{"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/structural-recurrent-neural-network-for","title":"Structural Recurrent Neural Network for Traffic Speed Prediction","arxiv_id":"1902.06506","date":"2019-02-18","proceeding":null,"authors":["Youngjoo Kim","Peng Wang","Lyudmila Mihaylova"],"abstract":"Deep neural networks have recently demonstrated the traffic prediction\ncapability with the time series data obtained by sensors mounted on road\nsegments. However, capturing spatio-temporal features of the traffic data often\nrequires a significant number of parameters to train, increasing computational\nburden. In this work we demonstrate that embedding topological information of\nthe road network improves the process of learning traffic features. We use a\ngraph of a vehicular road network with recurrent neural networks (RNNs) to\ninfer the interaction between adjacent road segments as well as the temporal\ndynamics. The topology of the road network is converted into a spatio-temporal\ngraph to form a structural RNN (SRNN). The proposed approach is validated over\ntraffic speed data from the road network of the city of Santander in Spain. The\nexperiment shows that the graph-based method outperforms the state-of-the-art\nmethods based on spatio-temporal images, requiring much fewer parameters to\ntrain.","url_abs":"http://arxiv.org/abs/1902.06506v1","url_pdf":"http://arxiv.org/pdf/1902.06506v1.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":"structural-recurrent-neural-network-for","repo_url":"https://github.com/rhymesg/SRNN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"traffic-prediction","task_name":"Traffic Prediction"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}