research-article

ZEST: A Hybrid Model on Predicting Passenger Demand for Chauffeured Car Service

Authors:

Hua Wei,

Yuandong Wang,

Tianyu Wo,

Yaxiao Liu,

Jie XuAuthors Info & Claims

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

Pages 2203 - 2208

https://doi.org/10.1145/2983323.2983667

Published: 24 October 2016 Publication History

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Abstract

Chauffeured car service based on mobile applications like Uber or Didi suffers from supply-demand disequilibrium, which can be alleviated by proper prediction on the distribution of passenger demand. In this paper, we propose a Zero-Grid Ensemble Spatio Temporal model (ZEST) to predict passenger demand with four predictors: a temporal predictor and a spatial predictor to model the influences of local and spatial factors separately, an ensemble predictor to combine the results of former two predictors comprehensively and a Zero-Grid predictor to predict zero demand areas specifically since any cruising within these areas costs extra waste on energy and time of driver. We demonstrate the performance of ZEST on actual operational data from ride-hailing applications with more than 6 million order records and 500 million GPS points. Experimental results indicate our model outperforms 5 other baseline models by over 10% both in MAE and sMAPE on the three-month datasets.

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Cited By

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N AK A(2024)Implementing FlowFlexDP for Advancing Passenger Demand Prediction using Cellular Footprints2024 2nd International Conference on Networking and Communications (ICNWC)10.1109/ICNWC60771.2024.10537421(1-10)Online publication date: 2-Apr-2024
https://doi.org/10.1109/ICNWC60771.2024.10537421
Linhares Lda Silva AMeireles M(2023)An incremental approach to forecasting and classification of taxi demand based on evolving fuzzy systemsJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-22211544:3(5059-5084)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.3233/JIFS-222115
Wang QGuo BCheng LYu ZLiu H(2023)CausalSE: Understanding Varied Spatial Effects with Missing Data Toward Adding New Bike-sharing StationsACM Transactions on Knowledge Discovery from Data10.1145/353642717:2(1-24)Online publication date: 20-Mar-2023
https://dl.acm.org/doi/10.1145/3536427
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Index Terms

ZEST: A Hybrid Model on Predicting Passenger Demand for Chauffeured Car Service
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Data stream mining
    2. Spatial-temporal systems
      1. Location based services
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Database theory
      1. Data modeling

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Published In

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

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New York, NY, United States

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

View all

N AK A(2024)Implementing FlowFlexDP for Advancing Passenger Demand Prediction using Cellular Footprints2024 2nd International Conference on Networking and Communications (ICNWC)10.1109/ICNWC60771.2024.10537421(1-10)Online publication date: 2-Apr-2024
https://doi.org/10.1109/ICNWC60771.2024.10537421
Linhares Lda Silva AMeireles M(2023)An incremental approach to forecasting and classification of taxi demand based on evolving fuzzy systemsJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-22211544:3(5059-5084)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.3233/JIFS-222115
Wang QGuo BCheng LYu ZLiu H(2023)CausalSE: Understanding Varied Spatial Effects with Missing Data Toward Adding New Bike-sharing StationsACM Transactions on Knowledge Discovery from Data10.1145/353642717:2(1-24)Online publication date: 20-Mar-2023
https://dl.acm.org/doi/10.1145/3536427
Yao SZhang HWang CZeng DYe M(2023)GSTGAT: Gated spatiotemporal graph attention network for traffic demand forecastingIET Intelligent Transport Systems10.1049/itr2.1244918:2(258-268)Online publication date: 21-Nov-2023
https://doi.org/10.1049/itr2.12449
Saxena DCao J(2022)Multimodal Spatio-Temporal Prediction with Stochastic Adversarial NetworksACM Transactions on Intelligent Systems and Technology10.1145/345802513:2(1-23)Online publication date: 5-Jan-2022
https://dl.acm.org/doi/10.1145/3458025
Wang YYin HWu LChen TLiu C(2022)Secure Your Ride: Real-time Matching Success Rate Prediction for Passenger-Driver PairsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2021.3112739(1-1)Online publication date: 2022
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Cao DZeng KWang JSharma PMa XLiu YZhou S(2022)BERT-Based Deep Spatial-Temporal Network for Taxi Demand PredictionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2021.312211423:7(9442)Online publication date: 2022
https://doi.org/10.1109/TITS.2021.3122114
Zhang CZhu FLv YYe PWang F(2022)MLRNN: Taxi Demand Prediction Based on Multi-Level Deep Learning and Regional Heterogeneity AnalysisIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2021.308051123:7(8412-8422)Online publication date: Jul-2022
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Zhang CZhu FWang XSun LTang HLv Y(2022)Taxi Demand Prediction Using Parallel Multi-Task Learning ModelIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2020.301554223:2(794-803)Online publication date: Feb-2022
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Uchiteleva EPrimak SLuccini MHussein AShami A(2022)The TriLS Approach for Drift-Aware Time-Series Prediction in IIoT EnvironmentIEEE Transactions on Industrial Informatics10.1109/TII.2021.312982518:10(6581-6591)Online publication date: Oct-2022
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A Unified Framework with Multi-source Data for Predicting Passenger Demands of Ride Services

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