Recent advances in communication technologies have enabled researchers to collect travel data based on ubiquitous and location-aware smartphones. These advances hold out the promise of allowing the automatic detection of the critical aspects (mode of transport, purpose, etc.) of people’s trips. Until now, efforts have concentrated on one aspect of trips (e.g. mode) at a time. Such methods have typically been developed on small data sets, often with data collected by researchers themselves and not in large-scale real-world data collection initiatives. This research develops a machine learning-based framework to identify complete trip information based on smartphone location data as well as online data from GTFS (General Transit Feed Specification) and Foursquare data. The framework has the potential to be integrated with smartphone travel surveys to produce all trip characteristics traditionally collected through household travel surveys. We use data from a recent, large-scale smartphone travel survey in Montréal, Canada. The collected smartphone data, augmented with GTFS and Foursquare data are used to train and validate three random forest models to predict mode of transport, transit itinerary as well as trip purpose (activity). According to cross-validation analysis, the random forest models show prediction accuracies of 87%, 81% and 71% for mode, transit itinerary and activity, respectively. The results compare favorably with previous studies, especially when taking the large, real-world nature of the dataset into account. Furthermore, the cross validation results show that the machine learning-based framework is an effective and automated tool to support trip information extraction for large-scale smartphone travel surveys, which have the potential to be a reliable and efficient (in terms of cost and human resources) data collection technique.
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