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This paper introduces TraveL, a Transformer-based framework for Multi-view Distributional Representation Learning that enhances path representation learning (PRL) by incorporating varied traveler behaviors and regional correlations among road segments. By leveraging a novel regional attention mechanism and the Kolmogorov-Smirnov test for training, the model effectively encodes paths along with travel starting times into distributional representations. Experimental results demonstrate that TraveL significantly outperforms state-of-the-art methods, achieving improvements of 14.7% in Mean K-S distance for travel time estimation and 16.7% in Mean Absolute Error for path similarity prediction.
TraveL captures the nuances of traveler behavior and regional correlations, leading to a 14.7% improvement in travel time distribution estimation over existing methods.
Path representation learning (PRL) for road networks has received increasing research attention, due to various path-related applications. Existing works on PRL typically exploit the co-occurrence relationship among road segments and paths to learn a vector as the path representation, without exploring the varied traveler behaviors and the regional correlation on the path. In this work, we propose to learn distributional representations, which provide valuable information for use in path-related applications, by capturing the varied traveler behaviors as well as the various dependencies within regions of road segments. We propose a novel Transformer-based Multi-view Distributional Representation Learning (TraveL) framework to encode a path along with a travel starting time to a distributional representation, which can be used to decode possible samples of on-path traveler behavior. Moreover, by analyzing the regional correlation which reveals various road segment relationships, we propose a regional attention to encode these correlations in a path. Also, we explore the idea of Kolmogorov-Smirnov (K-S) test to compare the sampled traveler behavior against the collected ground truth to facilitate training. Experimental results show that the proposed TraveL model outperforms the state-of-the-art methods on both synthetic and real-world datasets, by 14.7% in Mean K-S distance for travel time distribution estimation, 16.7% in Mean Absolute Error (MAE) for path similarity prediction, and 3.97% in MAE for destination prediction.
