01366206

Component

https://doi.org/10.3141/2299-13

http://scholar.google.com...nier&publication_year=2012

New urban planning concepts are being redefined to emphasize walkability (a measure of how walker-friendly an area is) and to accommodate the pedestrian as a key road user. However, the availability of reliable information on pedestrian traffic remains a major challenge and inhibits a better understanding of many pedestrian issues. Therefore, the importance of developing new techniques for the collection of pedestrian data cannot be overstated. This paper describes the use of computer vision techniques for the automated collection of pedestrian data through several applications, including measurement of pedestrian counts, tracking, and walking speeds. An efficient pedestrian-tracking algorithm, the MMTrack, was used. The algorithm employed a large-margin learning criterion to combine different sources of information effectively. The applications were demonstrated with a real-world data set from Vancouver, British Columbia, Canada. The data set included 1,135 pedestrian tracks. Manual counts and tracking were performed to validate the results of the automated data collection. The results show a 5% average error in counting, which is considered reliable. The results of walking speed validation showed excellent agreement between manual and automated walking speed values (root mean square error = 0.0416 m/s, R² = .9269). Further analysis was conducted on the mean walking speed of pedestrians as it related to several factors. Gender, age, and the group size were found to influence the pedestrian mean walking speed significantly. The results demonstrate that computer vision techniques have the potential to collect microscopic data on road users at a degree of automation and accuracy that cannot be feasibly achieved by manual or semiautomated techniques.

01457870

12-1675

English

pp 121–127

2012

Transportation Research Record: Journal of the Transportation Research Board

9780309223386

Print

Figures; References; Tables

Algorithms; Automatic data collection systems; Computer vision; Pedestrian counts; Pedestrian traffic; Pedestrians

Walking speed

Vancouver (Canada)

Data and Information Technology; Pedestrians and Bicyclists; I72: Traffic and Transport Planning

TRIS, TRB, ATRI

Feb 8 2012 5:04PM

• Analysis of Pedestrian Exposure to Risk in Relation to Crossing Behavior
• Analysis of Pedestrian Travel with Static Bluetooth Sensors
• Bicycle and Pedestrian Studies Based on Data from National Household Travel Survey
• Dedicated Short-Range Communication System for Pedestrians and Vehicles During Right Turns
• Developing Standard Pedestrian-Equivalent Factors: Passenger Car–Equivalent Approach for Dealing with Pedestrian Diversity
• Development and Application of a Pedestrian Volume Model in San Francisco, California
• Documenting Targeted Behaviors Associated with Pedestrian Safety
• Evaluating Effectiveness of Infrastructure-Based Countermeasures for Pedestrian Safety
• How Common Is Pedestrian Travel to, from, and Within Shopping Districts?
• Measuring Nonmotorized Accessibility and Connectivity in a Robust Pedestrian Network
• Nature of Modeling Boundary Pedestrian Crashes at Zones
• Pedestrian Safety at Intersections Under Control of Permissive Left-Turn Signal
• Pedestrian Safety at Slip Lanes and Alternative Turn Lane Treatments
• Perception of Pedestrian Traffic Signals by Pedestrians with Varying Levels of Vision
• Safer Midblock Environments for Pedestrian and Bicycle Crossings: Experiment with Zigzag Pavement Markings
• Simple, Inexpensive Approach to Sampling for Pedestrian and Bicycle Surveys: Approach Developed in Pedestrian and Bicycling Survey
• To Go or Not To Go: Pedestrian Behavior at Intersections with Standard Pedestrian Call Buttons
• Using Free and Proprietary Data to Compare Shortest-Path Lengths for Effective Pedestrian Routing in Street Networks