{"type":"ScholarlyArticle","title":"Aggressive Driving Saves More Time? Multi-task Learning for Customized Travel Time Estimation","authors":[{"name":"Ruipeng Gao","slug":"ruipeng-gao","given":"Ruipeng","family":"Gao","affiliation":"Beijing Jiaotong University"},{"name":"Xiaoyu Guo","slug":"xiaoyu-guo","given":"Xiaoyu","family":"Guo","affiliation":"Beijing Jiaotong University"},{"name":"Fuyong Sun","slug":"fuyong-sun","given":"Fuyong","family":"Sun","affiliation":"Beijing Jiaotong University"},{"name":"Lin Dai","slug":"lin-dai","given":"Lin","family":"Dai","affiliation":"Beijing Jiaotong University"},{"name":"Jiayan Zhu","slug":"jiayan-zhu","given":"Jiayan","family":"Zhu","affiliation":"Beijing DiDi Infinity Technology and Development Co. , Ltd."},{"name":"Chenxi Hu","slug":"chenxi-hu","given":"Chenxi","family":"Hu","affiliation":"Beijing DiDi Infinity Technology and Development Co. , Ltd."},{"name":"Haibo Li","slug":"haibo-li","given":"Haibo","family":"Li","affiliation":"Beijing DiDi Infinity Technology and Development Co. , Ltd."}],"abstract":"Estimating the origin-destination travel time is a fundamental problem in many location-based services for vehicles, e.g., ride-hailing, vehicle dispatching, and route planning. Recent work has made significant progress to accuracy but they largely rely on GPS traces which are too coarse to model many personalized driving events. In this paper, we propose Customized Travel Time Estimation (CTTE) that fuses GPS traces, smartphone inertial data, and road network within a deep recurrent neural network. It constructs a link traffic database with topology representation, speed statistics, and query distribution. It also uses inertial data to estimate the arbitrary phone's pose in car, and detects fine-grained driving events. The multi-task learning structure predicts both traffic speed at public level and customized travel time at personal level. Extensive experiments on two real-world traffic datasets from Didi Chuxing have demonstrated our effectiveness.","doi":"10.24963/ijcai.2019/234","url":"https://ijcai.org/proceedings/2019/234","pdf":"https://ijcai.org/proceedings/2019/0234.pdf","markdown":"https://ijcai.org/proceedings/2019/234.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/234","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Knowledge Representation and Reasoning","track":"Main track","number":234,"pages":{"first":1689,"last":1696},"keywords":["Multidisciplinary Topics and Applications: Transportation","Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning"],"videos":[],"references":[{"position":1,"raw":"[BMV, 2019] Aggressive driving. https://www.in.gov/bmv/ files/Drivers Manual Chapter 5.pdf, 2019.","title":"Aggressive driving","authors":"Bmv","year":"2019","venue":null,"doi":null,"url":"https://www.in.gov/bmv/files/DriversManualChapter5.pdf","internal_url":null},{"position":2,"raw":"[Chen et al., 2018] Changhao Chen, Xiaoxuan Lu, Andrew Markham, and Niki Trigoni. Ionet: Learning to cure the curse of drift in inertial odometry. In Proceedings of AAAI, pages 6468-6476, 2018.","title":"Ionet: Learning to cure the curse of drift in inertial odometry","authors":"Chen","year":"2018","venue":"Proceedings of AAAI","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chun-Hsin Wu et al., 2004] Chun-Hsin Wu, Jan-Ming Ho, and D. T. Lee. Travel-time prediction with support vector regression. IEEE Transactions on Intelligent Transporta- tion Systems, 5(4):276-281, 2004.","title":"Travel-time prediction with support vector regression","authors":"Chun-Hsin Wu, Jan-Ming Ho, D T Lee","year":"2004","venue":"IEEE Transactions on Intelligent Transportation Systems","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Gal and Ghahramani, 2016] Yarin Gal and Zoubin Ghahra- mani. A theoretically grounded application of dropout in recurrent neural networks. In Proceedings of NIPS, pages 1027-1035, 2016.","title":"Yarin Gal and Zoubin Ghahramani. A theoretically grounded application of dropout in recurrent neural networks","authors":"Ghahramani Gal","year":"2016","venue":"Proceedings of NIPS","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Gao et al., 2017] R. Gao, M. Zhao, T. Ye, F. Ye, Y. Wang, and G. Luo. Smartphone-based real time vehicle tracking in indoor parking structures. IEEE Transactions on Mobile Computing, 16(7):2023-2036, 2017.","title":"Smartphone-based real time vehicle tracking in indoor parking structures","authors":"R Gao, M Zhao, T Ye, F Ye, Y Wang, G Luo","year":"2017","venue":"IEEE Transactions on Mobile Computing","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[He