Exploring Risk Factors with Crash Severity on China Two-Lane Rural Roads Using a Random-Parameter Ordered Probit Model
Understanding the factors that contribute to traffic crashes can help provide a fundamental basis to plan and develop appropriate countermeasures for road safety issues emerging in particular on two-lane rural roads. However, most of the studies have focused on urban roadways and freeway systems, an...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/8870497 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832566506689396736 |
|---|---|
| author | Shikun Xie Xiaofeng Ji Wenchen Yang Rui Fang Jingjing Hao |
| author_facet | Shikun Xie Xiaofeng Ji Wenchen Yang Rui Fang Jingjing Hao |
| author_sort | Shikun Xie |
| collection | DOAJ |
| description | Understanding the factors that contribute to traffic crashes can help provide a fundamental basis to plan and develop appropriate countermeasures for road safety issues emerging in particular on two-lane rural roads. However, most of the studies have focused on urban roadways and freeway systems, and few studies have investigated the issue of heterogeneity on two-lane rural roads. The purpose of this study is to uncover the risk factors influencing crash severity on two-lane rural roads in China. A sample of 1490 traffic crashes occurring on two-lane rural roads between 2012 and 2017 was collected from the Mouding County Highway Bureau in Yunnan, China. A random-parameter ordered probit model was estimated using these data to capture underlying unobserved characteristics in personal traits, vehicle attributes, roadway conditions, environmental factors, and crash attribute. To better understand the effect of critical factors on crash severity outcome probability, an elasticity analysis was then introduced. The results show that six factors such as driver’s attribution, illegal driving behaviour, access segment, day of week, vehicle type, and crash form have a significant impact on the injury severity, and the impacts of driving behaviours, access segment, and vehicle-fixed object crashes had significant variation across observations. Besides, the correlations between critical factors and the probability of serious injury sustained in traffic crashes are identified and discussed. The local driver indicator has more positive impact on the crash severity than nonlocal driver, and nonaccess segment appears a higher probability of serious or vicious collisions. It is worth mentioning that motorcycle-involved crashes do show an obvious correlation with crash injury severity. As for crash forms, vehicle-vehicle crashes are more likely to lead to severe crash injury. Besides, high-risk driving behaviour (e.g., fatigue driving, speeding, and converse driving), weekends, and holidays are found to have significant contribution to increasing the probability of traffic crash injuries and fatalities on two-lane rural roads. |
| format | Article |
| id | doaj-art-caa84d2fa1e84ca5a3125332982b386d |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-caa84d2fa1e84ca5a3125332982b386d2025-02-03T01:04:03ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/88704978870497Exploring Risk Factors with Crash Severity on China Two-Lane Rural Roads Using a Random-Parameter Ordered Probit ModelShikun Xie0Xiaofeng Ji1Wenchen Yang2Rui Fang3Jingjing Hao4Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650504, ChinaFaculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650504, ChinaNational Engineering Laboratory for Surface Transportation Weather Impacts Prevention, Broadvision Engineering Consultants, Kunming 650200, ChinaNational Engineering Laboratory for Surface Transportation Weather Impacts Prevention, Broadvision Engineering Consultants, Kunming 650200, ChinaSchool of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, ChinaUnderstanding the factors that contribute to traffic crashes can help provide a fundamental basis to plan and develop appropriate countermeasures for road safety issues emerging in particular on two-lane rural roads. However, most of the studies have focused on urban roadways and freeway systems, and few studies have investigated the issue of heterogeneity on two-lane rural roads. The purpose of this study is to uncover the risk factors influencing crash severity on two-lane rural roads in China. A sample of 1490 traffic crashes occurring on two-lane rural roads between 2012 and 2017 was collected from the Mouding County Highway Bureau in Yunnan, China. A random-parameter ordered probit model was estimated using these data to capture underlying unobserved characteristics in personal traits, vehicle attributes, roadway conditions, environmental factors, and crash attribute. To better understand the effect of critical factors on crash severity outcome probability, an elasticity analysis was then introduced. The results show that six factors such as driver’s attribution, illegal driving behaviour, access segment, day of week, vehicle type, and crash form have a significant impact on the injury severity, and the impacts of driving behaviours, access segment, and vehicle-fixed object crashes had significant variation across observations. Besides, the correlations between critical factors and the probability of serious injury sustained in traffic crashes are identified and discussed. The local driver indicator has more positive impact on the crash severity than nonlocal driver, and nonaccess segment appears a higher probability of serious or vicious collisions. It is worth mentioning that motorcycle-involved crashes do show an obvious correlation with crash injury severity. As for crash forms, vehicle-vehicle crashes are more likely to lead to severe crash injury. Besides, high-risk driving behaviour (e.g., fatigue driving, speeding, and converse driving), weekends, and holidays are found to have significant contribution to increasing the probability of traffic crash injuries and fatalities on two-lane rural roads.http://dx.doi.org/10.1155/2020/8870497 |
| spellingShingle | Shikun Xie Xiaofeng Ji Wenchen Yang Rui Fang Jingjing Hao Exploring Risk Factors with Crash Severity on China Two-Lane Rural Roads Using a Random-Parameter Ordered Probit Model Journal of Advanced Transportation |
| title | Exploring Risk Factors with Crash Severity on China Two-Lane Rural Roads Using a Random-Parameter Ordered Probit Model |
| title_full | Exploring Risk Factors with Crash Severity on China Two-Lane Rural Roads Using a Random-Parameter Ordered Probit Model |
| title_fullStr | Exploring Risk Factors with Crash Severity on China Two-Lane Rural Roads Using a Random-Parameter Ordered Probit Model |
| title_full_unstemmed | Exploring Risk Factors with Crash Severity on China Two-Lane Rural Roads Using a Random-Parameter Ordered Probit Model |
| title_short | Exploring Risk Factors with Crash Severity on China Two-Lane Rural Roads Using a Random-Parameter Ordered Probit Model |
| title_sort | exploring risk factors with crash severity on china two lane rural roads using a random parameter ordered probit model |
| url | http://dx.doi.org/10.1155/2020/8870497 |
| work_keys_str_mv | AT shikunxie exploringriskfactorswithcrashseverityonchinatwolaneruralroadsusingarandomparameterorderedprobitmodel AT xiaofengji exploringriskfactorswithcrashseverityonchinatwolaneruralroadsusingarandomparameterorderedprobitmodel AT wenchenyang exploringriskfactorswithcrashseverityonchinatwolaneruralroadsusingarandomparameterorderedprobitmodel AT ruifang exploringriskfactorswithcrashseverityonchinatwolaneruralroadsusingarandomparameterorderedprobitmodel AT jingjinghao exploringriskfactorswithcrashseverityonchinatwolaneruralroadsusingarandomparameterorderedprobitmodel |