A neighborhood-based collaborative filtering algorithm for secondary activity location choice prediction using smart card data

Collaborative filtering is a method of predicting the interests of a single person by collecting preference information from many people. Collaborative filtering algorithms have commonly been used to predict the preference of a consumer for a movie or a song in a recommendation system. This data-driven approach only relies on empirical observations and does not require imposing theory-based prior assumptions about behavior, resulting in a more flexible way to capture preferences and potentially a better prediction. In addition, one of the assumptions underlying travel behavior modeling is that different personal attributes (e.g., socioeconomic status) cause the heterogeneity of travel preferences, which is always difficult to model using big data due to the anonymity. Collaborative filtering seems promising for tackling this issue. This work specifically focuses on the problem of predicting one’s secondary activity location (other than work or living). A tailored collaborative filtering algorithm is applied to the three-month metro smart card data from Shanghai, China. Results show that the collaborative filtering algorithm outperforms the other prediction methods, including an estimated multinomial logit model, which shows the relevance of exploring further such method.

  • Supplemental Notes:
    • This paper was sponsored by TRB committee ADB40 Standing Committee on Transportation Demand Forecasting.
  • Corporate Authors:

    Transportation Research Board

  • Authors:
    • Wang, Yihong
    • Correia, Gonçalo Homem de Alameida
    • van Arem, Bart
    • Timmermans, H J P (Harry)
  • Conference:
  • Date: 2019


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; References;
  • Pagination: 6p

Subject/Index Terms

Filing Info

  • Accession Number: 01697537
  • Record Type: Publication
  • Report/Paper Numbers: 19-04097
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 7 2018 9:30AM