Understanding Urban Mobility in New York City using GPS data and Smartphone App
Topics:
Keywords: GPS, Web GIS, smartphone apps, travel survey, carbon footprint, travel modal shift
Abstract Type: Paper Abstract
Authors:
Hongmian Gong Hunter College of CUNY
Paul Rivers Building Energy Exchange
Abstract
Daily travel data at the individual level provide spatial, temporal, and behavioral information for mobility and time geography studies. They are also used by governments at various levels to design programs for reducing traffic congestion, energy consumption, and emission of vehicle pollutants.
We built a framework that uses GPS data, Web GIS, cloud computing, and smartphone app to study urban mobility in New York City. A smartphone app for iPhone or Android phone was provided to individuals so that their daily travel data were sent to our Amazon cloud server. A mode detection algorithm in the cloud server calculates and speculates on the travel modes of the individuals. The carbon footprint and calories/fat burned correspond to their travel modes were calculated and sent back to their smartphones to encourage public transit use by the individuals.
This framework was used in a small survey at Hunter College to measure the travel tendencies of individuals in weekdays and weekends as well as willingness to shift travel mode based on app experience over a week.
This abstract is submitted to:
Symposium on Human Dynamics Research: Mining Human Dynamics with Big Data & Spatio-Temporal Analysis, OR
Symposium on Human Dynamics Research: Advances in Dynamic Environmental Exposure, Mobility Patterns, and Health Outcomes
Understanding Urban Mobility in New York City using GPS data and Smartphone App
Category
Paper Abstract
Description
Submitted By:
Hongmian Gong
gong@hunter.cuny.edu
This abstract is part of a session: Symposium on Human Dynamics Research: Mining Human Dynamics with Big Data & Spatio-Temporal Analysis II