{"type":"ScholarlyArticle","title":"DeepTravel: a Neural Network Based Travel Time Estimation Model with Auxiliary Supervision","authors":[{"name":"Hanyuan Zhang","slug":"hanyuan-zhang","given":"Hanyuan","family":"Zhang","affiliation":"† School of Computer Science , Shanghai Key Laboratory of Data Science , Fudan University , China; † Shanghai Insitute of Intelligent Electroics & Systems , Shanghai , China"},{"name":"Hao Wu","slug":"hao-wu","given":"Hao","family":"Wu","affiliation":"† School of Computer Science , Shanghai Key Laboratory of Data Science , Fudan University , China; † Shanghai Insitute of Intelligent Electroics & Systems , Shanghai , China"},{"name":"Weiwei Sun","slug":"weiwei-sun","given":"Weiwei","family":"Sun","affiliation":"† School of Computer Science , Shanghai Key Laboratory of Data Science , Fudan University , China; † Shanghai Insitute of Intelligent Electroics & Systems , Shanghai , China"},{"name":"Baihua Zheng","slug":"baihua-zheng","given":"Baihua","family":"Zheng","affiliation":"‡ Singapore Management University , Singapore"}],"abstract":"Estimating the travel time of a path is of great importance to smart urban mobility. Existing approaches are either based on estimating the time cost of each road segment which are not able to capture many cross-segment complex factors, or designed heuristically in a non-learning-based way which fail to leverage the natural abundant temporal labels of the data, i.e., the time stamp of each trajectory point. In this paper, we leverage on new development of deep neural networks and propose a novel auxiliary supervision model, namely DeepTravel, that can automatically and effectively extract different features, as well as make full use of the temporal labels of the trajectory data. We have conducted comprehensive experiments on real datasets to demonstrate the out-performance of DeepTravel over existing approaches.","doi":"10.24963/ijcai.2018/508","url":"https://ijcai.org/proceedings/2018/508","pdf":"https://ijcai.org/proceedings/2018/0508.pdf","markdown":"https://ijcai.org/proceedings/2018/508.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/508","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":508,"pages":{"first":3655,"last":3661},"keywords":["Machine Learning: Neural Networks","Machine Learning: Deep Learning","Machine Learning Applications: Applications of Supervised Learning"],"videos":[],"references":[{"position":1,"raw":"[Ahmed and Cook, 1979] Mohammed S. 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