Link Representation Learning for Probabilistic Travel Time Estimation

• URL: http://arxiv.org/abs/2407.05895v1
• Date: Mon, 8 Jul 2024 13:01:53 GMT
• Title: Link Representation Learning for Probabilistic Travel Time Estimation
• Authors: Chen Xu, Qiang Wang, Lijun Sun,
• Abstract summary: In this paper, we propose to model trip-level link travel time using a Gaussian hierarchical model. The results demonstrate its superior performance compared to state-of-the-art deterministic and probabilistic baselines. We find that the learned link representations align well with the physical geometry of the network, making them suitable as input for other applications.
• Score: 21.092166159353702
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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