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Multi-task learning is assumed as a powerful inference method, specifically,where there is a considerable correlation between multiple tasks, predictingthem in an unique framework may enhance prediction results. This researchchallenges this assumption by developing several single-task models to comparetheir results against multi-task learners to infer mode and purpose of tripfrom smartphone travel survey data collected as part of a smartphone-basedtravel survey. GPS trajectory data along with socio-demographics anddestination-related characteristics are fed into a multi-input neural networkframework to predict two outputs; mode and purpose. We deployed RecurrentNeural Networks (RNN) that are fed by sequential GPS trajectories. To processthe socio-demographics and destination-related characteristics, another neuralnetwork, with different embedding and dense layers is used in parallel with RNNlayers in a multi-input multi-output framework. The results are comparedagainst 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 modeand purpose with an F1-measures of 84.33% and 78.28%, while the bestsingle-task learner to infer mode of transport was a GRU model that achieved anF1-measure of 86.50%, and the best single-task Bi-GRU purpose detection modelthat reached an F1-measure of 77.38%. While there's an assumption of higherperformance of multi-task over sing-task learners, the results of this studydoes not hold such an assumption and shows, in the context of mode and trippurpose inference from GPS trajectory data, a multi-task learning approach doesnot bring any considerable advantage over single-task learners.

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