DeepTrip: A Deep Learning Model for the Individual Next Trip Prediction With Arbitrary Prediction Times

• Published in: IEEE transactions on intelligent transportation systems Vol. 24; no. 6; pp. 5842 - 5855
• Main Authors: Zhang, Pengfei, Koutsopoulos, Haris N., Ma, Zhenliang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Data models; Deep learning; Global Positioning System; Impact analysis; individual mobility; metro systems; Mobility management; Next trip prediction; Numerical models; Prediction algorithms; Predictive models; Random sampling; Trajectory; Transportation systems; Travel
• ISSN: 1524-9050; 1558-0016; 1558-0016
• DOI: 10.1109/TITS.2023.3252043

The increasing availability of travel trajectory data allows for a better understanding of travel behavior. In the individual mobility analysis, the problem of next trip prediction assumes a central role and is beneficial for applications such as personalized services and mobility management. This paper addresses the next trip prediction problem with arbitrary prediction times (the time when the prediction is made). This problem has not been studied adequately in the literature and it is important for applications driven by system events, such as proactive travel recommendations under disruptions or crowding in transport systems. It predicts an individual's next trips given their historical trip sequences and the prediction time. We formulate the next trip prediction problem as on-board and off-board predictions depending on an individual's travel status (i.e. on-board/off-board). Using historical/real-time travel trajectories, a DeepTrip model is proposed based on a trip sequence-to-sequence deep learning structure coupled with an attention mechanism. A novel overlapped embedding method is proposed to represent continuous travel attributes capturing simultaneously the categorical and numerical feature information. We also develop a random-sampling training algorithm to learn the impact of the prediction time. The model is validated using trip data in urban rails. The results show that DeepTrip outperforms statistical-based models by more than 10% in terms of accuracy and other deep learning models by 2%-3%. The impact analysis shows that different representations are appropriate for the two prediction cases (on-board/off-board), and the prediction performance does not monotonically improve as the prediction time approaches the next trip.