Enriching Activity-Based Models using Smartphone-Based Travel Surveys

Smartphone-based travel surveys have attracted much attention recently, for their potential to improve data quality and response rate. One of the first such survey systems, Future Mobility Sensing (FMS), leverages sensors on smartphones, and machine learning techniques to collect detailed personal travel data. The main purpose of this research is to compare data collected by FMS and traditional methods, and study the implications of using FMS data for travel behavior modeling. Since its initial field test in Singapore, FMS has been used in several large-scale household travel surveys, including one in Tel Aviv, Israel. We present comparative analyses that make use of the rich datasets from Singapore and Tel Aviv, focusing on three main aspects: (1) richness in activity behaviors observed, (2) completeness of travel and activity data, and (3) data accuracy. Results show that FMS has clear advantages over traditional travel surveys: it has higher resolution and better accuracy of times, locations, and paths; FMS represents out-of-work and leisure activities well; and reveals large variability in day-to-day activity pattern, which is inadequately captured in a one-day snapshot in typical traditional surveys. FMS also captures travel and activities that tend to be under-reported in traditional surveys such as multiple stops in a tour and work-based sub-tours. These richer and more complete and accurate data can improve future activity-based modeling.

• Record URL:
  https://doi.org/10.1177/0361198118798475
• Record URL:
  https://doi.org/10.1177/0361198118798475
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/03611981
• Authors:
  • Nahmias-Biran, Bat-hen
  • Han, Yafei
  • Bekhor, Shlomo
  • Zhao, Fang
  • Zegras, Christopher
  • Ben-Akiva, Moshe
• Publication Date: 2018

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References; Tables;
• Pagination: pp 280-291
• Serial:
  • Transportation Research Record: Journal of the Transportation Research Board
  • Volume: 2672
  • Issue Number: 42
  • Publisher: Sage Publications, Incorporated
  • ISSN: 0361-1981
  • EISSN: 2169-4052
  • Serial URL: http://journals.sagepub.com/home/trr

Subject/Index Terms

• TRT Terms: Activity choices; Data collection; Data quality; Smartphones; Travel behavior; Travel surveys
• Identifier Terms: Future Mobility Sensing (Software)
• Geographic Terms: Singapore; Tel Aviv (Israel)
• Subject Areas: Data and Information Technology; Planning and Forecasting; Transportation (General);

Filing Info

• Accession Number: 01664111
• Record Type: Publication
• Report/Paper Numbers: 18-05952
• Files: TRIS, TRB, ATRI
• Created Date: Mar 23 2018 10:32AM