Predicting Crash Frequency for Urban Expressway considering Collision Types Using Real-Time Traffic Data
Current studies on traffic crash prediction mainly focus on the crash frequency and crash severity of freeways or arterials. However, collision type for urban expressway crash is rarely considered. Meanwhile, with the rapid development of urban expressway systems in China in recent years, traffic sa...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/8523818 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832556321656799232 |
|---|---|
| author | Hui Zhang Siyao Li Chaozhong Wu Qi Zhang Yafen Wang |
| author_facet | Hui Zhang Siyao Li Chaozhong Wu Qi Zhang Yafen Wang |
| author_sort | Hui Zhang |
| collection | DOAJ |
| description | Current studies on traffic crash prediction mainly focus on the crash frequency and crash severity of freeways or arterials. However, collision type for urban expressway crash is rarely considered. Meanwhile, with the rapid development of urban expressway systems in China in recent years, traffic safety problems have attracted more attention. In addition, the traffic characteristics are considered to be a potentially important predictor of traffic accidents; however, their impact on crashes has been controversial. Therefore, a crash frequency predicting model for urban expressway considering collision types is proposed in this study. The loop detector traffic data and historical crash data were aggregated based on the similarities of the traffic conditions 5 minutes before crash occurrence, among which crashes were divided by collision type (rear-end collision and side-impact collision). The impact of traffic characteristics along with weather variables as well as their interactions on crash frequency was modelled by using negative binomial regression model. The results indicated that the influence of traffic and weather factors on two collision types shared similar trend, but different level. For rear-end collisions, crash frequency increased with lower average speed and high traffic volume under low speed limit. And when the speed limit is high, higher average speed coupled with larger volume increases the probability of crash. Higher average speed and traffic volume increase the probability of side-impact collisions, without being affected by the speed limit. The findings of the present study could help to determine efficient safety countermeasures aimed at improving the safety performance of urban expressway. |
| format | Article |
| id | doaj-art-2a1cfc0e4bd94ecbadd23f37c0e470b1 |
| 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-2a1cfc0e4bd94ecbadd23f37c0e470b12025-02-03T05:45:45ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/85238188523818Predicting Crash Frequency for Urban Expressway considering Collision Types Using Real-Time Traffic DataHui Zhang0Siyao Li1Chaozhong Wu2Qi Zhang3Yafen Wang4Intelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, ChinaIntelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, ChinaIntelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, ChinaIntelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan 430063, ChinaRoad Traffic Safety Risk Prevention and Control Center, Wuhan Traffic Management Bureau, Wuhan Municipal Public Security Bureau, Wuhan, ChinaCurrent studies on traffic crash prediction mainly focus on the crash frequency and crash severity of freeways or arterials. However, collision type for urban expressway crash is rarely considered. Meanwhile, with the rapid development of urban expressway systems in China in recent years, traffic safety problems have attracted more attention. In addition, the traffic characteristics are considered to be a potentially important predictor of traffic accidents; however, their impact on crashes has been controversial. Therefore, a crash frequency predicting model for urban expressway considering collision types is proposed in this study. The loop detector traffic data and historical crash data were aggregated based on the similarities of the traffic conditions 5 minutes before crash occurrence, among which crashes were divided by collision type (rear-end collision and side-impact collision). The impact of traffic characteristics along with weather variables as well as their interactions on crash frequency was modelled by using negative binomial regression model. The results indicated that the influence of traffic and weather factors on two collision types shared similar trend, but different level. For rear-end collisions, crash frequency increased with lower average speed and high traffic volume under low speed limit. And when the speed limit is high, higher average speed coupled with larger volume increases the probability of crash. Higher average speed and traffic volume increase the probability of side-impact collisions, without being affected by the speed limit. The findings of the present study could help to determine efficient safety countermeasures aimed at improving the safety performance of urban expressway.http://dx.doi.org/10.1155/2020/8523818 |
| spellingShingle | Hui Zhang Siyao Li Chaozhong Wu Qi Zhang Yafen Wang Predicting Crash Frequency for Urban Expressway considering Collision Types Using Real-Time Traffic Data Journal of Advanced Transportation |
| title | Predicting Crash Frequency for Urban Expressway considering Collision Types Using Real-Time Traffic Data |
| title_full | Predicting Crash Frequency for Urban Expressway considering Collision Types Using Real-Time Traffic Data |
| title_fullStr | Predicting Crash Frequency for Urban Expressway considering Collision Types Using Real-Time Traffic Data |
| title_full_unstemmed | Predicting Crash Frequency for Urban Expressway considering Collision Types Using Real-Time Traffic Data |
| title_short | Predicting Crash Frequency for Urban Expressway considering Collision Types Using Real-Time Traffic Data |
| title_sort | predicting crash frequency for urban expressway considering collision types using real time traffic data |
| url | http://dx.doi.org/10.1155/2020/8523818 |
| work_keys_str_mv | AT huizhang predictingcrashfrequencyforurbanexpresswayconsideringcollisiontypesusingrealtimetrafficdata AT siyaoli predictingcrashfrequencyforurbanexpresswayconsideringcollisiontypesusingrealtimetrafficdata AT chaozhongwu predictingcrashfrequencyforurbanexpresswayconsideringcollisiontypesusingrealtimetrafficdata AT qizhang predictingcrashfrequencyforurbanexpresswayconsideringcollisiontypesusingrealtimetrafficdata AT yafenwang predictingcrashfrequencyforurbanexpresswayconsideringcollisiontypesusingrealtimetrafficdata |