Understanding the underlying relationship between pedestrian injury severity outcomes and factors leading to more severe injuries is very important in dealing with the problem of pedestrian safety. To investigate injury severity outcomes, many previous works relied on statistical regression models. There has also been some interest for data mining techniques, in particular for clustering techniques which segment the data into more homogeneous subsets. This research combines these two approaches (data mining and statistical regression methods) to identify the main contributing factors associated with the levels of pedestrian injury severity outcomes. This work relies on the analysis of two unique pedestrian injury severity datasets from the City of New York, US (2002US ( -2006 and the City of Montreal, Canada (2003-2006. General injury severity models were estimated for the whole datasets and for subpopulations obtained through clustering analysis. This paper shows how the segmentation of the accident datasets help to better understand the complex relationship between the injury severity outcomes and the contributing geometric, built environment and socio-demographic factors. While using the same methodology for the two datasets, different techniques were tested. For instance, for New York, latent class with ordered probit method provides the best results. However, for Montreal, the K-means with multinomial logit model is identified as the most appropriate technique. The results show the power of using clustering with regression to provide a complementary and more detailed analysis. Among other results, it was found that pedestrian age, location at intersection, actions prior to accident, driver age, vehicle type, vehicle movement, driver alcohol involvement and lighting conditions have an influence on the likelihood of a fatal crash. Moreover, several features within the built environment are shown to have an effect. Finally, the research provides recommendations for policy makers, traffic engineers, and law enforcement to reduce the severity of pedestrian-vehicle collisions. KEYWORDS:Pedestrian safety, regression, latent class, clustering, severity, built environmental, land use variables Mohamed, Saunier, Miranda-Moreno, Ukkusuri 3 INTRODUCTIONRoad user safety is a primary concern, not only for traffic safety specialists and traffic engineers, but for educators and law enforcement as well. Most importantly, pedestrian safety is a vital traffic issue as all road users are pedestrians at one point or another. Since pedestrians are vulnerable road users and suffer more in road crashes, it is important to understand the factors affecting pedestrian injury severity levels. In this way, traffic engineers, planners, decision makers and law enforcement will be able to precisely target these factors through various counter-measures, such as improvements to motorized vehicles, roadway and pedestrian facility design, control strategies at conflict locations, and driver and pedestrian education programs.This pape...

Intelligent Transportation Systems need methods to automatically monitor the road traffic, and especially track vehicles. Most research has concentrated on highways. Traffic in intersections is more variable, with multiple entrance and exit regions. This paper describes an extension to intersections of the feature-tracking algorithm described in [1]. Vehicle features are rarely tracked from their entrance in the field of view to their exit. Our algorithm can accommodate the problem caused by the disruption of feature tracks. It is evaluated on video sequences recorded on four different intersections.

Road collisions represent a worldwide pandemic which can be addressed through the improvement of existing tools for safety analysis. The paper presents a refined probabilistic framework for the analysis of road user interactions. In particular, the identification of potential collision points is used to estimate collision probabilities and their spatial distribution can be visualized. A probabilistic time to collision is also introduced, and interactions are categorized in four categories: head-on, rear-end, side and parallel. The framework is applied to a large dataset of video recordings containing more than 300 severe interactions and collisions collected in Kentucky. The results demonstrate the usefulness of the approach in studying road user behavior and mechanisms that may lead to collisions.

Pedestrians are vulnerable road users, and despite their limited representation in traffic events, pedestrian-involved injuries and fatalities are overrepresented in traffic collisions. However, little is known about pedestrian exposure to the risk of collision, especially when compared with the amount of knowledge available for motorized traffic. More data and analysis are therefore required to understand the processes that involve pedestrians in collisions. Collision statistics alone are inadequate for the study of pedestrian–vehicle collisions because of data quantity and quality issues. Surrogate safety measures, as provided by the collection and study of traffic conflicts, were developed as a proactive complementary approach to offer more in-depth safety analysis. However, high costs and reliability issues have inhibited the extensive application of traffic conflict analysis. An automated video analysis system is presented that can (a) detect and track road users in a traffic scene and classify them as pedestrians or motorized road users, (b) identify important events that may lead to collisions, and (c) calculate several severity conflict indicators. The system seeks to classify important events and conflicts automatically but can also be used to summarize large amounts of data that can be further reviewed by safety experts. The functionality of the system is demonstrated on a video data set collected over 2 days at an intersection in downtown Vancouver, British Columbia, Canada. Four conflict indicators are automatically computed for all pedestrian–vehicle events and provide detailed insight into the conflict process. Simple detection rules on the indicators are tested to classify traffic events. This study is unique in its attempt to extract conflict indicators from video sequences in a fully automated way.

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This paper presents a novel application of automated video analysis for a before-and-after (BA) safety evaluation of a scramble phase treatment. Data availability has been a common challenge to pedestrian studies, especially for proactive safety analysis. The traditional reliance on collision data has many shortcomings because of the quality and quantity of collision records. Qualitative and quantitative issues with road collision data are more pronounced in pedestrian safety studies. In addition, little information on the mechanism of action implicated can be drawn from collision reports. Traffic conflict techniques have been advocated as supplements or alternatives to collision-based safety analysis. Automated conflict analysis has been advocated as a new safety analysis paradigm that empowers the drawbacks of survey-based and observer-based traffic conflict analysis. One of the areas of focus of pedestrian safety that could greatly benefit from vision-based road user tracking is BA evaluation of safety treatments. This paper demonstrates the feasibility of conducting a BA analysis with video data collected from a commercial-grade camera in Chinatown, Oakland, California. Video sequences for a period of 2 h before and 2 h after scramble were automatically analyzed. The BA results of the automated analysis exhibit a declining pattern of conflict frequency, a reduction in the spatial density of conflicts, and a shift in the spatial distribution of conflicts farther from crosswalks.

Traffic safety analysis has often been undertaken with historical collision data. However, well-recognized availability and quality problems are associated with collision data. In addition, the use of collision records for safety analysis is reactive: a significant number of collisions has to be recorded before action is taken. Therefore, the observation of traffic conflicts has been advocated as a complementary approach in the analysis of traffic safety. However, incomplete conceptualization and the cost of training observers and collecting conflict data have been factors inhibiting extensive application of the traffic conflict technique. The goal of this research is to develop a method for automated analysis of road safety with video sensors to address the problem of dependency on the deteriorating collision data. The method automates the extraction of traffic conflicts from video sensor data. This method should address the main shortcomings of the traffic conflict technique. A comprehensive system is described for traffic conflict detection in video data. The system is composed of a feature-based vehicle tracking algorithm adapted for intersections and a traffic conflict detection method based on the clustering of vehicle trajectories. The clustering uses a K-means approach with hidden Markov models and a simple heuristic to find the number of clusters automatically. Traffic conflicts can then be detected by identifying and adapting pairs of models of conflicting trajectories. The technique is demonstrated on real-world video sequences of traffic conflicts.

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