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Collective Traffic Prediction with Partially Observed Traffic History using Location-Based Social Media

Authors:

Yanhua LiAuthors Info & Claims

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

Pages 2179 - 2184

https://doi.org/10.1145/2983323.2983662

Published: 24 October 2016 Publication History

Abstract

Traffic prediction has become an important and active research topic in the last decade. Existing solutions mainly focus on exploiting the past and current traffic data, collected from various kinds of sensors, such as loop detectors, GPS devices, etc. In real-world road systems, only a small fraction of the road segments are deployed with sensors. For all the other road segments without sensors or historical traffic data, previous methods may no longer work. In this paper, we propose to use location-based social media, which captures a much larger area of the road systems than deployed sensors, to predict the traffic conditions. A simple but effective method called CTP is proposed to incorporate location-based social media semantics into the learning process. CTP also exploits complex dependencies among different regions to improve the prediction performances through collective inference. Empirical studies using traffic data and tweets collected in Los Angeles area demonstrate the effectiveness of CTP.

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Cited By

Zhang SZhang JYang LWang CGao Z(2024)COV-STFormer for Short-Term Passenger Flow Prediction During COVID-19 in Urban Rail Transit SystemsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.332337925:5(3793-3811)Online publication date: May-2024
https://doi.org/10.1109/TITS.2023.3323379
Laynes-Fiascunari VRabelo LGutierrez-Franco E(2024)Enhancing Last-Mile Delivery: Social Media Insights and Deep Learning ApplicationsComputational Logistics10.1007/978-3-031-71993-6_12(176-186)Online publication date: 8-Sep-2024
https://doi.org/10.1007/978-3-031-71993-6_12
Laynes-Fiascunari VGutierrez-Franco ERabelo LSarmiento ALee G(2023)A Framework for Urban Last-Mile Delivery Traffic Forecasting: An In-Depth Review of Social Media Analytics and Deep Learning TechniquesApplied Sciences10.3390/app1310588813:10(5888)Online publication date: 10-May-2023
https://doi.org/10.3390/app13105888
Show More Cited By

Index Terms

Collective Traffic Prediction with Partially Observed Traffic History using Location-Based Social Media
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

October 2016

2566 pages

ISBN:9781450340731

DOI:10.1145/2983323

General Chairs:
Snehasis Mukhopadhyay
Indiana University Purdue University Indianapolis, USA
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign, USA
,
Program Chairs:
Elisa Bertino
Purdue University
,
Fabio Crestani
University of Lugano
,
Javed Mostafa
University of North Carolina
,
Jie Tang
Tsinghua University
,
Luo Si
Alibaba Group Inc & Purdue University
,
Xiaofang Zhou
University of Queensland
,
Yi Chang
Yahoo Research
,
Yunyao Li
IBM Research - Almaden
,
Parikshit Sondhi
WalmartLabs

Copyright © 2016 ACM.

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Publication History

Published: 24 October 2016

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CIKM'16: ACM Conference on Information and Knowledge Management

October 24 - 28, 2016

Indiana, Indianapolis, USA

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CIKM '16 Paper Acceptance Rate 160 of 701 submissions, 23%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Zhang SZhang JYang LWang CGao Z(2024)COV-STFormer for Short-Term Passenger Flow Prediction During COVID-19 in Urban Rail Transit SystemsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.332337925:5(3793-3811)Online publication date: May-2024
https://doi.org/10.1109/TITS.2023.3323379
Laynes-Fiascunari VRabelo LGutierrez-Franco E(2024)Enhancing Last-Mile Delivery: Social Media Insights and Deep Learning ApplicationsComputational Logistics10.1007/978-3-031-71993-6_12(176-186)Online publication date: 8-Sep-2024
https://doi.org/10.1007/978-3-031-71993-6_12
Laynes-Fiascunari VGutierrez-Franco ERabelo LSarmiento ALee G(2023)A Framework for Urban Last-Mile Delivery Traffic Forecasting: An In-Depth Review of Social Media Analytics and Deep Learning TechniquesApplied Sciences10.3390/app1310588813:10(5888)Online publication date: 10-May-2023
https://doi.org/10.3390/app13105888
Hu XZhou ZLi HHu YGu FKersten JFan HKlan F(2023)Location Reference Recognition from Texts: A Survey and ComparisonACM Computing Surveys10.1145/362581956:5(1-37)Online publication date: 27-Nov-2023
https://dl.acm.org/doi/10.1145/3625819
Wang YHe ZHu J(2022)Traffic Information Mining From Social Media Based on the MC-LSTM-Conv ModelIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2020.302109623:2(1132-1144)Online publication date: Feb-2022
https://doi.org/10.1109/TITS.2020.3021096
Pan MLi YZhang ZLuo J(2022)SCCS: Smart Cloud Commuting System With Shared Autonomous VehiclesIEEE Transactions on Big Data10.1109/TBDATA.2020.30412638:5(1301-1311)Online publication date: 1-Oct-2022
https://doi.org/10.1109/TBDATA.2020.3041263
Zhang YLi YZhou XKong XLuo J(2022)Off-Deployment Traffic Estimation — A Traffic Generative Adversarial Networks ApproachIEEE Transactions on Big Data10.1109/TBDATA.2020.30145118:4(1084-1095)Online publication date: 1-Aug-2022
https://doi.org/10.1109/TBDATA.2020.3014511
Zhang YLi YZhou XKong XLuo J(2022)STrans-GAN: Spatially-Transferable Generative Adversarial Networks for Urban Traffic Estimation2022 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM54844.2022.00085(743-752)Online publication date: Nov-2022
https://doi.org/10.1109/ICDM54844.2022.00085
Zhang YLi YZhou XLuo J(2022)Mest-GAN: Cross-City Urban Traffic Estimation with Me ta S patial-T emporal G enerative A dversarial N etworks2022 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM54844.2022.00084(733-742)Online publication date: Nov-2022
https://doi.org/10.1109/ICDM54844.2022.00084
Laynes Fiascunari VRabelo L(2022)Preliminary Study for Impact of Social Media Networks on Traffic PredictionApplied Informatics10.1007/978-3-031-19647-8_15(204-218)Online publication date: 19-Oct-2022
https://doi.org/10.1007/978-3-031-19647-8_15
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