research-article

The Pulse of Urban Transport: Exploring the Co-evolving Pattern for Spatio-temporal Forecasting

Authors:

Ivor W. TsangAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 15, Issue 6

Article No.: 103, Pages 1 - 25

https://doi.org/10.1145/3450528

Published: 19 May 2021 Publication History

Abstract

Transportation demand forecasting is a topic of large practical value. However, the model that fits the demand of one transportation by only considering the historical data of its own could be vulnerable since random fluctuations could easily impact the modeling. On the other hand, common factors like time and region attribute, drive the evolution demand of different transportation, leading to a co-evolving intrinsic property between different kinds of transportation. In this work, we focus on exploring the co-evolution between different modes of transport, e.g., taxi demand and shared-bike demand. Two significant challenges impede the discovery of the co-evolving pattern: (1) diversity of the co-evolving correlation, which varies from region to region and time to time. (2) Multi-modal data fusion. Taxi demand and shared-bike demand are time-series data, which have different representations with the external factors. Moreover, the distribution of taxi demand and bike demand are not identical. To overcome these challenges, we propose a novel method, known as co-evolving spatial temporal neural network (CEST). CEST learns a multi-view demand representation for each mode of transport, extracts the co-evolving pattern, then predicts the demand for the target transportation based on multi-scale representation, which includes fine-scale demand information and coarse-scale pattern information. We conduct extensive experiments to validate the superiority of our model over the state-of-art models.

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Cited By

Yi PHuang FPeng JBao Z(2024)Dynamic Spatial-Temporal Embedding via Neural Conditional Random Field for Multivariate Time Series ForecastingACM Transactions on Spatial Algorithms and Systems10.1145/367516510:4(1-23)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3675165
Wang PLuo XTai WZhang KTrajcevsky GZhou F(2024)Score-based Graph Learning for Urban Flow PredictionACM Transactions on Intelligent Systems and Technology10.1145/365562915:3(1-25)Online publication date: 17-May-2024
https://dl.acm.org/doi/10.1145/3655629
Rong CLiu ZDing JLi Y(2024)Learning to Generate Temporal Origin-destination Flow Based-on Urban Regional Features and Traffic InformationACM Transactions on Knowledge Discovery from Data10.1145/364914118:6(1-17)Online publication date: 20-Feb-2024
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Index Terms

The Pulse of Urban Transport: Exploring the Co-evolving Pattern for Spatio-temporal Forecasting
1. Applied computing
  1. Operations research
    1. Transportation
2. Information systems
  1. Information systems applications
    1. Data mining
    2. Spatial-temporal systems

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Information & Contributors

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 15, Issue 6

June 2021

474 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3465438

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

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Copyright © 2021 Association for Computing Machinery.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 19 May 2021

Accepted: 01 February 2021

Revised: 01 November 2020

Received: 01 June 2020

Published in TKDD Volume 15, Issue 6

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Yi PHuang FPeng JBao Z(2024)Dynamic Spatial-Temporal Embedding via Neural Conditional Random Field for Multivariate Time Series ForecastingACM Transactions on Spatial Algorithms and Systems10.1145/367516510:4(1-23)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3675165
Wang PLuo XTai WZhang KTrajcevsky GZhou F(2024)Score-based Graph Learning for Urban Flow PredictionACM Transactions on Intelligent Systems and Technology10.1145/365562915:3(1-25)Online publication date: 17-May-2024
https://dl.acm.org/doi/10.1145/3655629
Rong CLiu ZDing JLi Y(2024)Learning to Generate Temporal Origin-destination Flow Based-on Urban Regional Features and Traffic InformationACM Transactions on Knowledge Discovery from Data10.1145/364914118:6(1-17)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1145/3649141
Deng JChen XJiang RDu Yin Yang YSong XTsang I(2024)Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series ForecastingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.337193136:8(3783-3800)Online publication date: 1-Aug-2024
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Yang HWang ESong YChen DWang XWang DLi J(2024)Identification and prediction method for acoustic emission and electromagnetic radiation signals of rock burst based on deep learningPhysics of Fluids10.1063/5.021940936:7Online publication date: 24-Jul-2024
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Liu ZYu LLi WZhu TSun L(2024)Multi-mode Spatial-Temporal Data Modeling with Fully Connected NetworksKnowledge Science, Engineering and Management10.1007/978-981-97-5498-4_18(233-247)Online publication date: 16-Aug-2024
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Deng JDeng JYin DJiang RSong X(2023)TTS-Norm: Forecasting Tensor Time Series via Multi-Way NormalizationACM Transactions on Knowledge Discovery from Data10.1145/360589418:1(1-25)Online publication date: 10-Aug-2023
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Wang CZhang KWang HChen B(2023)Auto-STGCN: Autonomous Spatial-Temporal Graph Convolutional Network SearchACM Transactions on Knowledge Discovery from Data10.1145/357128517:5(1-21)Online publication date: 7-Apr-2023
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