Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Learning about the unobservable: The role of attitudes, measurement errors, norms and perceptions in user behaviour.
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, System Analysis and Economics. (CTS)ORCID iD: 0000-0003-4512-9054
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Unobservable factors are important to understand user behaviour. Moreover, they contain information to help design services that willsolve today’s challenges. Yet, we have barely scratched the surface ofthe underlying mechanisms ruling user behaviour. For decades, userbehaviour analysis has focused on the capabilities of observable variables,as well as assumptions of regular preferences and rational behaviourto explain user choices; and amalgamated unobservable factorsinto ”black-box” variables. As a response, the field of behaviouraleconomics has produced an array of so-called choice anomalies, wherepeople seem not to be fully rational. Furthermore, as a consequence of the ”digital revolution”, nowwe harvest data on an unprecedented scale -both in quantity andresolution- that is nurturing the golden age of analytics. This explosionof analytics contributes to reveal fascinating patterns of humanbehaviour and shows that when users face difficult choices, predictionsbased only on observable variables result in wider gaps between observedand predicted behaviour, than predictions including observableand unobservable factors. Impacts of the ”digital revolution” are not limited to data and analyticsbut they have filtered through the whole tissue of society. Forinstance, telecommunications allow users to telework, and telework allowsusers to change their travel patterns, which in turn contributes toincrease the overall system complexity. In addition to the new worlddynamics facilitated by Information and Communications Technology,megatrends such as hyper-urbanization or increase demand of personalisedtransport services are imposing pressures on transport networksat a furious pace, which also contributes to increase the complexity ofthe choices needed in order to navigate the networks efficiently. In an effort to alleviate these pressures, new mobility services suchas electric and autonomous vehicles; bicycle and car sharing schemes;mobility as a service; vacuum rail systems or even flying cars are evolving. Each of these services entails a different set of observable variableslike travel time and cost, but also a completely different set of unobservableones such as expectations, normative beliefs or perceptionsthat will impact user behaviour. Hence, a good understanding of theimpact of underlying, unobservable, factors -especially when servicesare radically different from what users know and have experienced inthe past- will help us to predict user behaviour in uncharted scenarios. Unobservable factors are elusive by nature, hence to incorporatethem into our models is an arduous task. Furthermore, there is evidence showing that the importance of these factors might differ across time and space, as user preferences, perceptions, normative beliefs, etc.are influenced by local conditions and cultures. As a consequence, we have witnessed a surge of interest in behavioural economics over the past two decades, due to its ability to increase the explanatory and predictive power of models based on economic theory by adding a more psychologically plausible foundation. This thesis contributes to the existing body of literature in TransportScience in the areas of user perceptions, measurement errors, and the influence of attitudes and social norms in the adoption of new mobility solutions. The work builds on the behavioural economics theoretical framework, underpinned by economic theory, discrete choice analysis -rational behaviour and random utility maximization-, as well as social and cognitive psychology. Methodological contributions include a framework to systematically test differences in user preferences for a set of public transport modes, relating to observed and unobserved attributes; and a framework to assess the magnitude of unobservable measurement errors in the input variables of large-scale travel demand models. On an empirical dimension, findings support the existence of a ”rail factor”, the impact of modelling assumptions on parameter estimates of hybrid choice models, the presence of larger measurement errors in the cost variables than in the time variables, -which in turn translates into diluted parameters that under-estimate the response to pricing interventions-, and that the model with the best fit does not guarantee better parameter estimates. Therefore, I expect this thesis to be of interest not only to modellers, but also to decision makers; and that its findings will contribute to the design of the mobility solutions that users need and desire, but also that will benefit society as a whole.

