Analyzing vehicle pedestrian interactions combining data cube structure
and predictive collision risk estimation model
- URL: http://arxiv.org/abs/2107.12507v1
- Date: Mon, 26 Jul 2021 23:00:56 GMT
- Title: Analyzing vehicle pedestrian interactions combining data cube structure
and predictive collision risk estimation model
- Authors: Byeongjoon Noh, Hansaem Park, Hwasoo Yeo
- Abstract summary: 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.
- Score: 5.73658856166614
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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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