Using High Resolution Mobility Data to Understand Human Behavior and Population Dynamics in Cities

City governments all over the world face challenges understanding mobility patterns within dense urban environments at high spatial and temporal resolution. Novel quantitative methods, derived from ubiquitous mobile connectivity, are needed to provide decision-makers better insights to improve urban management and planning.

Year 1 of this two-year project involved using WiFi probe request data to model urban mobility in a dense, mixed-use district in New York City. The researchers collected probe request data from over 54 access points of a public WiFi network in Lower Manhattan for over three months, accounting for more than 500,000,000 observations and over 800,000 unique devices per week. First, the researchers aggregated unique entries per access point and per hour, demonstrating the potential to use WiFi data to approximate local population counts by type of user. They then used a spatial network analysis to identify edge frequencies and directions of journeys between the network nodes and applied the results to the road and pedestrian sidewalk network to identify usage levels and trajectories at the street segment level.

The second year of this project built on the probe request data and combined it with various physical, social, and environmental data, collected from sensors and administrative records, to understand the impact of various factors on mobility patterns and behavior. The researchers expanded the project’s mobility model to understand trajectories and pedestrian flows under various conditions, including weather, air quality, and construction and development activity. Part of this research involved developing an algorithm to distinguish between pedestrians, bicyclists, and vehicles. The team also explored the use of WiFi data to establish workday length and productivity for workers and attempted to generate real-time estimates of building occupancy.

The potential benefits of this work are significant for transportation planning, urban design, emergency response, and economic development. WiFi probe data are a novel data source that can be used to create a more spatially and temporally granular picture of local populations, to forecast localized populations given some exogenous environmental or physical conditions, and to analyze actual trajectories and paths of travel. Effectively modeling population dynamics at high spatial and temporal resolutions can have significant implications for city operations and policy, strategic long-term planning processes, emergency response and management, and public health.