A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Patterns of urban foot traffic dynamics
Abstract Using publicly available traffic camera data in New York City, we quantify time-dependent patterns in aggregate pedestrian foot traffic. These patterns exhibit repeatable diurnal behaviors that differ for weekdays and weekends but are broadly consistent across neighborhoods in the borough of Manhattan. Weekday patterns contain a characteristic 3-peak structure with increased foot traffic around 9:00 am, 12:00–1:00 pm, and 5:00 pm aligned with the “9-to-5” work day in which pedestrians are on the street during their morning commute, during lunch hour, and then during their evening commute. Weekend days do not show a peaked structure, but rather increase steadily until sunset. Our study period of June 28, 2017 to September 11, 2017 contains two American holidays, the 4th of July and Labor Day, and their foot traffic patterns are quantitatively similar to weekend days despite the fact that they fell on weekdays. Projecting all days in our study period onto the weekday/weekend phase space (by regressing against the average weekday and weekend day) we find that Friday foot traffic can be represented as a mixture of both the 3-peak weekday structure and non-peaked weekend structure. We also show that anomalies in the foot traffic patterns can be used for detection of events and network-level disruptions. Finally, we show that clustering of foot traffic time series generates associations between cameras that are spatially aligned with distributions of commercial, residential, and unbuilt space proximal to the camera locations, indicating that foot traffic dynamics encode information about the surrounding built environment.
Highlights Computer vision algorithms applied to traffic cameras yield aggregate foot traffic. Patterns of NYC foot traffic dynamics differ for weekdays, weekends, and holidays. Weekday aggregate foot traffic shows three peaks that align with workforce behavior. Fridays are well-represented by a linear mixture of 3-peak and weekend behavior. Locations clustered by foot traffic patterns geospatially align with land use characteristics.
Patterns of urban foot traffic dynamics
Abstract Using publicly available traffic camera data in New York City, we quantify time-dependent patterns in aggregate pedestrian foot traffic. These patterns exhibit repeatable diurnal behaviors that differ for weekdays and weekends but are broadly consistent across neighborhoods in the borough of Manhattan. Weekday patterns contain a characteristic 3-peak structure with increased foot traffic around 9:00 am, 12:00–1:00 pm, and 5:00 pm aligned with the “9-to-5” work day in which pedestrians are on the street during their morning commute, during lunch hour, and then during their evening commute. Weekend days do not show a peaked structure, but rather increase steadily until sunset. Our study period of June 28, 2017 to September 11, 2017 contains two American holidays, the 4th of July and Labor Day, and their foot traffic patterns are quantitatively similar to weekend days despite the fact that they fell on weekdays. Projecting all days in our study period onto the weekday/weekend phase space (by regressing against the average weekday and weekend day) we find that Friday foot traffic can be represented as a mixture of both the 3-peak weekday structure and non-peaked weekend structure. We also show that anomalies in the foot traffic patterns can be used for detection of events and network-level disruptions. Finally, we show that clustering of foot traffic time series generates associations between cameras that are spatially aligned with distributions of commercial, residential, and unbuilt space proximal to the camera locations, indicating that foot traffic dynamics encode information about the surrounding built environment.
Highlights Computer vision algorithms applied to traffic cameras yield aggregate foot traffic. Patterns of NYC foot traffic dynamics differ for weekdays, weekends, and holidays. Weekday aggregate foot traffic shows three peaks that align with workforce behavior. Fridays are well-represented by a linear mixture of 3-peak and weekend behavior. Locations clustered by foot traffic patterns geospatially align with land use characteristics.
Patterns of urban foot traffic dynamics
Dobler, Gregory (author) / Vani, Jordan (author) / Dam, Trang Tran Linh (author)
2021-06-05
Article (Journal)
Electronic Resource
English
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