Automated Pedestrian Safety Analysis at a Signalized Intersection in New York City: Automated Data Extraction for Safety Diagnosis and Behavioral Study

Abstract

Automated computer vision techniques were used to analyze 2 h of video data collected at a major signalized intersection in New York City. The main objectives of this study were to diagnose pedestrian safety issues and identify contributing factors at the intersection and to demonstrate the feasibility of the automatic extraction of pedestrian data required for pedestrian behavior analysis—mainly pedestrian speed and gait parameters. The safety study was conducted with traffic conflict techniques. The main factor that contributed to the high number of pedestrian and vehicle conflicts was found to be pedestrian violations, mainly temporal violations in which pedestrians crossed the street during the “Don't Walk” or flashing “Don't Walk” phase. During the 2 h analyzed, about one-third of pedestrians were noncompliant with the signal timing or crosswalk boundary (17.9% spatial violations and 15.3% temporal violations). Pedestrian speed, step frequency, and step length were automatically extracted for 333 pedestrians and were found to follow the normal distribution with 95% confidence (mean and standard deviation of 1.47 ± 0.27 m/s, 1.96 ± 0.17 Hz, and 0.75 ± 0.14 m, respectively). Gait analysis showed that the walking speed for single pedestrians was 9% higher than for those who walked in groups. Males tended to be slightly faster than females, with higher step length but lower step frequency. Violators tended to have higher walking speeds compared with non-violators, and the difference in speed was dependent on step length but not on step frequency.

Active Road User Interactions With Autonomous Vehicles: Proactive Safety Assessment

“…This goes to show the critical role that proactive safety assessments play in addressing safety concerns of active road users for the future of AV technologies. The traffic conflict technique was established as a reliable and proactive safety assessment tool that has been successfully used to address numerous safety applications in the literature, for example, Hussein et al ( 30 , 31 ), Tageldin et al ( 32 ), Zaki et al ( 33 ), Kumar and Ghosh ( 34 ), and Nasernejad ( 35 ). In this technique, the frequency and severity of the observed conflicts between road users are used as a surrogate measure for actual collisions.…”

Analyzing the Safety Consequences of Pedestrian Spatial Violation at Mid-Blocks: A Bayesian Structural Equation Modeling Approach

“…Moreover, spatial violations were shown to be an important contributor to the frequency and severity of pedestrian collisions. For example, Hussein et al ( 23 ) analyzed the association between pedestrian violation and the frequency of pedestrian-vehicle conflicts at a signalized intersection in New York City. The study identified pedestrian violations as the main contributor to pedestrian-vehicle conflicts and showed that 18% of the pedestrians tend to cross the street in a non-designated space.…”

Vision-Based Potential Pedestrian Risk Analysis on Unsignalized Crosswalk Using Data Mining Techniques

“…This makes it possible to project the coordinates of the car from an oblique viewpoint to dimensionally-accurate space [16]. Likewise, Hussein et al tracked pedestrians from two hours of video data from a major signalized intersection in New York City [17]. The study used an automated video handling system set to calibrate the image into an overhead view, and then tracked the pedestrians using computer vision techniques.…”

“…intersection in New York City [17]. The study used an automated video handling system set to calibrate the image into an overhead view, and then tracked the pedestrians using computer vision techniques.…”