Jonas Sjöblom

Ebrahim Balouji, Jonas Sjöblom, Nikolce Murgovski et al.

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.

Victor Eberstein, Jonas Sjöblom, Nikolce Murgovski et al.

Trip destination prediction is an area of increasing importance in many applications such as trip planning, autonomous driving and electric vehicles. Even though this problem could be naturally addressed in an online learning paradigm where data is arriving in a sequential fashion, the majority of research has rather considered the offline setting. In this paper, we present a unified framework for trip destination prediction in an online setting, which is suitable for both online training and online prediction. For this purpose, we develop two clustering algorithms and integrate them within two online prediction models for this problem. We investigate the different configurations of clustering algorithms and prediction models on a real-world dataset. We demonstrate that both the clustering and the entire framework yield consistent results compared to the offline setting. Finally, we propose a novel regret metric for evaluating the entire online framework in comparison to its offline counterpart. This metric makes it possible to relate the source of erroneous predictions to either the clustering or the prediction model. Using this metric, we show that the proposed methods converge to a probability distribution resembling the true underlying distribution with a lower regret than all of the baselines.