Patterns of Urban Foot Traffic Dynamics

TL;DR

This study analyzes pedestrian foot traffic patterns in Manhattan using traffic camera data, revealing consistent diurnal behaviors, differences between weekdays and weekends, and potential for anomaly detection and neighborhood characterization.

Contribution

The paper introduces a detailed analysis of urban foot traffic dynamics, identifying characteristic patterns, anomalies, and spatial associations, which were not previously quantified at this scale.

Findings

Weekday foot traffic shows a 3-peak pattern aligned with work hours.

Weekend foot traffic increases steadily until sunset.

Anomalies can be used to detect events and disruptions.

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 9:00am, 12:00-1:00pm, and 5:00pm 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 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 Manhattan neighborhood boundaries indicating that foot traffic dynamics encode information about neighborhood character.