{"type":"ScholarlyArticle","title":"Enhancing Sustainable Urban Mobility Prediction with Telecom Data: A Spatio-Temporal Framework Approach","authors":[{"name":"ChungYi Lin","slug":"chungyi-lin","given":"ChungYi","family":"Lin","affiliation":"Internet of Things Laboratory , Chunghwa Telecom Laboratories; National Taiwan University"},{"name":"Shen-Lung Tung","slug":"shen-lung-tung","given":"Shen-Lung","family":"Tung","affiliation":"Internet of Things Laboratory , Chunghwa Telecom Laboratories"},{"name":"Hung-Ting Su","slug":"hung-ting-su","given":"Hung-Ting","family":"Su","affiliation":"National Taiwan University"},{"name":"Winston H. Hsu","slug":"winston-h-hsu","given":"Winston H.","family":"Hsu","affiliation":"National Taiwan University; Mobile Drive Technology"}],"abstract":"Traditional traffic prediction, limited by the scope of sensor data, falls short in comprehensive traffic management. Mobile networks offer a promising alternative using network activity counts, but these lack crucial directionality. Thus, we present the TeltoMob dataset, featuring undirected telecom counts and corresponding directional flows, to predict directional mobility flows on roadways. To address this, we propose a two-stage spatio-temporal graph neural network (STGNN) framework. The first stage uses a pre-trained STGNN to process telecom data, while the second stage integrates directional and geographic insights for accurate prediction. Our experiments demonstrate the framework's compatibility with various STGNN models and confirm its effectiveness. We also show how to incorporate the framework into real-world transportation systems, enhancing sustainable urban mobility.","doi":"10.24963/ijcai.2024/812","url":"https://ijcai.org/proceedings/2024/812","pdf":"https://ijcai.org/proceedings/2024/0812.pdf","markdown":"https://ijcai.org/proceedings/2024/812.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/812","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"AI for Good","track":"AI for Good","number":812,"pages":{"first":7340,"last":7348},"keywords":["Knowledge Representation and Reasoning: General","Data Mining: General"],"videos":[],"references":[{"position":1,"raw":"[Babu and Manoj, 2020] Sarath Babu and BS Manoj. 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