Vision-Based Potential Pedestrian Risk Analysis on Unsignalized Crosswalk Using Data Mining Techniques

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Though the technological advancement of smart city infrastructure has significantly improved urban pedestrians' health and safety, there remains a large number of road traffic accident victims, making it a pressing current transportation concern. In particular, unsignalized crosswalks present a major threat to pedestrians, but we lack dense behavioral data to understand the risks they face. In this study, we propose a new model for potential pedestrian risky event (PPRE) analysis, using video footage gathered by road security cameras already installed at such crossings. Our system automatically detects vehicles and pedestrians, calculates trajectories, and extracts frame-level behavioral features. We use k-means clustering and decision tree algorithms to classify these events into six clusters, then visualize and interpret these clusters to show how they may or may not contribute to pedestrian risk at these crosswalks. We confirmed the feasibility of the model by applying it to video footage from unsignalized crosswalks in Osan city, South Korea.
Publisher
MDPI
Issue Date
2020-02
Language
English
Article Type
Article
Citation

APPLIED SCIENCES-BASEL, v.10, no.3

ISSN
2076-3417
DOI
10.3390/app10031057
URI
http://hdl.handle.net/10203/274159
Appears in Collection
CE-Journal Papers(저널논문)
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