Multi-task Recurrent Neural Networks to Simultaneously Infer Mode and Purpose in GPS Trajectories

Multi-task learning is assumed as a powerful inference method, specifically, where there is a considerable correlation between multiple tasks, predicting them in an unique framework may enhance prediction results. This research challenges this assumption by developing several single-task models to compare their results against multi-task learners to infer mode and purpose of trip from smartphone travel survey data collected as part of a smartphone-based travel survey. GPS trajectory data along with socio-demographics and destination-related characteristics are fed into a multi-input neural network framework to predict two outputs; mode and purpose. We deployed Recurrent Neural Networks (RNN) that are fed by sequential GPS trajectories. To process the socio-demographics and destination-related characteristics, another neural network, with different embedding and dense layers is used in parallel with RNN layers in a multi-input multi-output framework. The results are compared against the single-task learners that classify mode and purpose independently. We also investigate different RNN approaches such as Long-Short Term Memory (LSTM), Gated Recurrent Units (GRU) and Bi-directional Gated Recurrent Units (Bi-GRU). The best multi-task learner was a Bi-GRU model able to classify mode and purpose with an F1-measures of 84.33% and 78.28%, while the best single-task learner to infer mode of transport was a GRU model that achieved an F1-measure of 86.50%, and the best single-task Bi-GRU purpose detection model that reached an F1-measure of 77.38%. While there's an assumption of higher performance of multi-task over sing-task learners, the results of this study does not hold such an assumption and shows, in the context of mode and trip purpose inference from GPS trajectory data, a multi-task learning approach does not bring any considerable advantage over single-task learners.