@article{ref1,
title="Detection method for all types of traffic conflicts in work zones",
journal="Sustainability (Basel)",
year="2022",
author="Xu, Zhepu and Chen, Dashan",
volume="14",
number="21",
pages="e14159-e14159",
abstract="Traffic conflict technology (TCT) is widely used to assess the safety of work zones. The current TCT is temporal and (or) spatial proximity defined based, which can only detect two-vehicle or multi-vehicle conflicts, and is not competent for single-vehicle conflicts. However, single-vehicle accidents in work zones are also severe. This study proposes a detection method for all types of traffic conflicts in work zones. Based on vehicle micro-behavior data, evasive behavior is detected by automatic segmentation, Support Vector Machine (SVM)-based behavior identification, and threshold-based judgment methods. Two-vehicle or multi-vehicle conflicts are detected by classical proximity defined-based method, i.e., the surrogate safety assessment model (SSAM). By comparing the analysis results of the evasive behavior with the one of SSAM, single-vehicle conflicts can be detected. Taking a practical work zone as an example, the effectiveness of this method in detecting all types of traffic conflicts in work zones is verified. The single-vehicle conflict can be subdivided into 10 types according to basic behavior types, such as straight-line driving and decelerating. The two or multi-vehicle conflicts can be subdivided into three types, such as rear-end conflict. The example also verifies the applicability of this method under different traffic volume scenarios.<p /> <p>Language: en</p>",
language="en",
issn="2071-1050",
doi="10.3390/su142114159",
url="http://dx.doi.org/10.3390/su142114159"
}