## 1 Introduction  
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]. However, despite the proliferation of technological advancements, road traffic accidents remain a leading cause of premature deaths, and rank among the most pressing transportation concerns around the world [3; 4]. In particular, pedestrians are at greatest likelihood of injury from incidents where speeding cars fail to yield to them at crosswalks [5; 6]. Therefore, it is essential to alleviate fatalities and injuries of vulnerable road users (VRUs) at unsignalized crosswalks. In general, there are two ways to support road users' safety; (1) passive safety systems such as speed cameras and fences which prevent drivers and pedestrians from engaging in risky or illegal behaviors; and (2) active safety systems which analyze historical accident data and forecast future driving states, based on vehicle dynamics and specific traffic infrastructures. 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. However, most active safety systems only use traffic accident statistics to determine the improvement of an urban environment post-facto. A different strategy is to pinpoint potential traffic risk events (e.g., near-miss collision) in order to prevent accidents proactively. Current research has focused on vision sensor systems such as closed-circuit televisions (CCTVs) which have already been deployed on many roads for security reasons. With these vision sensors, potential traffic risks could be more easily analyzed, (1) by assessing pedestrian safety at unsignalized roads based on vehicle-pedestrian interactions [9; 10]; (2) recording pedestrian behavioral patterns such as walking phases and speeds [11]; and (3) guiding decision-makers and administrators with nuanced data on pedestrian-automobile interactions [11; 12]. Many studies have reliably extracted trajectories by manually inspecting large amounts of traffic surveillance video [13; 14; 15]. However, this is costly and time-consuming to do at the urban scale, so we seek to develop automated processes that generate useful data for pedestrian safety analysis. In this study, 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 CCTVs deployed on the road. We had three objectives in this study: (1) Detect traffic-related objects and translate their oblique features into overhead features through the use of simple image processing techniques; (2) automatically extract the behavioral characteristics which affect the likelihood of potential pedestrian risky events; and (3) analyze interactions between objects and then classify the degrees of risk through the use of data mining techniques. The rest of this paper is organized as follows:  
1. Materials and methods: Overview of our video dataset, methods of processing images into trajectories, and data mining on resulting trajectories. 2. Experiments and results: Visualizations and interpretation of resulting clusters, and discussion of results and limitations. 3. Conclusion: Summary of our study and future research directions.