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

• URL: http://arxiv.org/abs/2303.15087v1
• Date: Mon, 27 Mar 2023 10:54:32 GMT
• Title: Prediction of Time and Distance of Trips Using Explainable Attention-based LSTMs
• Authors: Ebrahim Balouji, Jonas Sj\"oblom, Nikolce Murgovski, Morteza Haghir Chehreghani
• Abstract summary: We propose machine learning solutions to predict the time of future trips and the possible distance the vehicle will travel. We use long short-term memory (LSTM)-based structures specifically designed to handle multi-dimensional historical data of trip time and distances simultaneously. Among the proposed methods, the most advanced one, parallel At-LSTM, predicts the next trip's distance and time with 3.99% error margin.
• Score: 6.07913759162059
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose machine learning solutions to predict the time of future trips and the possible 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.

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