{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/link-representation-learning-for","title":"Link Representation Learning for Probabilistic Travel Time Estimation","arxiv_id":"2407.05895","date":"2024-07-08","proceeding":null,"authors":["Chen Xu","Qiang Wang","Lijun Sun"],"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.","url_abs":"https://arxiv.org/abs/2407.05895v1","url_pdf":"https://arxiv.org/pdf/2407.05895v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"link-representation-learning-for","repo_url":"https://github.com/ChenXu02/ProbTTE","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"link-representation-learning-for","repo_url":"https://github.com/ChenXu02/SPTTE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"travel-time-estimation","task_name":"Travel Time Estimation"}],"methods":[{"method_slug":"align","method_name":"ALIGN"},{"method_slug":"focus","method_name":"Focus"},{"method_slug":"gps","method_name":"GPS"},{"method_slug":null,"method_name":"Travel"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2407.05895","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}