Pedestrian Safety Analysis in Mixed Traffic Conditions Using Video Data

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Pedestrian Safety Analysis in Mixed Traffic Conditions Using Video Data

Zhang, Yingying / Yao, Danya / Qiu, Tony Z. / Peng, Lihui / Zhang, Yi

With the dramatic development of image processing technology, a growing number of traffic flow detection and analyses have been conducted by using video data. Time to collision (TTC) and postencroachment time (PET) are two major parameters used to indicate the severity of a potential collision and to capture an imminent vehicular accident. However, microlevel pedestrian-involved collisions are less studied because they are hard to observe or record. This paper tries to extract the traffic object locations from video data, to define the time difference to collision (TDTC) parameter as a variation from TTC and PET to fit the pedestrian-involved potential collisions/conflicts, analyze the interaction behavior between pedestrian and vehicles, and validate the TDTC parameter in indicating pedestrian safety performance by using 100 groups of interaction data. The results show that the interaction cases with larger TDTC values are safer, whereas the cases with continuously closer to zero TDTC values are more dangerous. About 80% of the cases classified by the TDTC parameter have the same result with the independent observation; if TDTC is combined with vehicle speed, the classification result can be improved. More mixed traffic scenes will be conducted based on this research in the future.