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Public transport: One mode or several?
KTH.
KTH. VTI Swedish National Road and Transport Research Institute, Sweden.ORCID iD: 0000-0001-9235-0232
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. Vol. 113, p. 137-156
Keywords [en]
Choice behaviour, Demand forecast, Generalised travel cost, Rail factor, Unobserved preferences, Value of travel time
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:kth:diva-227524DOI: 10.1016/j.tra.2018.03.018ISI: 000438180900010Scopus ID: 2-s2.0-85045651681OAI: oai:DiVA.org:kth-227524DiVA, id: diva2:1206287
Note

QC 20180516

Available from: 2018-05-16 Created: 2018-05-16 Last updated: 2018-12-17Bibliographically 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

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Börjesson, Maria

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