Traffic Demand Prediction Based on Dynamic Transition Convolutional Neural Network

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

46 Scopus Citations
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Author(s)

  • Bowen Du
  • Xiao Hu
  • Leilei Sun
  • Yanan Qiao
  • Weifeng Lv

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number8968739
Pages (from-to)1237-1247
Journal / PublicationIEEE Transactions on Intelligent Transportation Systems
Volume22
Issue number2
Online published24 Jan 2020
Publication statusPublished - Feb 2021

Abstract

Precise traffic demand prediction could help government and enterprises make better management and operation decisions by providing them with data-driven insights. However, it is a nontrivial effort to design an effective traffic demand prediction method due to the spatial and temporal characteristics of traffic demand distributions, dynamics of human mobility, and impacts of multiple environmental factors. To handle these problems, a Dynamic Transition Convolutional Neural Network (DTCNN) is proposed for the purpose of precise traffic demand prediction. Particularly, a transition network is first constructed according to the citiwide historical departure and arrival records, where the nodes are virtual stations discovered by a density-peak based clustering algorithm and the edges of two nodes correspond to transition flows of two stations. Then, a dynamic transition convolution unit is designed to model the spatial distributions of the traffic demands, and to capture the evolution of the demand dynamics. Last, a unifying learning framework is provided to incorporate the spatiotemporal states of the traffic demands with environmental factors. Experiments have been conducted on NYC taxi and bike-sharing data, and the results validate the effectiveness of the proposed method.

Research Area(s)

  • Traffic demand prediction, spatiotemporal, transition convolution, deep learning

Citation Format(s)

Traffic Demand Prediction Based on Dynamic Transition Convolutional Neural Network. / Du, Bowen; Hu, Xiao; Sun, Leilei et al.
In: IEEE Transactions on Intelligent Transportation Systems, Vol. 22, No. 2, 8968739, 02.2021, p. 1237-1247.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review