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Crosswalks present a major threat to pedestrians, but we lack dense behavioral data to investigate the risks they face. One of the breakthroughs is to analyze potential risky behaviors of the road users (e.g., near-miss collision), which can provide clues to take actions such as deployment of additional safety infrastructures. In order to capture these subtle potential risky situations and behaviors, the use of vision sensors makes it easier to study and analyze potential traffic risks. In this study, we introduce a new approach to obtain the potential risky behaviors of vehicles and pedestrians from CCTV cameras deployed on the roads. This study has three novel contributions: (1) recasting CCTV cameras for surveillance to contribute to the study of the crossing environment; (2) creating one sequential process from partitioning video to extracting their behavioral features; and (3) analyzing the extracted behavioral features and clarifying the interactive moving patterns by the crossing environment. These kinds of data are the foundation for understanding road users' risky behaviors, and further support decision makers for their efficient decisions in improving and making a safer road environment. We validate the feasibility of this model by applying it to video footage collected from crosswalks in various conditions in Osan City, Republic of Korea.

Road traffic accidents are a severe threat to human lives, particularly to vulnerable road users (VRUs) such as 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 various behaviors of vehicles/pedestrians and environmental features. We obtain these behavioral features from actual traffic video footages in the city with complete automatic processing. The proposed framework mainly analyzes these behaviors in multi-dimensional perspectives by constructing a data cube structure, which combines the Long Short-Term Memory (LSTM)-based predictive collision risk (PCR) estimation model and the on-line analytical processing (OLAP) operations. From the PCR estimation model, we categorize the severity of risks as four levels; "relatively safe," "caution," "warning," and "danger," and apply the proposed framework to assess the crosswalk safety with behavioral features. With the proposed framework, the various descriptive results are harvested, but we aim at conducting analysis based on two scenarios in our analytic experiments; the movement patterns of vehicles and pedestrians by road environment and the relationships between risk levels and car speeds. Consequently, the proposed framework can support decision-makers (e.g., urban planners, safety administrators) by providing the valuable information to improve pedestrian safety for future accidents, and it can help us better understand cars' and pedestrians' proactive behavior near the crosswalks. In order to confirm the feasibility and applicability of the proposed framework, we implement and apply it to actual operating CCTVs in Osan City, Republic of Korea., (Copyright © 2021 Elsevier Ltd. All rights reserved.)