Developing and Optimizing a Transportation Mode Inference Model Utilizing Data from GPS Embedded Smartphones

Advances in wireless communications and technologies provide the opportunity to collect detailed information on travel trajectory using smart-phones equipped with GPS and accelerometers. These types of smart-phones are ubiquitous and, as such, present an opportunity to conveniently collect spatial and temporal data at regular time intervals. This can be useful to utilize as a method to document travel behavior – origin, destination, departure time, route choice, trip purpose, and mode choice. One of the challenges that has been addressed in the literature is how to identify the transportation mode of travel. This paper presents a data-driven classification model to infer transportation mode choice from data collected with GPS equipped smart phones. Rather than making a priori assumptions, the authors instead employ an optimization method to objectively produce the following classifier components and methods: a ranked feature vector based on the power of differentiation between different modes; the classification technique between the range of candidate classifiers; the number of ranked attributes to include in the feature vector; data formatting; and optimal model parameters. The model is trained and tested using known transportation mode segments – limits of travel by a given mode. The calibrated model is evaluated by testing its ability to classify travel mode correctly for GPS data at a different level of disaggregation than the one used in the model training step. The model provides an accuracy of approximately 86% at the disaggregated level (e.g. Walk, Bike, Transit, and Private Automobile) and approximately 94% at aggregated level (e.g. Non-Motorized and Motorized.)