Scandinavian Working Papers in Economics

Working Papers,
Swedish National Road & Transport Research Institute (VTI)

No 2024:1: Long-distance mode choice estimation on joint travel survey mand mobile phone network data

Angelica Andersson (), Ida Kristoffersson (), Andrew Daly and Maria Börjesson ()
Additional contact information
Angelica Andersson: Swedish National Road and Transport Research Institute (VTI); ITN, Linköping University, Sweden, Postal: VTI, Dept. of Transport Economics, P.O. Box 55685, SE-102 15 Stockholm, Sweden
Ida Kristoffersson: Swedish National Road and Transport Research Institute (VTI), Postal: VTI, Dept. of Transport Economics, P.O. Box 55685, SE-102 15 Stockholm, Sweden
Andrew Daly: ITS, University of Leeds, United Kingdom
Maria Börjesson: Swedish National Road and Transport Research Institute (VTI); IEI, Linköping University, Sweden, Postal: VTI, Dept. of Transport Economics, P.O. Box 55685, SE-102 15 Stockholm, Sweden

Abstract: The accuracy of a transport demand model’s predictions is inherently limited by the quality of the underlying data. This issue has been highlighted by the decline in response rates for transport surveys, which have traditionally served as the primary data source for estimating transport demand models. At the same time, mobile phone network data, not requiring active participation from subjects, have become increasingly available. However, some key trip and traveller characteristics enhancing the prediction power of the estimated models are not collected in mobile phone network data. In this paper we therefore investigate what can be gained from combining mobile phone network data with travel survey data, using the strengths of each data source, to estimate long-distance mode choice models. We propose and estimate a set of mode choice demand models on joint mobile phone network data and travel survey data. We show that combining the two data sources produces more credible estimates than models estimated on each data source separately. The travel survey should preferably include the variables: travel party size, cars per household licence, licence holding, in addition to origin, destination, mode, trip purpose, age, and gender of the respondent.

Keywords: Data combination; Discrete choice modelling; Latent class model; Longdistance mode choice; Mobile phone network data; Transport elasticities; Transport planning; Planning practice

JEL-codes: R41; R42

Language: English

25 pages, February 22, 2024

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