title: Using GPS, GIS, and Accelerometer Data to Predict Transportation Modes.
abstract: BACKGROUND
  Active transportation is a substantial source of physical activity, which has a positive influence on many health outcomes. A survey of transportation modes for each trip is challenging, time-consuming, and requires substantial financial investments. This study proposes a passive collection method and the prediction of modes at the trip level using random forests.
  
  
  METHODS
  The RECORD GPS study collected real-life trip data from 236 participants over 7 d, including the transportation mode, global positioning system, geographical information systems, and accelerometer data. A prediction model of transportation modes was constructed using the random forests method. Finally, we investigated the performance of models on the basis of a limited number of participants/trips to predict transportation modes for a large number of trips.
  
  
  RESULTS
  The full model had a correct prediction rate of 90%. A simpler model of global positioning system explanatory variables combined with geographical information systems variables performed nearly as well. Relatively good predictions could be made using a model based on the 991 trips of the first 30 participants.
  
  
  CONCLUSIONS
  This study uses real-life data from a large sample set to test a method for predicting transportation modes at the trip level, thereby providing a useful complement to time unit-level prediction methods. By enabling predictions on the basis of a limited number of observations, this method may decrease the workload for participants/researchers and provide relevant trip-level data to investigate relations between transportation and health.
authors.affiliation: 1Institut National de la Santé et de la Recherche Médicale, UMR_S 1136, Pierre Louis Institute of Epidemiology and Public Health, Research Team in Social Epidemiology, Paris, FRANCE; 2Sorbonne Universités, Université Pierre et Marie Curie Univ Paris 06, UMR_S 1136, Pierre Louis Institute of Epidemiology and Public Health, Research Team in Social Epidemiology, Paris, FRANCE; 3Ecole des Hautes études en Santé Publique School of Public Health, Rennes, FRANCE; and 4IPC Medical Centre, Paris, FRANCE.
authors.forename: Ruben
authors.initials: R
authors.lastname: Brondeel
authors.forename: Bruno
authors.initials: B
authors.lastname: Pannier
authors.forename: Basile
authors.initials: B
authors.lastname: Chaix
mesh_terms: D061725:Accelerometry; D000465:Algorithms; D001642:Bicycling; D040362:Geographic Information Systems; D006801:Humans; D018986:Motor Vehicles; D014186:Transportation; D016138:Walking