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In this paper, we propose machine learning solutions to predict the time offuture trips and the possible distance the vehicle will travel. For thisprediction task, we develop and investigate four methods. In the first method,we use long short-term memory (LSTM)-based structures specifically designed tohandle multi-dimensional historical data of trip time and distancessimultaneously. Using it, we predict the future trip time and forecast thedistance a vehicle will travel by concatenating the outputs of LSTM networksthrough fully connected layers. The second method uses attention-based LSTMnetworks (At-LSTM) to perform the same tasks. The third method utilizes twoLSTM networks in parallel, one for forecasting the time of the trip and theother for predicting the distance. The output of each LSTM is then concatenatedthrough fully connected layers. Finally, the last model is based on twoparallel At-LSTMs, where similarly, each At-LSTM predicts time and distanceseparately through fully connected layers. Among the proposed methods, the mostadvanced one, i.e., parallel At-LSTM, predicts the next trip's distance andtime with 3.99% error margin where it is 23.89% better than LSTM, the firstmethod. We also propose TimeSHAP as an explainability method for understandinghow the networks perform learning and model the sequence of information.

Prediction of Time and Distance of Trips Using Explainable Attention-based LSTMs