Characteristic Analysis of Pedestrian Violation Crossing Behavior Based on Logistics Model

2014

This paper addresses the automated analysis of pedestrians’ conformance behavior and data collection in an intersection with a perceived high rate of traffic conflicts involving pedestrians. The intersection is located in the Downtown Eastside of Vancouver, British Columbia, Canada. Despite implementation of countermeasures such as reducing the speed limit, safety issues linger at the intersection. The intersection is characterized by a high pedestrian volume and considerable crossing violations by pedestrians, which elevate the safety risk and disrupt vehicle traffic flow. A challenge in performing pedestrian road safety analysis is the shortfall of reliable data. Recent advances in automated detection of pedestrians through the use of computer vision expanded the range of applications in traffic safety. In this study, an automated system for identifying pedestrian crossing nonconformance to traffic regulations by using pattern matching was developed and tested. The results show satisfactory accuracy in detecting both spatial and temporal violations, with the detection rate for violations being more than 84% correct. The automated collection of pedestrian data on crossing speed and counts is also demonstrated with high accuracy. The availability of these data is important for diagnosing the safety issues at the intersection and for justifying capital for implementing pedestrian safety countermeasures.

Section: Literature Review Crossing Behavior Of Pedestriansmentioning

confidence: 99%

Automated Analysis of Pedestrians’ Nonconforming Behavior and Data Collection at an Urban Crossing

2014

“…For example, the results by Karkee et al suggested a relation between pedestrian crossing compliance and traffic conflicts (40). Significant factors affecting pedestrian behavior at intersections were studied by Yanfeng et al (41). Several parameters, such as waiting time, crossing time, and arrival rate, were suggested as key variables for describing pedestrian characteristics and improving crossing design and signal timing plans (41).…”

Section: Violation Detectionmentioning

confidence: 99%

“…Significant factors affecting pedestrian behavior at intersections were studied by Yanfeng et al (41). Several parameters, such as waiting time, crossing time, and arrival rate, were suggested as key variables for describing pedestrian characteristics and improving crossing design and signal timing plans (41). Hamed developed models to study the relationship between pedestrian waiting time at the curbside and the number of crossing attempts (42).…”

Section: Violation Detectionmentioning

confidence: 99%

Application of Computer Vision to Diagnosis of Pedestrian Safety Issues

Tageldin

et al. 2013

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.

“…Such nonconforming behavior leads to an increase in collision exposure. The main factors that affect pedestrian behavior at intersections were studied in Yanfeng et al (10). Several parameters, such as wait time, crossing time, and arrival rate, were suggested to be key variables in the identification of pedestrian characteristics and the improvement of crossing design and signal timing plans (10).…”

Section: Use Of Computer Vision To Identify Pedestrians' Nonconforming Behavior At Urban Intersectionsmentioning

confidence: 99%

“…The main factors that affect pedestrian behavior at intersections were studied in Yanfeng et al (10). Several parameters, such as wait time, crossing time, and arrival rate, were suggested to be key variables in the identification of pedestrian characteristics and the improvement of crossing design and signal timing plans (10). Hamed has developed models to study the relationship between pedestrian wait time at the curbside and the number of crossing attempts (11).…”

Section: Use Of Computer Vision To Identify Pedestrians' Nonconforming Behavior At Urban Intersectionsmentioning

confidence: 99%

Use of Computer Vision to Identify Pedestrians' Nonconforming Behavior at Urban Intersections

Ismail

et al. 2012

The detection and understanding of nonconforming behavior (violations) can be useful in forming safety diagnoses and developing safety countermeasures. Traffic violations occur when road users, including pedestrians, seek increased mobility and disregard traffic laws and regulations. Such behavior can cause additional collision risks. This paper's objective is to demonstrate the automated identification of pedestrian crossing violations with computer vision techniques. Two types of violations are considered. The first is spatial violations: pedestrians cross an intersection in nondesignated crossing regions. The second is temporal violations: pedestrians cross an intersection during an improper signal phase. The methodology primarily relies on the identification of road users' trajectories and separating pedestrians with nonconforming behavior from those with conforming behavior. The methodology is demonstrated on two urban intersections, one in downtown Vancouver, Canada, the other in Kuwait City, Kuwait. The results show satisfactory accuracy in the detection of spatial and temporal violations, with an approximately 90% correct violation detection rate having been achieved in both case studies.