Spatial-Temporal Taxi Demand Prediction Using LSTM-CNN

Pengfeng Shu, Ying Sun, Yifan Zhao, Gangyan Xu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

6 Citations (Scopus)

Abstract

Spatial-temporal taxi demand prediction is vital for efficient planning and scheduling of taxis, which could improve overall service level of public transportation in megacities. However, previous research mainly focuses on predicting the taxi demand within certain areas, and seldom considers the inter-area demands, which is essential for the macro-level taxi scheduling. Therefore, this paper proposes an effective model for spatial-temporal inter-area taxi demand prediction through integrating Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). CNN is adopted to extract the correlation between features and temporal closeness dependence while LSTM for fusing them in time series. The model is verified using the historical data in Haikou (China) and results show it is more accurate and stable than traditional LSTM in inter-area taxi demand prediction.

Original languageEnglish
Title of host publication2020 IEEE 16th International Conference on Automation Science and Engineering, CASE 2020
PublisherIEEE Computer Society
Pages1226-1230
Number of pages5
ISBN (Electronic)9781728169040
DOIs
Publication statusPublished - Aug 2020
Externally publishedYes
Event16th IEEE International Conference on Automation Science and Engineering, CASE 2020 - Hong Kong, Hong Kong
Duration: 20 Aug 202021 Aug 2020

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2020-August
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Conference

Conference16th IEEE International Conference on Automation Science and Engineering, CASE 2020
Country/TerritoryHong Kong
CityHong Kong
Period20/08/2021/08/20

Keywords

  • CNN
  • LSTM
  • Smart City
  • Smart Mobility
  • Taxi Demand Prediction

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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