et al., 2016] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Identity mappings in deep residual networks. In Proceedings of ECCV, pages 630-645, 10 2016.","title":"Identity mappings in deep residual networks","authors":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun","year":"2016","venue":"Proceedings of ECCV","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Jagadeesh et al., 2004] G. R. Jagadeesh, T. Srikanthan, and X. D. Zhang. A map matching method for gps based real- time vehicle location. Journal of Navigation, 57(3):429- 440, 2004.","title":"A map matching method for gps based realtime vehicle location","authors":"G R Jagadeesh, T Srikanthan, X D Zhang","year":"2004","venue":"Journal of Navigation","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Li et al., 2018] Yaguang Li, Kun Fu, Zheng Wang, Cyrus Shahabi, Jieping Ye, and Yan Liu. Multi-task representa- tion learning for travel time estimation. In Proceedings of KDD, pages 1695-1704, 2018.","title":"Multi-task representation learning for travel time estimation","authors":"Li","year":"2018","venue":"Proceedings of KDD","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Liang et al., 2018] Yuxuan Liang, Songyu Ke, Junbo Zhang, Xiuwen Yi, and Yu Zheng. Geoman: Multi-level attention networks for geo-sensory time series prediction. In Proceedings of IJCAI, pages 3428-3434, 2018.","title":"Geoman: Multi-level attention networks for geo-sensory time series prediction","authors":"Yuxuan Liang, Songyu Ke, Junbo Zhang, Xiuwen Yi, Yu Zheng","year":"2018","venue":"Proceedings of IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2018/476"},{"position":10,"raw":"[Lindqvist and Hong, 2011] Janne Lindqvist and Jason Hong. Undistracted driving: A mobile phone that doesn't distract. In ACM HotMobile, pages 70-75, 2011.","title":"Undistracted driving: A mobile phone that doesn't distract","authors":"Janne Lindqvist, Jason Hong","year":"2011","venue":"ACM HotMobile","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Ma et al., 2015] Xiaolei Ma, Zhimin Tao, Yinhai Wang, Haiyang Yu, and Yunpeng Wang. Long short-term memo- ry neural network for traffic speed prediction using remote microwave sensor data. Transportation Research Part C: Emerging Technologies, 54:187 -197, 2015.","title":"Long short-term memory neural network for traffic speed prediction using remote microwave sensor data","authors":"Xiaolei Ma, Zhimin Tao, Yinhai Wang, Haiyang Yu, Yunpeng Wang","year":"2015","venue":"Transportation Research Part C: Emerging Technologies","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Mikolov et al., 2013] Tomas Mikolov, Kai Chen, Greg Cor- rado, and Jeffrey Dean. Efficient estimation of word rep- resentations in vector space. CoRR, abs/1301.3781, 2013.","title":"Efficient estimation of word representations in vector space","authors":"Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Mohan et al., 2008] Prashanth Mohan, Venkata N. Padman- abhan, and Ramachandran Ramjee. Nericell: Using mo- bile smartphones for rich monitoring of road and traffic conditions. In ACM SenSys, pages 357-358, 2008.","title":"Nericell: Using mobile smartphones for rich monitoring of road and traffic conditions","authors":"Prashanth Mohan, N Venkata, Ramachandran Padmanabhan, Ramjee","year":"2008","venue":"ACM SenSys","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Pan et al., 2012] B. Pan, U. Demiryurek, and C. Shahabi. Utilizing real-world transportation data for accurate traffic prediction. In 2012 IEEE 12th International Conference on Data Mining, pages 595-604, 2012. Perozzi et al., 2014]","title":"Utilizing real-world transportation data for accurate traffic prediction","authors":"B Pan, U Demiryurek, C Shahabi","year":"2012","venue":"IEEE 12th International Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. Deepwalk: Online learning of social repre- sentations. In Proceedings of KDD, pages 701-710, 2014.","title":"Deepwalk: Online learning of social representations","authors":"Bryan Perozzi, Rami Al-Rfou, Steven Skiena","year":"2014","venue":"Proceedings of KDD","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Rahmani et al., 2013] M. Rahmani, E. Jenelius, and H. N. Koutsopoulos. Route travel time estimation using low- frequency floating car data. In Proceedings of IEEE Conference on Intelligent Transportation Systems (ITSC), pages 2292-2297, 2013.","title":"Route travel time estimation using lowfrequency floating car data","authors":"M Rahmani, E Jenelius, H N Koutsopoulos","year":"2013","venue":"Proceedings of IEEE Conference on Intelligent Transportation Systems (ITSC)","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Ristanoski et al., 2013] Goce Ristanoski, Wei Liu, and James Bailey. Time series forecasting using distribution enhanced linear regression. In Advances in Knowledge Discovery and Data Mining, pages 484-495. Springer Berlin Heidelberg, 2013.","title":"Time series forecasting using distribution enhanced linear regression","authors":"Goce Ristanoski, Wei Liu, James Bailey","year":"2013","venue":"Advances in Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Sevlian and Rajagopal, 2010] Raffi Sevlian and Ram Ra- jagopal. Travel time estimation using floating car data. CoRR, abs/1012.4249, 2010.","title":"Raffi Sevlian and Ram Rajagopal. Travel time estimation using floating car data","authors":"Rajagopal Sevlian","year":"2010","venue":null,"doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Shen et al., 2018] Sheng Shen, Mahanth Gowda, and Romit Roy Choudhury. Closing the gaps in inertial motion track- ing. In Proceedings of MobiCom, pages 429-444, 2018.","title":"Closing the gaps in inertial motion tracking","authors":"Sheng Shen, Mahanth Gowda, Romit Roy Choudhury","year":"2018","venue":"Proceedings of MobiCom","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Wang et al., 2016a] Hongjian Wang, Yu-Hsuan Kuo, Daniel Kifer, and Zhenhui Li. A simple baseline for travel time estimation using large-scale trip data. In Proceedings of ACM SIGSPATIAL, 2016.","title":"A simple baseline for travel time estimation using large-scale trip data","authors":"Wang","year":"2016","venue":"Proceedings of ACM SIGSPATIAL","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Wang et al., 2016b] J. Wang, Q. Gu, J. Wu, G. Liu, and Z. Xiong. Traffic speed prediction and congestion source exploration: A deep learning method. In Proceedings of IEEE ICDM, pages 499-508, 2016.","title":"Traffic speed prediction and congestion source exploration: A deep learning method","authors":"Wang","year":"2016","venue":"Proceedings of IEEE ICDM","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Wang et al., 2016c] J. Wang, Q. Gu, J. Wu, G. Liu, and Z. Xiong. Traffic speed prediction and congestion source exploration: A deep learning method. In Proceedings of ICDM, pages 499-508, 2016.","title":"Traffic speed prediction and congestion source exploration: A deep learning method","authors":"Wang","year":"2016","venue":"Proceedings of ICDM","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Wang et al., 2018a] Dong Wang, Junbo Zhang, Wei Cao, Jian Li, and Yu Zheng. When will you arrive? estimating travel time based on deep neural networks. In Proceedings of AAAI, 2018.","title":"When will you arrive? estimating travel time based on deep neural networks","authors":"Wang","year":"2018","venue":"Proceedings of AAAI","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Wang et al., 2018b] Zheng Wang, Kun Fu, and Jieping Ye. Learning to estimate the travel time. In Proceedings of KDD, pages 858-866, 2018.","title":"Learning to estimate the travel time","authors":"Wang","year":"2018","venue":"Proceedings of KDD","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Williams and Hoel, 2003] Billy M. Williams and Lester A. Hoel. Modeling and forecasting vehicular traffic flow as a seasonal arima process: Theoretical basis and empirical results. Journal of Transportation Engineering, 129:664- 672, 2003.","title":"Modeling and forecasting vehicular traffic flow as a seasonal arima process: Theoretical basis and empirical results","authors":"Billy M Williams, Lester A Hoel","year":"2003","venue":"Journal of Transportation Engineering","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Yao et al., 2017] Shuochao Yao, Shaohan Hu, Yiran Zhao, Aston Zhang, and Tarek Abdelzaher. Deepsense: A unified deep learning framework for time-series mobile sensing data processing. In Proceedings of WWW, pages 351-360, 2017.","title":"Deepsense: A unified deep learning framework for time-series mobile sensing data processing","authors":"Yao","year":"2017","venue":"Proceedings of WWW","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Zhang et al., 2018] Hanyuan Zhang, Hao Wu, Weiwei Sun, and Baihua Zheng. Deeptravel: a neural network based travel time estimation model with auxiliary supervision. In Proceedings of IJCAI, 2018.","title":"Deeptravel: a neural network based travel time estimation model with auxiliary supervision","authors":"Hanyuan Zhang, Hao Wu, Weiwei Sun, Baihua Zheng","year":"2018","venue":"Proceedings of IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2018/508"}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}