{"type":"ScholarlyArticle","title":"LC-RNN: A Deep Learning Model for Traffic Speed Prediction","authors":[{"name":"Zhongjian Lv","slug":"zhongjian-lv","given":"Zhongjian","family":"Lv","affiliation":"School of Computer Science and Technology , Soochow University , China"},{"name":"Jiajie Xu","slug":"jiajie-xu","given":"Jiajie","family":"Xu","affiliation":"School of Computer Science and Technology , Soochow University , China; Provincial Key Laboratory for Computer Information Processing Technology , Soochow University; State Key Laboratory of Software Architecture ( Neusoft Corporation) , China"},{"name":"Kai Zheng","slug":"kai-zheng","given":"Kai","family":"Zheng","affiliation":"University of Electronic Science and Technology , China"},{"name":"Hongzhi Yin","slug":"hongzhi-yin","given":"Hongzhi","family":"Yin","affiliation":"The University of Queensland , Australia"},{"name":"Pengpeng Zhao","slug":"pengpeng-zhao","given":"Pengpeng","family":"Zhao","affiliation":"School of Computer Science and Technology , Soochow University , China"},{"name":"Xiaofang Zhou","slug":"xiaofang-zhou","given":"Xiaofang","family":"Zhou","affiliation":"The University of Queensland , Australia; School of Computer Science and Technology , Soochow University , China"}],"abstract":"Traffic speed prediction is known as an important but challenging problem. In this paper, we propose a novel model, called LC-RNN, to achieve more accurate traffic speed prediction than existing solutions. It takes advantage of both RNN and CNN models by a rational integration of them, so as to learn more meaningful time-series patterns that can adapt to the traffic dynamics of surrounding areas. Furthermore, since traffic evolution is restricted by the underlying road network, a network embedded convolution structure is proposed to capture topology aware features. The fusion with other information, including periodicity and context factors, is also considered to further improve accuracy. Extensive experiments on two real datasets demonstrate that our proposed LC-RNN outperforms six well-known existing methods.","doi":"10.24963/ijcai.2018/482","url":"https://ijcai.org/proceedings/2018/482","pdf":"https://ijcai.org/proceedings/2018/0482.pdf","markdown":"https://ijcai.org/proceedings/2018/482.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/482","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning Applications","track":"Main track","number":482,"pages":{"first":3470,"last":3476},"keywords":["Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Bengio et al., 2002] Y. 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