, the findings in this study can be useful for traffic emergency management agency to apply an alternative methodology for predicting traffic incident clearance time when model uncertainty is considered.http://dx.doi.org/10.1155/2021/6671983 |
| spellingShingle | Yajie Zou Bo Lin Xiaoxue Yang Lingtao Wu Malik Muneeb Abid Jinjun Tang Application of the Bayesian Model Averaging in Analyzing Freeway Traffic Incident Clearance Time for Emergency Management Journal of Advanced Transportation |
| title | Application of the Bayesian Model Averaging in Analyzing Freeway Traffic Incident Clearance Time for Emergency Management |
| title_full | Application of the Bayesian Model Averaging in Analyzing Freeway Traffic Incident Clearance Time for Emergency Management |
| title_fullStr | Application of the Bayesian Model Averaging in Analyzing Freeway Traffic Incident Clearance Time for Emergency Management |
| title_full_unstemmed | Application of the Bayesian Model Averaging in Analyzing Freeway Traffic Incident Clearance Time for Emergency Management |
| title_short | Application of the Bayesian Model Averaging in Analyzing Freeway Traffic Incident Clearance Time for Emergency Management |
| title_sort | application of the bayesian model averaging in analyzing freeway traffic incident clearance time for emergency management |
| url | http://dx.doi.org/10.1155/2021/6671983 |
| work_keys_str_mv | AT yajiezou applicationofthebayesianmodelaveraginginanalyzingfreewaytrafficincidentclearancetimeforemergencymanagement AT bolin applicationofthebayesianmodelaveraginginanalyzingfreewaytrafficincidentclearancetimeforemergencymanagement AT xiaoxueyang applicationofthebayesianmodelaveraginginanalyzingfreewaytrafficincidentclearancetimeforemergencymanagement AT lingtaowu applicationofthebayesianmodelaveraginginanalyzingfreewaytrafficincidentclearancetimeforemergencymanagement AT malikmuneebabid applicationofthebayesianmodelaveraginginanalyzingfreewaytrafficincidentclearancetimeforemergencymanagement AT jinjuntang applicationofthebayesianmodelaveraginginanalyzingfreewaytrafficincidentclearancetimeforemergencymanagement |