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

Deng, Jinliang1,2; Chen, Xiusi3; Fan, Zipei1,4; Jiang, Renhe1,4; Song, Xuan1,4; Tsang, Ivor W.5

2021-12-01

10.1145/3450528

ACM Transactions on Knowledge Discovery from Data   影响因子和分区

1556-4681

1556-472X

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.

Demand forecasting multi-modal learning spatio-temporal analysis neural network

SCI ; EI

英语

第一 ; 通讯

ARC["DP180100106","DP200101328"]

Computer Science

Computer Science, Information Systems ; Computer Science, Software Engineering

WOS:000766204500010

ASSOC COMPUTING MACHINERY

20222012121821

Bicycles ; Data fusion ; Data mining ; Digital storage ; Modal analysis ; Taxicabs ; Urban transportation

Highway Transportation:432 ; Passenger Highway Transportation:432.2 ; Railroad Transportation:433 ; Automobiles:662.1 ; Data Storage, Equipment and Techniques:722.1 ; Data Processing and Image Processing:723.2 ; Mathematics:921

Web of Science

1.Southern Univ Sci & Technol, Xueyuan Ave, Shenzhen 518055, Guangdong, Peoples R China
2.Univ Technol Sydney, Sydney, NSW, Australia
3.Univ Calif Los Angeles, 405 Hilgard Ave, Los Angeles, CA 90095 USA
4.Univ Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 2778561, Japan
5.Univ Technol Sydney, Australian Artificial Intelligence Inst, 15 Broadway, Ultimo, NSW 2007, Australia

Deng, Jinliang,Chen, Xiusi,Fan, Zipei,et al. The Pulse of Urban Transport: Exploring the Co-evolving Pattern for Spatio-temporal Forecasting[J]. ACM Transactions on Knowledge Discovery from Data,2021,15(6).

Deng, Jinliang,Chen, Xiusi,Fan, Zipei,Jiang, Renhe,Song, Xuan,&Tsang, Ivor W..(2021).The Pulse of Urban Transport: Exploring the Co-evolving Pattern for Spatio-temporal Forecasting.ACM Transactions on Knowledge Discovery from Data,15(6).

Deng, Jinliang,et al."The Pulse of Urban Transport: Exploring the Co-evolving Pattern for Spatio-temporal Forecasting".ACM Transactions on Knowledge Discovery from Data 15.6(2021).