  We utilize Wi-Fi communications from smartphones to predict their mobility
mode, i.e. walking, biking and driving. Wi-Fi sensors were deployed at four
strategic locations in a closed loop on streets in downtown Toronto. Deep
neural network (Multilayer Perceptron) along with three decision tree based
classifiers (Decision Tree, Bagged Decision Tree and Random Forest) are
developed. Results show that the best prediction accuracy is achieved by
Multilayer Perceptron, with 86.52% correct predictions of mobility modes.
