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

Deng, J; Chen, X; Fan, Z; Jiang, R; Song, X; Tsang, IW

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

Deng, J Chen, X Fan, Z Jiang, R Song, X Tsang, IW

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Publisher:: Association for Computing Machinery (ACM)
Publication Type:: Journal Article
Citation:: ACM Transactions on Knowledge Discovery from Data, 2021, 15, (6), pp. 1-25
Issue Date:: 2021-05-19

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Field	Value	Language
dc.contributor.author	Deng, J
dc.contributor.author	Chen, X
dc.contributor.author	Fan, Z
dc.contributor.author	Jiang, R
dc.contributor.author	Song, X
dc.contributor.author	Tsang, IW
dc.date.accessioned	2022-05-02T02:00:37Z
dc.date.available	2022-05-02T02:00:37Z
dc.date.issued	2021-05-19
dc.identifier.citation	ACM Transactions on Knowledge Discovery from Data, 2021, 15, (6), pp. 1-25
dc.identifier.issn	1556-4681
dc.identifier.issn	1556-472X
dc.identifier.uri	http://hdl.handle.net/10453/156903
dc.description.abstract	<jats:p>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.</jats:p>
dc.language	en
dc.publisher	Association for Computing Machinery (ACM)
dc.relation	http://purl.org/au-research/grants/arc/DP180100106
dc.relation	http://purl.org/au-research/grants/arc/DP200101328
dc.relation.ispartof	ACM Transactions on Knowledge Discovery from Data
dc.relation.isbasedon	10.1145/3450528
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0806 Information Systems
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	The Pulse of Urban Transport: Exploring the Co-evolving Pattern for Spatio-temporal Forecasting
dc.type	Journal Article
utslib.citation.volume	15
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2022-05-02T02:00:30Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	15
utslib.citation.issue	6

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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http://hdl.handle.net/10453/156903