Analyzing vehicle pedestrian interactions combining data cube structure and predictive collision risk estimation model
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Traffic accidents are a threat to human lives, particularly pedestrians causing premature deaths. Therefore, it is necessary to devise systems to prevent accidents in advance and respond proactively, using potential risky situations as one of the surrogate safety measurements. This study introduces a new concept of a pedestrian safety system that combines the field and the centralized processes. The system can warn of upcoming risks immediately in the field and improve the safety of risk frequent areas by assessing the safety levels of roads without actual collisions. In particular, this study focuses on the latter by introducing a new analytical framework for a crosswalk safety assessment with behaviors of vehicle/pedestrian and environmental features. We obtain these behavioral features from actual traffic video footage in the city with complete automatic processing. The proposed framework mainly analyzes these behaviors in multidimensional perspectives by constructing a data cube structure, which combines the LSTM based predictive collision risk estimation model and the on line analytical processing operations. From the PCR estimation model, we categorize the severity of risks as four levels and apply the proposed framework to assess the crosswalk safety with behavioral features. Our analytic experiments are based on two scenarios, and the various descriptive results are harvested the movement patterns of vehicles and pedestrians by road environment and the relationships between risk levels and car speeds. Thus, the proposed framework can support decision makers by providing valuable information to improve pedestrian safety for future accidents, and it can help us better understand their behaviors near crosswalks proactively. In order to confirm the feasibility and applicability of the proposed framework, we implement and apply it to actual operating CCTVs in Osan City, Korea.
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