Place, publisher, year, edition, pages
Stockholm: Kungliga Tekniska högskolan, 2019. , p. 48
Series
TRITA-ABE-DLT ; 1837
Keywords [en]
Attitudes, Measurement errors, Discrete choice analysis, Latent variables, Model misspecification, Normative beliefs, Rail factor, User perceptions, Social norms, Value of travel time savings
National Category
Transport Systems and Logistics Economics
Research subject
Transport Science
Identifiers
URN: urn:nbn:se:kth:diva-240362ISBN: 978-91-7873-022-3 (print)OAI: oai:DiVA.org:kth-240362DiVA, id: diva2:1271394
Public defence
2019-01-31, F3, Lindstedtsvägen 26, Stockholm, 14:00 (English)
Opponent
Supervisors
Note

QC 20181217

Available from: 2018-12-18 Created: 2018-12-17 Last updated: 2018-12-18Bibliographically approved
List of papers
1. Public transport: One mode or several?
Open this publication in new window or tab >>Public transport: One mode or several?
2018 (English)In: Transportation Research Part A: Policy and Practice, ISSN 0965-8564, E-ISSN 1879-2375, Vol. 113, p. 137-156Article in journal (Refereed) Published
Abstract [en]

This paper develops a methodology for testing and implementing differences in preferences for a set of public transport modes, relating to observed and unobserved attributes, in state-of-practice large-scale travel demand models. Results of a case study for commuters in the Stockholm public transport system suggest that there are preference differences among public transport modes. We found that the value of time for train is lower than for bus and metro, and that it is higher for auxiliary modes than for the main mode. Surprisingly, we found no evidence for differences proportional to the in-vehicle time between bus and metro, suggesting that characteristics of in-vehicle time in these two modes are valued equally by the travellers. Nevertheless, unobserved preference for metro is higher than the preference for bus. Regarding the existence of a rail factor, we find evidence to support the hypothesis that rail-based modes have in fact a smaller time parameter (train) or higher alternative specific constant (metro), indicating that rail modes are preferable to bus, ceteris paribus.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Choice behaviour, Demand forecast, Generalised travel cost, Rail factor, Unobserved preferences, Value of travel time
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:kth:diva-227524 (URN)10.1016/j.tra.2018.03.018 (DOI)000438180900010 ()2-s2.0-85045651681 (Scopus ID)
Note

QC 20180516

Available from: 2018-05-16 Created: 2018-05-16 Last updated: 2018-12-17Bibliographically approved
2. Quantifying errors in travel time and cost by latent variables
Open this publication in new window or tab >>Quantifying errors in travel time and cost by latent variables
2018 (English)In: Transportation Research Part B: Methodological, ISSN 0191-2615, E-ISSN 1879-2367, Vol. 117, p. 520-541Article in journal (Refereed) Published
Abstract [en]

Travel time and travel cost are key variables for explaining travel behaviour and deriving the value of time. However, a general problem in transport modelling is that these variables are subject to measurement errors in transport network models. In this paper we show how to assess the magnitude of the measurement errors in travel time and travel cost by latent variables, in a large-scale travel demand model. The case study for Stockholm commuters shows that assuming multiplicative measurement errors for travel time and cost result in a better fit than additive ones, and that parameter estimates of the choice model are impacted by some of the key modelling assumptions. Moreover, our results suggest that measurement errors in our dataset are larger for the travel cost than for the travel time, and that measurement errors are larger in self-reported travel time than software-calculated travel time for car-driver and car-passenger, and of similar magnitude for public transport. Among self-reported travel times, car-passenger has the largest errors, followed by car-driver and public transport, and for the software-calculated times, public transport exhibits larger errors than car. These errors, if not corrected, lead to biases in measures derived from the models, such as elasticities and values of travel time. 

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Error quantification, Hybrid choice models, Latent variables, Measurement error models, RP value of time, Self-reported indicators, Cost benefit analysis, Measurement errors, Transportation routes, Choice model, Latent variable, Value of time, Travel time
National Category
Civil Engineering
Identifiers
urn:nbn:se:kth:diva-236620 (URN)10.1016/j.trb.2018.09.010 (DOI)000455559600026 ()2-s2.0-85054168076 (Scopus ID)
Note

QC 20190205

Available from: 2018-11-19 Created: 2018-11-19 Last updated: 2019-02-05Bibliographically approved
3. Misspecified Hybrid Choice Models: An empirical study of parameter bias and model selection.
Open this publication in new window or tab >>Misspecified Hybrid Choice Models: An empirical study of parameter bias and model selection.
2018 (English)Other (Other academic)
Abstract [en]

Model misspecification is likely to occur when working with real datasets. However, previous studies showing the advantages of hybrid choice models to account for measurement errors have mostly used models where structural and measurement equations match the functions employed in the data generating process, especially when parameter estimate biases were discussed.

