Investigating the Differences of Single-Vehicle and Multivehicle Accident Probability Using Mixed Logit Model
Road traffic accidents are believed to be associated with not only road geometric feature and traffic characteristic, but also weather condition. To address these safety issues, it is of paramount importance to understand how these factors affect the occurrences of the crashes. Existing studies have...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2018/2702360 |
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| author | Bowen Dong Xiaoxiang Ma Feng Chen Suren Chen |
| author_facet | Bowen Dong Xiaoxiang Ma Feng Chen Suren Chen |
| author_sort | Bowen Dong |
| collection | DOAJ |
| description | Road traffic accidents are believed to be associated with not only road geometric feature and traffic characteristic, but also weather condition. To address these safety issues, it is of paramount importance to understand how these factors affect the occurrences of the crashes. Existing studies have suggested that the mechanisms of single-vehicle (SV) accidents and multivehicle (MV) accidents can be very different. Few studies were conducted to examine the difference of SV and MV accident probability by addressing unobserved heterogeneity at the same time. To investigate the different contributing factors on SV and MV, a mixed logit model is employed using disaggregated data with the response variable categorized as no accidents, SV accidents, and MV accidents. The results indicate that, in addition to speed gap, length of segment, and wet road surfaces which are significant for both SV and MV accidents, most of other variables are significant only for MV accidents. Traffic, road, and surface characteristics are main influence factors of SV and MV accident possibility. Hourly traffic volume, inside shoulder width, and wet road surface are found to produce statistically significant random parameters. Their effects on the possibility of SV and MV accident vary across different road segments. |
| format | Article |
| id | doaj-art-6ed3835c54fa4902bf0a9679031764e7 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-6ed3835c54fa4902bf0a9679031764e72025-02-03T01:25:20ZengWileyJournal of Advanced Transportation0197-67292042-31952018-01-01201810.1155/2018/27023602702360Investigating the Differences of Single-Vehicle and Multivehicle Accident Probability Using Mixed Logit ModelBowen Dong0Xiaoxiang Ma1Feng Chen2Suren Chen3College of Traffic Engineering and Key Laboratory of Road & Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai 201804, ChinaCollege of Traffic Engineering and Key Laboratory of Road & Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai 201804, ChinaCollege of Traffic Engineering and Key Laboratory of Road & Traffic Engineering of the Ministry of Education, Tongji University, 4800 Cao’an Road, Shanghai 201804, ChinaDepartment of Civil & Environmental Engineering, Colorado State University, Fort Collins, CO 80523, USARoad traffic accidents are believed to be associated with not only road geometric feature and traffic characteristic, but also weather condition. To address these safety issues, it is of paramount importance to understand how these factors affect the occurrences of the crashes. Existing studies have suggested that the mechanisms of single-vehicle (SV) accidents and multivehicle (MV) accidents can be very different. Few studies were conducted to examine the difference of SV and MV accident probability by addressing unobserved heterogeneity at the same time. To investigate the different contributing factors on SV and MV, a mixed logit model is employed using disaggregated data with the response variable categorized as no accidents, SV accidents, and MV accidents. The results indicate that, in addition to speed gap, length of segment, and wet road surfaces which are significant for both SV and MV accidents, most of other variables are significant only for MV accidents. Traffic, road, and surface characteristics are main influence factors of SV and MV accident possibility. Hourly traffic volume, inside shoulder width, and wet road surface are found to produce statistically significant random parameters. Their effects on the possibility of SV and MV accident vary across different road segments.http://dx.doi.org/10.1155/2018/2702360 |
| spellingShingle | Bowen Dong Xiaoxiang Ma Feng Chen Suren Chen Investigating the Differences of Single-Vehicle and Multivehicle Accident Probability Using Mixed Logit Model Journal of Advanced Transportation |
| title | Investigating the Differences of Single-Vehicle and Multivehicle Accident Probability Using Mixed Logit Model |
| title_full | Investigating the Differences of Single-Vehicle and Multivehicle Accident Probability Using Mixed Logit Model |
| title_fullStr | Investigating the Differences of Single-Vehicle and Multivehicle Accident Probability Using Mixed Logit Model |
| title_full_unstemmed | Investigating the Differences of Single-Vehicle and Multivehicle Accident Probability Using Mixed Logit Model |
| title_short | Investigating the Differences of Single-Vehicle and Multivehicle Accident Probability Using Mixed Logit Model |
| title_sort | investigating the differences of single vehicle and multivehicle accident probability using mixed logit model |
| url | http://dx.doi.org/10.1155/2018/2702360 |
| work_keys_str_mv | AT bowendong investigatingthedifferencesofsinglevehicleandmultivehicleaccidentprobabilityusingmixedlogitmodel AT xiaoxiangma investigatingthedifferencesofsinglevehicleandmultivehicleaccidentprobabilityusingmixedlogitmodel AT fengchen investigatingthedifferencesofsinglevehicleandmultivehicleaccidentprobabilityusingmixedlogitmodel AT surenchen investigatingthedifferencesofsinglevehicleandmultivehicleaccidentprobabilityusingmixedlogitmodel |