Comparative Analysis of Big and Small (Survey) Data for Deriving Human Mobility Patterns

The next-generation household travel surveys, the core data generation mechanism for supporting both short- and long-term transportation planning applications, are poised to be transformed. It is now increasingly recognized that passively-solicited big data, or large amount of data generated through various types of subscription services, will play an important role in this transformation. Passively-solicited data in its various forms (e.g., mobile sightings, app-based data) not only differ substantially from the household travel survey data, but also among themselves. The authors argue that the very first step for the passively-solicited data to be integrated into the next-generation household travel surveys is to understand their differences. This paper proposes a three-order analysis framework to analyze these differences. Two case studies each involving a big dataset and a corresponding survey dataset are analyzed to demonstrate their respective properties. The analysis results confirm many distinct properties of such big data as compared between themselves and to the survey data.

• Supplemental Notes:
  • This paper was sponsored by TRB committee ABJ40 Standing Committee on Travel Survey Methods.
• Corporate Authors:

  Transportation Research Board

  ,    
• Authors:
  • Wang, Jingxing
  • Wang, Feilong
  • Ban, Xuegang J
  • Chen, Cynthia
• Conference:
  • Transportation Research Board 98th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2019-1-13 to 2019-1-17
• Date: 2019

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References; Tables;
• Pagination: 15p

Subject/Index Terms

• TRT Terms: Data analysis; Data collection; Mobile applications; Mobility; Smartphones
• Identifier Terms: National Household Travel Survey
• Subject Areas: Data and Information Technology; Society; Transportation (General);

Filing Info

• Accession Number: 01697524
• Record Type: Publication
• Report/Paper Numbers: 19-04203
• Files: TRIS, TRB, ATRI
• Created Date: Mar 1 2019 3:51PM