 

The aim of this study is to investigate the extent of parameter bias in misspecified hybrid choice models, assess if different modelling assumptions required to make the hybrid choice models operative impact the parameter estimates of the choice model, and evaluate the prediction accuracy of misspecified hybrid choice models in comparison with a simpler, also misspecified, multinomial logit. For these tasks, a mode choice model is estimated on 100 synthetic datasets. The synthetic datasets were designed to mimic the conditions present in real datasets; hence the postulated structural and measurement equations of the hybrid choice models are less flexible than the functions used for the data generating process.

 

Results show that hybrid choice models, even if misspecified, manage to recover better parameter estimates than a multinomial logit. However, hybrid choice models are not unbeatable, as results indicate that misspecified hybrid choice models will still yield biased parameter estimates. Moreover, results suggest that all models, the multinomial logit and the hybrid choice models, successfully isolate the source of model bias, preventing its propagation to other parameter estimates. Furthermore, results indicate that parameter estimates from hybrid choice models are robust to modelling assumptions. Finally, results show that a simple multinomial logit provides higher out-of-sample prediction accuracy than the hybrid choice models, highlighting that better parameter estimates, do not always translate into better model predictions.

Keywords
Hybrid Choice Models (HCM); Integrated Choice and Latent Variable models (ICLV); Mode choice; Latent variables; Model misspecification, Parameter bias, Synthetic dataset, Out-of-sample prediction
National Category
Transport Systems and Logistics Economics
Identifiers
urn:nbn:se:kth:diva-240354 (URN)
Note

QC 20181217

Available from: 2018-12-17 Created: 2018-12-17 Last updated: 2018-12-18Bibliographically approved
4. User attitudes towards a corporate Mobility as a Service
Open this publication in new window or tab >>User attitudes towards a corporate Mobility as a Service
2018 (English)Manuscript (preprint) (Other academic)
Abstract [en]

Mobility as a service (MaaS) envisages enabling a co-operative and interconnected single transport market which provides users with hassle free mobility. Among MaaS postulated benefits, MaaS enthusiasts claim that MaaS solutions could persuade people to give up their car. Conversely, there is a fear that MaaS could in fact induce less sustainable travel, by means of inducing extra demand, and even attract current public transport users towards taxi and car-pool alternatives.

 

In this study we investigate user attitudes and expectations towards a corporate MaaS solution, through a latent class and latent variable model. Results support that there is a trend from car ownership to usership. We also find no evidence that MaaS solutions could produce a shift from public transport users to other less space-efficient shared-mobility solutions such as taxis or car-pool alternatives under our experiment conditions. In connection with user’s preference to share a car journey with strangers, we find the existence of two opposite trends. This finding suggests that there might be appetite for both types of solutions, where users could choose between private or shared journeys by car. Moreover, we find that normative beliefs impact user mobility styles, and that the need and feeling for flexibility is found to be one of the key factors for users to embrace a MaaS solution.

Keywords
Mobility as a Service, MaaS, Travel behaviour, Attitudes, Norms, Latent Class and Latent Variable Model (LCLVM).
National Category
Transport Systems and Logistics Economics
Identifiers
urn:nbn:se:kth:diva-240357 (URN)
Funder
Integrated Transport Research Lab (ITRL)VINNOVA, 2017-01976
Note

QC 20181218

Available from: 2018-12-17 Created: 2018-12-17 Last updated: 2018-12-18Bibliographically approved

Open Access in DiVA

fulltext(600 kB)64 downloads
File information
File name FULLTEXT01.pdfFile size 600 kBChecksum SHA-512
bbc97709163f43e7d1731763595094a5707c68a744a0065f6455c09acf226d8abcb30d729d17139c164d9d4e6d18da5b87b64913c3dc41b1de13131d032872ee
Type fulltextMimetype application/pdf

Authority records BETA

Lorenzo Varela, Juan Manuel

Search in DiVA

By author/editor
Lorenzo Varela, Juan Manuel
By organisation
System Analysis and Economics
Transport Systems and LogisticsEconomics

Search outside of DiVA

GoogleGoogle Scholar
Total: 64 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 551 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf