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
Quantifying errors in travel time and cost by latent variables
KTH, School of Architecture and the Built Environment (ABE), Centres, Centre for Transport Studies, CTS.ORCID iD: 0000-0003-4512-9054
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. Vol. 117, p. 520-541
Keywords [en]
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: urn:nbn:se:kth:diva-236620DOI: 10.1016/j.trb.2018.09.010ISI: 000455559600026Scopus ID: 2-s2.0-85054168076OAI: oai:DiVA.org:kth-236620DiVA, id: diva2:1263979
Note

QC 20190205

Available from: 2018-11-19 Created: 2018-11-19 Last updated: 2019-02-05Bibliographically approved
In thesis
1. Learning about the unobservable: The role of attitudes, measurement errors, norms and perceptions in user behaviour.
Open this publication in new window or tab >>Learning about the unobservable: The role of attitudes, measurement errors, norms and perceptions in user behaviour.
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
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:nbn:se:kth:diva-240362 (URN)978-91-7873-022-3 (ISBN)
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

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Lorenzo Varela, Juan Manuel

Search in DiVA

By author/editor
Lorenzo Varela, Juan Manuel
By organisation
Centre for Transport Studies, CTS
In the same journal
Transportation Research Part B: Methodological
Civil Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 14 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