Modeling Injury Severity for Nighttime and Daytime Crashes by Using Random Parameter Logit Models Accounting for Heterogeneity in Means and Variances
Understanding the factors contributing to crash severity, along with their influence degrees across different times of day, can assist in better highway design and in developing effective countermeasures for ameliorating highway safety (especially during nighttime). This study examines the influence...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/7871338 |
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| author | Chenzhu Wang Ping Zhang Fei Chen Jianchuan Cheng |
| author_facet | Chenzhu Wang Ping Zhang Fei Chen Jianchuan Cheng |
| author_sort | Chenzhu Wang |
| collection | DOAJ |
| description | Understanding the factors contributing to crash severity, along with their influence degrees across different times of day, can assist in better highway design and in developing effective countermeasures for ameliorating highway safety (especially during nighttime). This study examines the influences of risk factors on crash severity, based on comparisons of nighttime and daytime crashes. By using a random parameter approach to account for unobserved heterogeneity, multivariate logit (RPML) models are proposed to analyze the crash severity based on the explanatory factors in terms of the crash, traffic, speed, road geometry, and sight characteristics. The goodness-of-fit and predictive measures highlight the better performance of the proposed models relative to standard models, as the proposed models reduce the unobserved heterogeneity and yield higher precision. In addition, the elasticity effects of the factors are calculated to investigate and compare their impact degrees in daytime and nighttime crashes. The findings could potentially be utilized to guide highway design and policies and to develop specific safety countermeasures. |
| format | Article |
| id | doaj-art-c0e6851ffab843d1bdfb7ef516116599 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-c0e6851ffab843d1bdfb7ef5161165992025-02-03T01:07:37ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/7871338Modeling Injury Severity for Nighttime and Daytime Crashes by Using Random Parameter Logit Models Accounting for Heterogeneity in Means and VariancesChenzhu Wang0Ping Zhang1Fei Chen2Jianchuan Cheng3School of TransportationSchool of EngineeringSchool of TransportationSchool of TransportationUnderstanding the factors contributing to crash severity, along with their influence degrees across different times of day, can assist in better highway design and in developing effective countermeasures for ameliorating highway safety (especially during nighttime). This study examines the influences of risk factors on crash severity, based on comparisons of nighttime and daytime crashes. By using a random parameter approach to account for unobserved heterogeneity, multivariate logit (RPML) models are proposed to analyze the crash severity based on the explanatory factors in terms of the crash, traffic, speed, road geometry, and sight characteristics. The goodness-of-fit and predictive measures highlight the better performance of the proposed models relative to standard models, as the proposed models reduce the unobserved heterogeneity and yield higher precision. In addition, the elasticity effects of the factors are calculated to investigate and compare their impact degrees in daytime and nighttime crashes. The findings could potentially be utilized to guide highway design and policies and to develop specific safety countermeasures.http://dx.doi.org/10.1155/2022/7871338 |
| spellingShingle | Chenzhu Wang Ping Zhang Fei Chen Jianchuan Cheng Modeling Injury Severity for Nighttime and Daytime Crashes by Using Random Parameter Logit Models Accounting for Heterogeneity in Means and Variances Journal of Advanced Transportation |
| title | Modeling Injury Severity for Nighttime and Daytime Crashes by Using Random Parameter Logit Models Accounting for Heterogeneity in Means and Variances |
| title_full | Modeling Injury Severity for Nighttime and Daytime Crashes by Using Random Parameter Logit Models Accounting for Heterogeneity in Means and Variances |
| title_fullStr | Modeling Injury Severity for Nighttime and Daytime Crashes by Using Random Parameter Logit Models Accounting for Heterogeneity in Means and Variances |
| title_full_unstemmed | Modeling Injury Severity for Nighttime and Daytime Crashes by Using Random Parameter Logit Models Accounting for Heterogeneity in Means and Variances |
| title_short | Modeling Injury Severity for Nighttime and Daytime Crashes by Using Random Parameter Logit Models Accounting for Heterogeneity in Means and Variances |
| title_sort | modeling injury severity for nighttime and daytime crashes by using random parameter logit models accounting for heterogeneity in means and variances |
| url | http://dx.doi.org/10.1155/2022/7871338 |
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