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Modelling long-distance travel demand by combining mobile phone and survey data
Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-4926-1434
2024 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Forecasts of the demand for long-distance travel are a key component enabling the calculation of social costs and benefits of policy actions such as infrastructure investments. Traditionally, such forecasting models have been based on travel survey data. However, response rates to travel surveys have been in decline for decades, calling into question whether the sample of respondents is really representative of the full population. As such, there is a need to explore alternative data sources. One promising alternative is mobile phone network data, which is collected without the need of active participation from the traveller. However, mobile phone network data in this thesis lacks trip and traveller specific information such as trip purpose, socio-economic information, travel party size and mode. Furthermore, it is difficult to distinguish between bus and car trips even at a later stage of data processing, as the two modes share the same infrastructure. 

The objective of this thesis is to investigate the use of mobile phone network data for long-distance mode choice modelling. More specifically, we investigate the specific aspects of mobile phone network data as a source of mode choice travel information in the first research paper of this thesis, how uncertainties connected to the identification of the used mode matter, and how it can be handled in the model. In the second research paper of this thesis, a full-scale Multinomial Logit mode choice model is implemented and evaluated, including the development of how to handle mobile phone network data-specific challenges in the dataset of this thesis, such as the lack of distinction between bus and car trips and the lack of trip purpose information. Once this full-scale mode choice model based only on mobile phone network data has been evaluated, a method for combining mobile phone network data with survey data is proposed in the third research paper of this thesis, and the joint model is compared to the mobile phone network data model in terms of behavioural credibility. Finally, it is investigated whether machine learning can be useful in modelling mode choices using the two data sources in the fourth research paper of this thesis. 

From the results of the papers included in this thesis, it is clear that it is possible to model mode choice based only on mobile phone network data, but that it is preferable to combine mobile phone network data with survey data, rather than to use any one data source separately. Either Multinomial Logit (MNL) models or Artificial Neural Networks (ANNs) can be used to model mode choices based on the two data sources. However, if ANN is selected for mode choice modelling, it is advisable to formulate the network based on the transport mode choice specific principles developed in the last paper of this thesis.

Abstract [sv]

Prognoser för reseefterfrågan av långväga resor är en nyckelkomponent för att möjliggöra beräkningen av samhällsnytta och samhällskostnader i policyåtgärder såsom infrastrukturinvesteringar. Historiskt har sådana prognosmodeller baserats på resvaneundersökningar, men svars-frekvensen i dessa undersökningar har sjunkit under flera decennier, vilket gör att det inte längre är sannolikt att urvalet av svarande är representativt för hela befolkningen. Därmed har det uppstått ett behov av att undersöka tillämpbarheten av alternativa datakällor för modellering av reseefterfrågan. Ett lovande alternativ är mobilnätsdata, som samlas in utan att den resan-de behöver göra ett aktivt val för att delta. Det möjliggör insamling av stora datamängder, men nackdelen är att specifik information om den resande saknas, eftersom personintegriteten måste skyddas för all data som samlas in utan ett aktivt beslut om deltagande. Data som är relevant ur ett transportmodelleringsperspektiv, men som saknas i mobilnätsdatan är till exempel reseärende, resenärens socio-ekonomi, resesällskapsstorlek och färdmedel. Dessutom är det svårt även i efterbearbetning av data att särskilja färdmedlen bil och buss, eftersom de använder samma infrastruktur.

Målsättningen i den här avhandlingen är att undersöka tillämpbarheten av mobilnätsdata för inrikes långväga färdmedelsvalsmodellering. Mer specifikt så utreds först specifika aspekter av mobilnätsdatan som datakälla till en färdmedelsvalsmodell i avhandlingens första artikel, bl a hur osäkerhet i förbearbetningen av identifieringen av färdmedelsvalet spelar in, och hur denna osäkerhet kan hanteras i modellen. Sedan implementeras och utvärderas en fullskalig logitbaserad färdmedelsvalsmodell baserat enbart på mobilnätsdata i avhandlingens andra artikel. För att möjliggöra detta utvecklas metoder för att hantera bristen på särskiljning mellan bil och buss, och bristen på reseärende i mobilnätsdatan. Efter det utvecklas i avhandlingens tredje artikel en metod för att kombinera mobilnätsdata med enkätdata i en logitbaserad färdmedelsvalsmodell. Den samskattade modellen jämförs sedan med modellen som baseras enbart på mobilnätsdata i termer av beteendemässig rimlighet. Slutligen utreds huruvida alternativa modelleringsmetoder i form av maskininlärning kan vara användbara i den variant av färdmedelsvalsmodell som baseras på båda datakällorna.

Baserat på resultaten i de artiklar som ingår i denna avhandling, står det klart att det är möjligt att modellera färdmedelsval baserat enbart på mobilnätsdata, men också att det är klart fördelaktigt att kombinera de båda datakällorna, snarare än att använda bara den ena. Både multinomial logit och artificiella neurala nätverk (ANN) kan användas för att modellera färdmedelsval, men om ANN används behöver nätverket anpassas enligt ett antal transportområdesspecifika principer som tas fram i den fjärde artikeln i denna avhandling.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2024. , p. 31
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2390
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:liu:diva-204932DOI: 10.3384/9789180756709ISBN: 9789180756693 (print)ISBN: 9789180756709 (electronic)OAI: oai:DiVA.org:liu-204932DiVA, id: diva2:1871595
Public defence
2024-09-13, K3, Kåkenhus, Campus Norrköping, Norrköping, 09:15 (English)
Opponent
Supervisors
Note

Funding: The research in this thesis has mainly been funded by the research projects DEMOPAN and DEMOPAN-2 within the research programme Transportekonomi at The Swedish Transport Ad-ministration.

Available from: 2024-06-17 Created: 2024-06-17 Last updated: 2024-06-17Bibliographically approved
List of papers
1. Long-distance mode choice model estimation using mobile phone network data
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2022 (English)In: Journal of Choice Modelling, E-ISSN 1755-5345, Vol. 42, article id 100337Article in journal (Refereed) Published
Abstract [en]

In this paper we develop two methods for the use of mobile phone data to support the estimation of long-distance mode choice models. Both methods are based on logit formulations in which we define likelihood functions and use maximum likelihood estimation. Mobile phone data consists of information about a sequence of antennae that have detected each phone, so the mode choice is not actually observed. In the first trip-based method, the mode of each trip is inferred by a separate procedure, and the estimation process is then straightforward. However, since it is not always possible to determine the mode choice with certainty (although it is possible in the majority of cases), this method might give biased results. In our second antenna-based method we therefore base the likelihood function on the sequences of antennae that have detected the phones. The estimation aims at finding a parameter vector in the mode choice model that would explain the observed sequences best. The main challenge with the antenna-based method is the need for detailed resolution of the available data. In this paper we show the derivation of the two methods, that they coincide in case of certainty about the chosen mode and discuss the validity of assumptions and their advantages and disadvantages. Furthermore, we apply the first trip-based method to empirical data and compare the results of two different ways of implementing it.

Place, publisher, year, edition, pages
Elsevier, 2022
Keywords
Demand model, Mode choice, Mobile phone network data, Travel behaviour, Long-distance travel
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:liu:diva-184941 (URN)10.1016/j.jocm.2021.100337 (DOI)000819919700002 ()
Available from: 2022-05-13 Created: 2022-05-13 Last updated: 2024-06-17Bibliographically approved

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Andersson, Angelica

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5678910118 of 11
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