The potential for using computer vision techniques to solve several shortcomings associated with traditional road safety and behavior analysis is demonstrated. Surrogate data such as traffic conflicts provide invaluable information that can be used to understand collision-contributing factors and the collision failure mechanism better. Recent advances in computer vision techniques have encouraged the use of proactive safety surrogate measures such as detection of conflicts and violations. The objective of this study is to demonstrate the automated safety diagnosis of pedestrian crossing safety issues by using computer vision techniques. The automated safety diagnosis is applied at a major signalized intersection in downtown Vancouver, British Columbia, Canada, at which concerns had been raised regarding the high conflict rate between vehicles and pedestrians as well as the elevated number of traffic violations (i.e., jaywalking). This study is unique in its attempt to extract conflict indicators and detect violations from video sequences in a fully automated way. This line of research benefits safety experts because it provides a prompt and objective safety evaluation for intersections. The research also provides a permanent database for traffic information that can be beneficial for a sound safety diagnosis as well as for developing safety countermeasures.

Summary There has been a growing interest in using surrogate safety measures such as traffic conflicts to analyse road safety from a broader perspective than collision data alone. This growing interest has been aided by recent advances in automated video‐based traffic conflict analysis. The automation enables accurate calculation of various conflict indicators such as time‐to‐collision and post‐encroachment time. These indicators rely on road users getting within specific temporal and spatial proximity from each other and therefore assume that proximity is a surrogate for conflict severity. However, this assumption may not be valid in many driving environments where close interactions between road users are common. The objective of this paper is to investigate the applicability of time proximity conflict indicators for evaluating pedestrian safety in less‐organized traffic environments with a high mix of road users. Several alternative behavioural conflict indicators based on detecting pedestrian evasive actions are recommended to better measure traffic conflicts in such traffic environments. These indicators represent variations in the spatio‐temporal gait parameters (step length, step frequency and walk ratio) immediately before the conflict point. A highly congested shared intersection in Shanghai, China, with frequent pedestrian conflicts is used as a case study. Traffic conflicts are analysed with the use of automated video‐based analysis techniques. The results showed that evasive action‐based indicators have higher potential to identify pedestrian conflicts and measure their severity in high mix less organized traffic environments than time proximity measures such as time‐to‐collision and post‐encroachment time. Copyright © 2016 John Wiley & Sons, Ltd.

Limitations associated with traditional collision-based safety analysis techniques led to a growing interest in the use of surrogate safety measures such as the traffic conflict technique. This interest was facilitated by advances in automated video-based data collection methods that helped to overcome the reliability issues associated with manual collection of data on traffic conflicts. Various objective conflict indicators that measure various spatial and temporal aspects of user proximity are available to measure the severity of traffic events. These time-proximity conflict measures assume that proximity is a surrogate for conflict severity. However, this assumption may not be valid in many driving environments. The objective of this paper is to investigate whether time-proximity conflict measures can be a good indicator of safety in less-organized traffic environments with highly mixed road users. A case study of motorcycle conflicts in a highly congested shared intersection in Shanghai, China, was used as a case study. Traffic conflicts were analyzed with the use of automated video-based analysis techniques. Several traffic conflict indicators designed to detect evasive actions, such as deceleration, jerk, and yaw rate, were recommended as better able to measure traffic conflicts in such traffic environments. The results showed that indicators that measured evasive actions had higher potential to identify motorcycle conflicts in highly mixed, less-organized traffic environments than did time-proximity measures such as the time to collision.

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Interest has grown in using traffic conflicts for studying safety from a broader perspective than relying only on collision data. Traffic conflict analysis is typically performed through the calculation of traditional conflict severity measures such as time-to-collision and postencroachment time. These measures rely on road users getting within specific temporal and spatial proximity from each other and therefore assume that proximity is the surrogate for severity. However, this assumption may not be valid in some driving environments where close interactions between road users are common and sudden evasive actions are frequently used to avoid collisions. It is suggested that evasive action–based conflict indicators can assess the analysis in some less-organized traffic environments. This study focused on the severity evaluation of pedestrian conflicts. Pedestrian evasive actions were reflected mainly in variations of spatiotemporal gait parameters (step frequency and step length). The objective was to compare the use of time proximity and evasive action–based conflict indicators in evaluating the severity of pedestrian conflicts in different traffic environments. Video data from intersections in five major cities—Shanghai, China; New Delhi, India; New York City; Doha, Qatar; and Vancouver, British Columbia, Canada—were analyzed with automated computer vision techniques to extract pedestrian-involved conflicts and calculate conflict indicators. Results show that evasive action–based indicators were more effective in identifying and measuring the severity of pedestrian conflicts than time proximity measures in traffic environments such as Shanghai and New Delhi. However, evasive action measures did not show the same potential in Vancouver and Doha, where time proximity measures were more effective.

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This article describes an automated approach for the analysis of right-turn merging behavior of vehicles. Traditional methods for collecting merging behavior data are labor intensive, suffer from reliability issues, are time consuming, and costly. Automated video merging behavior analysis is advocated as alternative data collection procedure resolving many shortcomings in the manual data collection. The main elements of the behavior analysis include merging conflicts, gap acceptance, and lane discipline. Traffic conflicts provide invaluable information that can be used to assess safety factors and to understand potential collision mechanisms. Gap acceptance is important for developing merging vehicles modeling frameworks. Lane discipline of merging vehicles is significant in showing potential aggressive and dangerous merging maneuvers and driver compliance to traffic rules. The article advocates automated computer vision as the engine to capture and analyze various merging behavior elements. The analysis is demonstrated using a case study from Doha, Qatar. A validation of the results was performed that demonstrated the soundness of the methodology and potential benefits for automated behavior data collection. The microscopic behavior data captured using the proposed automated methodology can be useful for use in road design, traffic management and safety evaluation.

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