Big Data Research ›› 2020, Vol. 6 ›› Issue (6): 105-118.doi: 10.11959/j.issn.2096-0271.2020057

• STUDY • Previous Articles    

Research on demand identification for customized bus based on multi-source mobility data

Xi CHEN1,Yinhai WANG2,Zhuang DAI3,Xiaolei MA4,4()   

  1. 1 School of Transportation Science and Engineering,Beihang University,Beijing 100191,China
    2 Department of Civil and Environmental Engineering,University of Washington,Seattle 98195,United States
    3 School of Transportation and Logistics,Southwest Jiaotong University,Chengdu 610031,China
    4 Beijing Advanced Innovation Center for Big Data and Brain Computing,Beihang University,Beijing 100191,China
  • Online:2020-11-15 Published:2020-12-12
  • Supported by:
    The National Natural Science Foundation of China(61773036)

Abstract:

As a new type of public transit services,the demand identification of customized bus (CB) has great practical significance,as well as the basis of route design of CB.Under the context of big data,a methodology framework of CB demand identification based on multi-source data was proposed by mining spatial-temporal characteristics from large scale mobility data.The proposed framework includes several phases,which are the identification of commuters from transit and Internet users,data fusion of travel demands and stop deployment method.This study takes Chengdu city as an example to verify the effectiveness of the proposed methods.The results can provide a theoretical support of CB route design.

Key words: customized bus, multi-source data, data mining, demand identification

CLC Number: 

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