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Real-time trip purpose prediction using online location-based search and discovery services
Affiliation:1. Humphrey School of Public Affairs, University of Minnesota, 301 19th Ave S #307, Minneapolis, MN 55455, United States;2. Division of Biostatistics, University of Minnesota, 420 Delaware St. SE, Minneapolis, MN 55455, United States;3. Department of Information and Decision Sciences, University of Minnesota, 321 19th Avenue South, Minneapolis, MN 55455, United States;1. Delaware Center for Transportation (DCT), Department of Civil and Environmental Engineering, University of Delaware, Newark, DE 19716, United States;2. Institute for Transportation Research and Education (ITRE), Department of Civil, Construction and Environmental Engineering, North Carolina State University, Centennial Campus, Box 8601, Raleigh, NC 27695-8601, United States;3. School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85281, United States;1. Department of Civil and Environmental Engineering, Mississippi State University, 501 Hardy Road, Mississippi State, MS 39762, USA;2. School of Civil Engineering, University of Sydney, Australia;1. The Center for Modern Chinese City Studies, East China Normal University, Shanghai 200062, China;2. Department of Civil, Environmental, and Geo-Engineering, University of Minnesota, 500 Pillsbury Drive SE, Minneapolis, MN 55455, USA;1. Humphrey School of Public Affairs, University of Minnesota, 301 19th Avenue, Minneapolis, MN 55455, USA;2. Department of Civil Engineering, Sharif University of Technology, Azadi Avenue, Tehran, Iran
Abstract:The use of smartphone technology is increasingly considered a state-of-the-art practice in travel data collection. Researchers have investigated various methods to automatically predict trip characteristics based upon locational and other smartphone sensing data. Of the trip characteristics being studied, trip purpose prediction has received relatively less attention. This research develops trip purpose prediction models based upon online location-based search and discovery services (specifically, Google Places API) and a limited set of trip data that are usually available upon the completion of the trip. The models have the potential to be integrated with smartphone technology to produce real-time trip purpose prediction. We use a recent, large-scale travel behavior survey that is augmented by downloaded Google Places information on each trip destination to develop and validate the models. Two statistical and machine learning prediction approaches are used, including nested logit and random forest methods. Both sets of models show that Google Places information is a useful predictor of trip purpose in situations where activity- and person-related information is uncollectable, missing, or unreliable. Even when activity- and person-related information is available, incorporating Google Places information provides incremental improvements in trip purpose prediction.
Keywords:Trip purpose prediction  Online location-based search  Google Places  Nested logit model  Random forest model  Smartphone
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