Determining an Improved Traffic Conflict Indicator for Highway Safety Estimation Based on Vehicle Trajectory Data
Currently, several traffic conflict indicators are used as surrogate safety measures. Each indicator has its own advantages, limitations, and suitability. There are only a few studies focusing on fixed object conflicts of highway safety estimation using traffic conflict technique. This study investi...
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doaj-9ba31ffafabf43c7a0140d0b8ff77df02021-08-26T14:22:44ZengMDPI AGSustainability2071-10502021-08-01139278927810.3390/su13169278Determining an Improved Traffic Conflict Indicator for Highway Safety Estimation Based on Vehicle Trajectory DataRuoxi Jiang0Shunying Zhu1Hongguang Chang2Jingan Wu3Naikan Ding4Bing Liu5Ji Qiu6School of Transportation, Wuhan University of Technology, 1178 Heping Avenue, Wuhan 430063, ChinaSchool of Transportation, Wuhan University of Technology, 1178 Heping Avenue, Wuhan 430063, ChinaSchool of Transportation, Wuhan University of Technology, 1178 Heping Avenue, Wuhan 430063, ChinaSchool of Transportation, Wuhan University of Technology, 1178 Heping Avenue, Wuhan 430063, ChinaSchool of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan 430205, ChinaSchool of Transportation, Wuhan University of Technology, 1178 Heping Avenue, Wuhan 430063, ChinaSchool of Transportation, Wuhan University of Technology, 1178 Heping Avenue, Wuhan 430063, ChinaCurrently, several traffic conflict indicators are used as surrogate safety measures. Each indicator has its own advantages, limitations, and suitability. There are only a few studies focusing on fixed object conflicts of highway safety estimation using traffic conflict technique. This study investigated which conflict indicator was more suitable for traffic safety estimation based on conflict-accident Pearson correlation analysis. First, a high-altitude unmanned aerial vehicle was used to collect multiple continuous high-precision videos of the Jinan-Qingdao highway. The vehicle trajectory data outputted from recognition of the videos were used to acquire conflict data following the procedure for each conflict indicator. Then, an improved indicator T<sub>i</sub> was proposed based on the advantages and limitations of the conventional indicators. This indicator contained definitions and calculation for three types of traffic conflicts (rear-end, lane change and with fixed object). Then the conflict-accident correlation analysis of TTC (Time to Collision)/PET (Post Encroachment Time)/DRAC (Deceleration Rate to Avoid Crash)/T<sub>i</sub> indicators were carried out. The results show that the average value of the correlation coefficient for each indicator with different thresholds are 0.670 for TTC, 0.669 for PET, and 0.710 for DRAC, and 0.771 for T<sub>i</sub>, which T<sub>i</sub> indicator is obviously higher than the other three conventional indicators. The findings of this study suggest TTC often fails to identify lane change conflicts, PET indicator easily misjudges some rear-end conflict when the speed of the following vehicle is slower than the leading vehicle, and PET is less informative than other indicators. At the same time, these conventional indicators do not consider the vehicle-fixed objects conflicts. The improved T<sub>i</sub> can overcome these shortcomings; thus, T<sub>i</sub> has the highest correlation. More data are needed to verify and support the study.https://www.mdpi.com/2071-1050/13/16/9278traffic safety estimationtraffic conflict techniquetraffic conflict indicatorhighwayvehicle trajectory dataUAV |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ruoxi Jiang Shunying Zhu Hongguang Chang Jingan Wu Naikan Ding Bing Liu Ji Qiu |
spellingShingle |
Ruoxi Jiang Shunying Zhu Hongguang Chang Jingan Wu Naikan Ding Bing Liu Ji Qiu Determining an Improved Traffic Conflict Indicator for Highway Safety Estimation Based on Vehicle Trajectory Data Sustainability traffic safety estimation traffic conflict technique traffic conflict indicator highway vehicle trajectory data UAV |
author_facet |
Ruoxi Jiang Shunying Zhu Hongguang Chang Jingan Wu Naikan Ding Bing Liu Ji Qiu |
author_sort |
Ruoxi Jiang |
title |
Determining an Improved Traffic Conflict Indicator for Highway Safety Estimation Based on Vehicle Trajectory Data |
title_short |
Determining an Improved Traffic Conflict Indicator for Highway Safety Estimation Based on Vehicle Trajectory Data |
title_full |
Determining an Improved Traffic Conflict Indicator for Highway Safety Estimation Based on Vehicle Trajectory Data |
title_fullStr |
Determining an Improved Traffic Conflict Indicator for Highway Safety Estimation Based on Vehicle Trajectory Data |
title_full_unstemmed |
Determining an Improved Traffic Conflict Indicator for Highway Safety Estimation Based on Vehicle Trajectory Data |
title_sort |
determining an improved traffic conflict indicator for highway safety estimation based on vehicle trajectory data |
publisher |
MDPI AG |
series |
Sustainability |
issn |
2071-1050 |
publishDate |
2021-08-01 |
description |
Currently, several traffic conflict indicators are used as surrogate safety measures. Each indicator has its own advantages, limitations, and suitability. There are only a few studies focusing on fixed object conflicts of highway safety estimation using traffic conflict technique. This study investigated which conflict indicator was more suitable for traffic safety estimation based on conflict-accident Pearson correlation analysis. First, a high-altitude unmanned aerial vehicle was used to collect multiple continuous high-precision videos of the Jinan-Qingdao highway. The vehicle trajectory data outputted from recognition of the videos were used to acquire conflict data following the procedure for each conflict indicator. Then, an improved indicator T<sub>i</sub> was proposed based on the advantages and limitations of the conventional indicators. This indicator contained definitions and calculation for three types of traffic conflicts (rear-end, lane change and with fixed object). Then the conflict-accident correlation analysis of TTC (Time to Collision)/PET (Post Encroachment Time)/DRAC (Deceleration Rate to Avoid Crash)/T<sub>i</sub> indicators were carried out. The results show that the average value of the correlation coefficient for each indicator with different thresholds are 0.670 for TTC, 0.669 for PET, and 0.710 for DRAC, and 0.771 for T<sub>i</sub>, which T<sub>i</sub> indicator is obviously higher than the other three conventional indicators. The findings of this study suggest TTC often fails to identify lane change conflicts, PET indicator easily misjudges some rear-end conflict when the speed of the following vehicle is slower than the leading vehicle, and PET is less informative than other indicators. At the same time, these conventional indicators do not consider the vehicle-fixed objects conflicts. The improved T<sub>i</sub> can overcome these shortcomings; thus, T<sub>i</sub> has the highest correlation. More data are needed to verify and support the study. |
topic |
traffic safety estimation traffic conflict technique traffic conflict indicator highway vehicle trajectory data UAV |
url |
https://www.mdpi.com/2071-1050/13/16/9278 |
work_keys_str_mv |
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