Learning Shared Mobility-Aware Knowledge for Multiple Urban Travel Demands

Qianru Wang, Bin Guo, Yi Ouyang, Lu Cheng, Liang Wang, Zhiwen Yu, Huan Liu

Research output: Contribution to journalArticlepeer-review

Abstract

With the growth of Internet of Things (IoT) devices, smart travel methods, such as sharing-bike and ride-hailing become popular commuting methods. With people's growing needs and the rapid dynamics in a city environment, simply using a single travel demand for prediction may be insufficient. Alternatively, modeling multiple travel demands simultaneously can deepen our understanding toward the status of these potentially correlated demands and deploy the transportation in the city better. An important observation in this work is that multiple travel demands in a city often show common patterns, referred to as the shared mobility-aware knowledge. In addition, there are also unique patterns that characterize individual travel demand resulting in unique knowledge. To better leverage the shared and unique knowledge, we propose a novel framework (MultiST) to predict multiple spatialoral sequences (multiple travel demands) via two components that extract the shared and unique spatialoral dependencies, respectively. For the unique component, we use convolutional neural networks and gated recurrent units to embed unique knowledge. For the shared component, we design a recurrent Gaussian cell to extract temporal dependencies. Empirical results show that MultiST outperforms six state-of-the-art baseline methods and three variants of MultiST. We further visualize the temporal dependencies of the shared knowledge and discuss the practical implications.

Original languageEnglish (US)
Pages (from-to)7025-7035
Number of pages11
JournalIEEE Internet of Things Journal
Volume9
Issue number9
DOIs
StatePublished - May 1 2022
Externally publishedYes

Keywords

  • Gaussian model
  • Multiple urban demands
  • Shared knowledge
  • Spatial - temporal prediction

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications

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