Article

Deeptransport: prediction and simulation of human mobility and transportation mode at a citywide level

Authors:

Hiroshi Kanasugi,

Ryosuke ShibasakiAuthors Info & Claims

IJCAI'16: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence

Pages 2618 - 2624

Published: 09 July 2016 Publication History

Abstract

Traffic congestion causes huge economic loss worldwide in every year due to wasted fuel, excessive air pollution, lost time, and reduced productivity. Understanding how humans move and select the transportation mode throughout a large-scale transportation network is vital for urban congestion prediction and transportation scheduling. In this study, we collect big and heterogeneous data (e.g., GPS records and transportation network data), and we build an intelligent system, namely DeepTransport, for simulating and predicting human mobility and transportation mode at a citywide level. The key component of DeepTransport is based on the deep learning architecture that that aims to understand human mobility and transportation patterns from big and heterogeneous data. Based on the learning model, given any time period, specific location of the city or people's observed movements, our system can automatically simulate or predict the persons' future movements and their transportation mode in the large-scale transportation network. Experimental results and validations demonstrate the efficiency and superior performance of our system, and suggest that human transportation mode may be predicted and simulated more easily than previously thought.

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Cited By

Fan ZYang XYuan WJiang RChen QSong XShibasaki RRenz MSarwat M(2022)Online trajectory prediction for metropolitan scale mobility digital twinProceedings of the 30th International Conference on Advances in Geographic Information Systems10.1145/3557915.3561040(1-12)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1145/3557915.3561040
Liu JZhang LZhu SLiu BLiang ZYang S(2022)Exploring Complex Dependencies for Multi-modal Semantic Trajectory PredictionNeural Processing Letters10.1007/s11063-021-10666-954:2(961-985)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s11063-021-10666-9
Fang ZYang GZhang DXie XWang GYang YZhang FZhang DZhu FChin Ooi BMiao CWang HSkrypnyk IHsu WChawla S(2021)MoCha: Large-Scale Driving Pattern Characterization for Usage-based InsuranceProceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining10.1145/3447548.3467114(2849-2857)Online publication date: 14-Aug-2021
https://dl.acm.org/doi/10.1145/3447548.3467114
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Published In

cover image Guide Proceedings

IJCAI'16: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence

July 2016

4277 pages

ISBN:9781577357704

Editor:
Gerhard Brewka
Leipzig University, Germany

Sponsors

Sony: Sony Corporation
Arizona State University: Arizona State University
Microsoft: Microsoft
Facebook: Facebook
AI Journal: AI Journal

Publisher

AAAI Press

Publication History

Published: 09 July 2016

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Cited By

Fan ZYang XYuan WJiang RChen QSong XShibasaki RRenz MSarwat M(2022)Online trajectory prediction for metropolitan scale mobility digital twinProceedings of the 30th International Conference on Advances in Geographic Information Systems10.1145/3557915.3561040(1-12)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1145/3557915.3561040
Liu JZhang LZhu SLiu BLiang ZYang S(2022)Exploring Complex Dependencies for Multi-modal Semantic Trajectory PredictionNeural Processing Letters10.1007/s11063-021-10666-954:2(961-985)Online publication date: 1-Apr-2022
https://dl.acm.org/doi/10.1007/s11063-021-10666-9
Fang ZYang GZhang DXie XWang GYang YZhang FZhang DZhu FChin Ooi BMiao CWang HSkrypnyk IHsu WChawla S(2021)MoCha: Large-Scale Driving Pattern Characterization for Usage-based InsuranceProceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining10.1145/3447548.3467114(2849-2857)Online publication date: 14-Aug-2021
https://dl.acm.org/doi/10.1145/3447548.3467114
Miao CWang JYu HZhang WQi YEl Fallah Seghrouchni ASukthankar GAn BYorke-Smith N(2020)Trajectory-User Linking with Attentive Recurrent NetworkProceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3398761.3398864(878-886)Online publication date: 5-May-2020
https://dl.acm.org/doi/10.5555/3398761.3398864
Pang YTsubouchi KYabe TSekimoto Y(2020)Intercity Simulation of Human Mobility at Rare Events via Reinforcement LearningProceedings of the 28th International Conference on Advances in Geographic Information Systems10.1145/3397536.3422244(293-302)Online publication date: 3-Nov-2020
https://dl.acm.org/doi/10.1145/3397536.3422244
Qin ZCao FYang YWang SLiu YTan CZhang D(2020)CellPredProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/33809824:1(1-24)Online publication date: 18-Mar-2020
https://dl.acm.org/doi/10.1145/3380982
Yuan HLi GBao ZFeng LMaier DPottinger RDoan ATan WAlawini ANgo H(2020)Effective Travel Time Estimation: When Historical Trajectories over Road Networks MatterProceedings of the 2020 ACM SIGMOD International Conference on Management of Data10.1145/3318464.3389771(2135-2149)Online publication date: 11-Jun-2020
https://dl.acm.org/doi/10.1145/3318464.3389771
Soares ESalehinejad HCampos CValaee S(2020)Recurrent Neural Networks for Online Travel Mode Detection2019 IEEE Global Communications Conference (GLOBECOM)10.1109/GLOBECOM38437.2019.9013316(1-6)Online publication date: 17-Jun-2020
https://dl.acm.org/doi/10.1109/GLOBECOM38437.2019.9013316
Lin XWang YXiao XLi ZBhowmick SZhu WTao DCheng XCui PRundensteiner ECarmel DHe QXu Yu J(2019)Path Travel Time Estimation using Attribute-related Hybrid Trajectories NetworkProceedings of the 28th ACM International Conference on Information and Knowledge Management10.1145/3357384.3357927(1973-1982)Online publication date: 3-Nov-2019
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Zhao YWang XLi JZhang DYang Z(2019)CellTransProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/33512833:3(1-26)Online publication date: 9-Sep-2019
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