City governments all over the world face challenges understanding mobility patterns within dense urban environments at high spatial and temporal resolution. While such measures are important to provide insights into the functional patterns of a city, 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 involves using WiFi probe request data to model urban mobility in a dense, mixed-use district in New York City. The researchers collect 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 aggregate 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 use a spatial network analysis to identify edge frequencies and directions of journeys between the network nodes and apply the results to the road and pedestrian sidewalk network to identify usage levels and trajectories at the street segment level.
Mean “first seen” time for individual devices on weekdays and weekends.
The second year of this project will build on the probe request data from lower Manhattan and combine it with various physical, social, and environmental data, collected from the PI’s deployed sensors and administrative records, to understand the impact of various factors on mobility patterns and behavior. The researchers will expand 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 involves developing an algorithm to distinguish between pedestrians, bicyclists and vehicles. The team will also explore the use of WiFi data to establish workday length and productivity for workers and attempt to generate real-time estimates of building occupancy.
The potential benefits of this work are significant for transportation planning, urban design, emergency response, and local 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.
This project aims to develop new models of pedestrian mobility using WiFi probe data as a novel data source. The models will be designed to scale to any region with a similar WiFi network infrastructure. Deliverables will also include a research paper submitted to a peer-reviewed journal and a final report.
Associate Professor, NYU
Constantine Kontokosta is the Principal Investigator on this project.
Awais Malik is a Researcher on this project.