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

Preprint, 2023

distance the vehicle will travel. For this prediction task, we develop and investigate four methods.
In the first method, we use long short-term memory (LSTM)-based structures specifically designed
to handle multi-dimensional historical data of trip time and distances simultaneously. Using it, we
predict the future trip time and forecast the distance a vehicle will travel by concatenating the outputs
of LSTM networks through fully connected layers. The second method uses attention-based LSTM
networks (At-LSTM) to perform the same tasks. The third method utilizes two LSTM networks in
parallel, one for forecasting the time of the trip and the other for predicting the distance. The output
of each LSTM is then concatenated through fully connected layers. Finally, the last model is based
on two parallel At-LSTMs, where similarly, each At-LSTM predicts time and distance separately
through fully connected layers. Among the proposed methods, the most advanced one, i.e., parallel
At-LSTM, predicts the next trip’s distance and time with 3.99 % error margin where it is 23.89 %
better than LSTM, the first method. We also propose TimeSHAP as an explainability method for
understanding how the networks perform learning and model the sequence of information.