Abstract
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.
Highlights
Around the world, many cities have adopted information and communication technologies (ICT) to create intelligent platforms within a broader smart city context, and use data to support the safety, health, and welfare of the average urban resident [1,2]
We propose a new model for the analysis of potential pedestrian risky events (PPREs) through the use of data mining techniques employed on real traffic video footage from circuit televisions (CCTVs) deployed on the road
In order to obtain the optimal number of clusters, K, we looked at the sum of squared errors between the the observations observationsand andcentroids centroidsinineach each cluster adjusting
Summary
Many cities have adopted information and communication technologies (ICT) to create intelligent platforms within a broader smart city context, and use data to support the safety, health, and welfare of the average urban resident [1,2]. A variety of studies have reported on examples of active safety systems, which include (1) the analysis of urban road infrastructure deficiencies and their relation to pedestrian accidents [6]; and (2) using long-term accident statistics to model the high fatality or injury rates of pedestrians at unsignalized crosswalks [7,8]. These are the most common types of safety systems that analyze vehicles and pedestrian behaviors, and their relationship to traffic accidents rates
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