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Data"],"published-print":{"date-parts":[[2025,2,28]]},"abstract":"<jats:p>The recent years have witnessed a surge in the development of traffic flow prediction methods, often deployed on cloud platforms to offer predictive services for entire transportation networks. However, the processes of training and executing a model for the entire traffic network are both time-consuming and computationally expensive. As a result, the utilization of edge servers for local sub-network prediction services has gained prominence. Nevertheless, training prediction models for numerous sub-networks within the extensive traffic network remains a time-intensive and computing resource-consuming task. To tackle this challenge, this article introduces the Pre-trained model REcommendation Framework for Edge computing enabled tRaffic flow prediction (PREFER). PREFER trains a set of traffic flow prediction models on selected sub-networks, then recommends optimal pre-trained models for edge servers. The recommendation is specifically based on performance prediction, integrating neural collaborative filtering and traffic flow characteristics. Experiments conducted on real datasets reveal that the pre-trained models recommended by PREFER perform close to the actual optimal ones and significantly outperform existing recommendation algorithms.<\/jats:p>","DOI":"10.1145\/3707464","type":"journal-article","created":{"date-parts":[[2024,12,9]],"date-time":"2024-12-09T14:20:48Z","timestamp":1733754048000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["PREFER: A Pre-trained Model Recommendation Framework for Edge Computing Enabled Traffic Flow Prediction"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-9860-2259","authenticated-orcid":false,"given":"Qiqi","family":"Cai","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0036-9436","authenticated-orcid":false,"given":"Jian","family":"Cao","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8325-3596","authenticated-orcid":false,"given":"Yirong","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Stanford University, Stanford, California, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7775-1740","authenticated-orcid":false,"given":"Shiyou","family":"Qian","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9428-6846","authenticated-orcid":false,"given":"Liangxiao","family":"Yuan","sequence":"additional","affiliation":[{"name":"Shanghai SEARI Intelligent System Co., Ltd, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1857-5569","authenticated-orcid":false,"given":"Jie","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Civil and Environment, Stanford University, Stanford, California, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,1,16]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"100739","article-title":"Urban traffic flow prediction techniques: A review","volume":"35","author":"Medina-Salgado Boris","year":"2022","unstructured":"Boris Medina-Salgado, Eddy Sanchez-DelaCruz, Pilar Pozos-Parra, and Javier E. 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