A Comparative Study of Pedestrian Crossing Behavior and Safety in Baltimore and Washington, D.C., Using Video Surveillance
There is a national effort to reduce pedestrian deaths. To achieve this goal, a better understanding of pedestrian travel behavior and interactions with vehicles is needed. Using video surveillance of locations in Baltimore, Maryland, and Washington, D.C., this study compares pedestrian-related travel behavior in the two neighboring cities. A computer vision pipeline approach will be used to identify pedestrians and vehicles from video surveillance footage in order to extract key metrics such as walking speed, gap acceptance, and type of unsafe maneuvers, to characterize pedestrian crossing behavior and associated traffic patterns. Statistical analyses of these metrics will determine which factors - such as land use, infrastructure, and sociodemographic characteristics - contribute to pedestrian travel behavior decisions and safety.
Universities and Sponsoring Organizations Involved
Morgan State University
University of Maryland
U.S. DOT Office of the Secretary/Research
Dr. Celeste Chavis, MSU, firstname.lastname@example.org
Kofi Nyarko (MSU), email@example.com
Cinzia Cirillo (UMD), firstname.lastname@example.org
Funding Sources and Amounts
USDOT: 120,000, Morgan: $40,000 (Match), UMD: $20,000 (Match)
Expected Completion Date
Expected Research Outcomes
The following outcomes will be generated from this work:
• A video-based tracking of pedestrians and vehicles using a computer vision pipeline approach
• A detailed database of pedestrian movement along a variety of facilities in two major cities: Baltimore, Maryland, and Washington, D.C.
• A pedestrian behavior and safety analysis at each location with provided recommendations
Expected Equity Impacts and Benefits of Implementation
Pedestrians are an important component of urban mobility. Though there is an understanding of the variability of pedestrians by age, gender, and mobility, there is a lack of understanding of the geographic and temporal variations in pedestrian-related travel behavior. The results of this study can inform policy and infrastructure decisions to improve the safety of pedestrians, roadways' most vulnerable user group. The pedestrian and vehicle tracking model has many future real-time applications. Furthermore, the results of this research can improve microsimulation calibration and inform dynamic traffic signalization.
Safety, travel behavior, pedestrians, computer visioning