Using Multiple Biased Data Sets to Recover Missing Trips with a Behaviorally Informed Model

Xiangyang Guan (), Shuai Huang () and Cynthia Chen ()
Additional contact information
Xiangyang Guan: Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington 98195
Shuai Huang: Department of Industrial and Systems Engineering, University of Washington, Seattle, Washington 91195
Cynthia Chen: Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington 98195

Transportation Science, 2025, vol. 59, issue 4, 743-762

Abstract: Trip generation, a critical first step in travel demand forecasting, requires not only estimating trips from the observed sample data, but also calculating the total number of trips in the population, including both the observed trips and the trips missed from the sample (we call them missing trips in this paper). The latter, how to recover missing trips, is scarcely studied in the academic literature, and the state-of-the-art practice is through the application of sample weights to extrapolate from observed trips to the population total. In recent years, big location-based service (LBS) has become a promising alternative data source (in addition to household travel survey data) in trip generation. Because users self-select into using different mobile services that result in LBS data, selection bias exists in the LBS data, and the kinds of trips excluded or included differ systematically among data sources. This study addresses this issue and develops a behaviorally informed approach to quantify the selection biases and recover missing trips. The key idea is that because biases reflected in different data sources are likely different, the integration of multiple biased data sources will mitigate biases. This is achieved by formulating a capture probability that specifies the probability of capturing a trip in a data set as a function of various behavioral factors (e.g., socio-demographics and area-related factors) and estimating the associated parameters through maximum likelihood or Bayesian methods. This approach is evaluated through experimental studies that test the effects of data and model uncertainty on its ability of recovering missing trips. The model is also applied to two real-world case studies: one using the 2017 National Household Travel Survey data and the other using two LBS data sets. Our results demonstrate the robustness of the model in recovering missing trips, even when the analyst completely mis-specifies the underlying trip generation process and the capture probability functions (for quantifying selection biases). The developed methodology can be scalable to any number of data sets and is applicable to both big and small data sets.

Keywords: self-selection bias; location-based service (LBS) data; small and big data; likelihood-based method; travel demand; Bayesian estimation (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/trsc.2024.0550 (application/pdf)

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:inm:ortrsc:v:59:y:2025:i:4:p:743-762

Access Statistics for this article

More articles in Transportation Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().