Link Representation Learning for Probabilistic Travel Time Estimation
<|reference_start|>Link Representation Learning for Probabilistic Travel Time Estimation: Travel time estimation is a crucial application in navigation apps and web mapping services. Current deterministic and probabilistic methods primarily focus on modeling individual trips, assuming independence among trips. However, in real-world scenarios, we often observe strong inter-trip correlations due to factors such as weather conditions, traffic management, and road works. In this paper, we propose to model trip-level link travel time using a Gaussian hierarchical model, which can characterize both inter-trip and intra-trip correlations. The joint distribution of travel time of multiple trips becomes a multivariate Gaussian parameterized by learnable link representations. To effectively use the sparse GPS trajectories, we also propose a data augmentation method based on trip sub-sampling, which allows for fine-grained gradient backpropagation in learning link representations. During inference, we estimate the probability distribution of the travel time of a queried trip conditional on the completed trips that are spatiotemporally adjacent. We refer to the overall framework as ProbTTE. We evaluate ProbTTE on two real-world GPS trajectory datasets, and the results demonstrate its superior performance compared to state-of-the-art deterministic and probabilistic baselines. Additionally, we find that the learned link representations align well with the physical geometry of the network, making them suitable as input for other applications.<|reference_end|>
@article{xu2024link,
    title={Link Representation Learning for Probabilistic Travel Time Estimation},
    author={Chen Xu, Qiang Wang, Lijun Sun},
    journal={arXiv preprint arXiv:2407.05895},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.05895},
    primaryClass={cs.LG stat.ML}
}