Effect of weather on pedestrian trip count and duration: City-scale evaluations using mobile phone application data

We examined the association between meteorological (weather) conditions in a given locale and pedestrian trips frequency and duration, through the use of locative digital data. These associations were determined for seasonality, urban microclimate, and commuting. We analyzed GPS data from a broadly...

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Bibliographic Details
Main Authors: Anthony P. Vanky, Santosh K. Verma, Theodore K. Courtney, Paolo Santi, Carlo Ratti
Format: Article
Language:English
Published: Elsevier 2017-12-01
Series:Preventive Medicine Reports
Online Access:http://www.sciencedirect.com/science/article/pii/S2211335517301158
Description
Summary:We examined the association between meteorological (weather) conditions in a given locale and pedestrian trips frequency and duration, through the use of locative digital data. These associations were determined for seasonality, urban microclimate, and commuting. We analyzed GPS data from a broadly available activity tracking mobile phone application that automatically recorded 247,814 trips from 5432 unique users in Boston and 257,697 trips from 8256 users in San Francisco over a 50-week period. Generally, we observed increased air temperature and the presence of light cloud cover had a positive association with hourly trip frequency in both cities, regardless of seasonality. Temperature and weather conditions generally showed greater associations with weekend and discretionary travel, than with weekday and required travel. Weather conditions had minimal association with the duration of the trip, once the trip was initiated. The observed associations in some cases differed between the two cities. Our study illustrates the opportunity that emerging technology presents to study active transportation, and exposes new methods to wider consideration in preventive medicine. Keywords: Weather, Pedestrian activity, Walking, Weather conditions and active transportation, Microclimates, Spatial behavior, Mobile phones, Locative data, Emerging technology, Big data
ISSN:2211-3355