01555435

Component

https://doi.org/10.3141/2519-03

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 nonviolators, and the difference in speed was dependent on step length but not on step frequency.

01596143

15-1849

English

pp 17–27

2015

Transportation Research Record: Journal of the Transportation Research Board

9780309369626

Print

Figures (8) ; Photos; References (36) ; Tables (2)

Computer vision; Pedestrian movement; Pedestrian safety; Pedestrian vehicle crashes; Signalized intersections; Traffic conflicts

New York (New York)

Highways; Operations and Traffic Management; Pedestrians and Bicyclists; Safety and Human Factors

TRIS, TRB, ATRI

Dec 30 2014 12:40PM

• Achieving Vision Zero: Data-Driven Investment Strategy to Eliminate Pedestrian Fatalities on a Citywide Level
• Association Between Built Environment and Pedestrian Fatal Crash Risk in Delhi, India
• Calibration of a Pedestrian Route Choice Model with a Basis in Friction Forces
• Can Good Walkability Expand the Size of Transit-Oriented Developments?
• Community-Based Pedestrian and Bicycle Safety Program: Developmental Framework and Process Evaluation
• Countdown Pedestrian Signals: Legibility and Comprehension Without Flashing Hand
• Determining the Most Suitable Pedestrian Level of Service Method for Dhaka City, Bangladesh, Through a Synthesis of Measurements
• Differences Between Walking and Bicycling over Time: Implications for Performance Management
• Do as I Say, Not as I Do: Observed Compliance Versus Stated Understanding of Pedestrian Crossing Laws
• Evaluating Pedestrian Level of Service at Signalized Intersections in China: Intercept Survey Method
• Factor Association with Multiple Correspondence Analysis in Vehicle–Pedestrian Crashes
• Leading Pedestrian Interval: Assessment and Implementation Guidelines
• Microscopic Pedestrian Interaction Behavior Analysis Using Gait Parameters
• New Model for Total Crossing Time of Pedestrian Platoon at a Signalized Crosswalk
• Pedestrian Crossing Behavior at Signalized Intersections in New York City
• Pedestrian Injury Severity Levels in the Halifax Regional Municipality, Nova Scotia, Canada: Hierarchical Ordered Probit Modeling Approach
• Pedestrian Safety Practitioners’ Perspectives of Driver Yielding Behavior Across North America
• Probit-Based Pedestrian Gap Acceptance Model for Midblock Crossing Locations
• Rectangular Rapid Flashing Beacons and Pedestrian Hybrid Beacons: Pedestrian and Driver Behavior Before and After Installation