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.

翻译：本文提出了基于机器学习的解决方案，用于预测未来行程时间及车辆可能行驶的距离。针对该预测任务，我们开发并研究了四种方法。第一种方法采用基于长短期记忆网络的结构，专门设计用于同时处理行程时间与距离的多维历史数据，通过全连接层拼接LSTM网络的输出来预测未来行程时间与车辆行驶距离。第二种方法使用基于注意力的LSTM网络执行相同任务。第三种方法并行使用两个LSTM网络，分别预测行程时间与距离，各LSTM输出再通过全连接层拼接。第四种模型基于两个并行At-LSTM网络，类似地分别通过全连接层预测时间与距离。在提出的方法中，最先进的并行At-LSTM模型预测下一次行程距离与时间的误差率为3.99%，比第一种方法LSTM提升了23.89%。我们还提出了TimeSHAP作为可解释性方法，用于理解网络如何学习与建模信息序列。