Journal on Communications ›› 2021, Vol. 42 ›› Issue (3): 54-64.doi: 10.11959/j.issn.1000-436x.2021018

• Papers • Previous Articles     Next Articles

Study of forecasting urban private car volumes based on multi-source heterogeneous data fusion

Chenxi LIU, Dong WANG, Huiling CHEN, Renfa LI   

  1. College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
  • Revised:2020-12-13 Online:2021-03-25 Published:2021-03-01
  • Supported by:
    The National Natural Science Foundation of China(61272061);The Open Fund of State Key Laboratory of GeoInformation Engineering(SKLGIE2018-M-4-3)

Abstract:

By effectively capturing the spatio-temporal characteristics of urban private car travel, a multi-source heterogeneous data fusion model for private car volume prediction was proposed.Firstly, private car trajectory and area-of-interest data were integrated.Secondly, the spatio-temporal correlations between private car travel and urban areas were modeled through multi-view spatio-temporal graphs, the multi-graph convolution-attention network (MGC-AN) was proposed to extract the spatio-temporal characteristics of private car travel.Finally, the spatio-temporal characteristics and external characteristics such as weather were integrated for joint prediction.Experiments were conducted on real datasets, which were collected in Changsha and Shenzhen.The experimental results show that, compared with the existing prediction model, the root mean square error of the MGC-AN is reduced 11.3%~20.3%, and the average absolute percentage error is reduced 10.8%~36.1%.

Key words: multi-source heterogeneous data, area of interest, graph neural network

CLC Number: 

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