Insights on data quality from a large-scale application of smartphone-based travel survey technology in the Phoenix metropolitan area, Arizona, USA

Shuyao Hong, Fang Zhao, Vladimir Livshits, Shari Gershenfeld, Jorge Santos and Moshe Ben-Akiva

Transportation Research Part A: Policy and Practice, 2021, vol. 154, issue C, 413-429

Abstract: Collecting accurate travel data is vital for transportation planning purposes. Regional travel demand forecasts as well as transportation system analyses depend on datasets that provide origins and destinations of travel for various modes, purposes of travel, socio-economic characteristics of the system users, and other attributes critical for understanding travel demand. GPS-based household travel surveys emerged as a state-of-the-practice method to collect travel data with increased accuracy and detail. The Maricopa Association of Governments conducted a survey utilizing Future Mobility Sensing (FMS) technology. One hundred percent of the sample was collected with the FMS technology platform that combines mobile sensing through a smartphone app with machine learning and a user interface. The technology enables detailed, multi-day, multimodal, user-verified travel and activity behavior data to be obtained with a reduced burden on participants. The data collected through the survey was analyzed together with a comparable dataset obtained through traditional recall-based collection methods during the same time period. The broad conclusions are that the 100% GPS-based surveys with the FMS technology platform provide greater accuracy, detail and completeness of data, as well as greater flexibility than traditional data collection approaches that rely on participant recall. Emphasis was made on comparative analyses between traditionally collected data and the GPS survey with the FMS technology. The paper systematically identifies and explains differences and provides original analyses that can inform future decision making relevant to similar data collection exercises. The method is particularly applicable for monitoring mobility in the ongoing conditions of rapidly changing travel behavior, especially due to the COVID-19 pandemic.

Keywords: Household travel survey; GPS-based surveys; Sensor-based data collection; Future mobility sensing; Activity-based models; Machine learning; smartphone-based survey (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0965856421002482
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:transa:v:154:y:2021:i:c:p:413-429

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.tra.2021.10.002

Access Statistics for this article

Transportation Research Part A: Policy and Practice is currently edited by John (J.M.) Rose

More articles in Transportation Research Part A: Policy and Practice from Elsevier
Bibliographic data for series maintained by Catherine Liu ().