| 1. |
- Lorenzo Varela, Juan Manuel, 1987-, et al.
(författare)
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Capturing generalised cost differences between public transport modes in Stockholm
- 2016
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Decision makers in many European countries seem to prefer rail over bus services, claiming that travellers prefer services operating on tracks, the preference is referred to as a “rail factor”. This assumption is to some extent supported by Eliasson’s (2016) finding that accessibility by metro increases the property prices of apartments in Stockholm more than accessibility by bus. Nevertheless, common large-scale transport models struggle to capture these effects.In this paper we show how state-of-practice large-scale models can be enhanced to capture these nuances by modelling public transport modes as different alternatives. Results suggest that differences among public transport alternatives are strongly linked to unobserved attributes. Surprisingly, we found no statistical evidence that differences are proportional to the in-vehicle time, suggesting that in-vehicle characteristics of public transport modes, such us comfort, are perceived as similar by the users. We also found that the value of time is higher for auxiliary modes than for the main mode, and that the unobserved preference for metro is highest and the preference for light rail lowest. These are empirical findings for the transport system of Stockholm, and whether these findings can be generalised to other cities is an empirical matter. However, the methodology developed in the present paper provides the possibility to investigate these issues further.
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| 2. |
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| 3. |
- Lorenzo Varela, Juan Manuel, 1987-, et al.
(författare)
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Estimating Values of Time on National travel survey data
- 2018
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Konferensbidrag (refereegranskat)abstract
- The Value of Travel Time (VTT) is fundamental in transport economics. Since 1984 (MVA et al., 1984) best practice for VTT estimation has been to use Stated Choice (SC) data. However, there is now plenty of evidence of reference dependence and gain-loss asymmetry in SC data, implying that such data do not reveal long-term stable preferences. This is a serious problem since the value of time is often applied in welfare analyses, where long-term stability of the preferences is a key assumption. A potential reason for the strong reference dependence found in SC data is the emphasis on a short-term reference point often used in SC data to reduce hypothetical bias. In the long-run there is no stable reference point. Also, the use of Stated Choice data always raises the issue of the credibility of hypothetical responses.An alternative to SC data is to use revealed preference (RP) data and a mode choice model to estimate the VTT. Observed behaviour has adapted to the (more stable) travel conditions and should thus be ruled by more long-term preferences. Many countries collect NTS (national travel survey) data and spend considerable resources on making them representative, which is an argument for using them for VTT estimation. However, a key problem in the use of NTS data for VTT estimation is measurement errors in the travel time and travel cost variables. Time and cost in NTS data is either self-reported or derived from a network assignment model.In this paper we estimate the distribution of the VTT whilst controlling for errors in the self-reported and model computed time and cost variables.
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| 4. |
- Lorenzo Varela, Juan Manuel, 1987-
(författare)
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Learning about the unobservable : The role of attitudes, measurement errors, norms and perceptions in user behaviour.
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 6. |
- Lorenzo Varela, Juan Manuel, 1987-
(författare)
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Misspecified Hybrid Choice Models: An empirical study of parameter bias and model selection.
- 2018
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 7. |
- Lorenzo Varela, Juan Manuel, 1987-
(författare)
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Parameter bias in misspecified HCM: An empirical study.
- 2018
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Konferensbidrag (refereegranskat)abstract
- Model misspecification is likely to occur when working with real datasets. However, previous studies showing the advantages of the hybrid choice models have mostly used models where structural and measurement equations match the functions employed in the data generating process, especially when parameter biases were discussed. The aim of this paper is to investigate the extent of parameter bias in misspecified hybrid choice models. For this task, a mode choice model is estimated on synthetic data with efforts focus on mimicking the conditions present in real datasets, where the postulated structural and measurement equations 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 might still yield biased parameter estimates. Moreover, results suggest that hybrid choice models successfully isolate the source of model bias, preventing its propagation to other parameter estimates. Results also show that parameter estimates from hybrid choice models are sensible to modelling assumptions, and that parameter estimates of the utility function are robust, given that errors are modelled.
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| 8. |
- Lorenzo Varela, Juan Manuel, 1987-
(författare)
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Parameter bias in misspecified Hybrid Choice Models : An empirical study.
- 2018
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Ingår i: Transportation Research Procedia. - : Elsevier B.V.. - 2352-1465. ; , s. 99-106
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Konferensbidrag (refereegranskat)abstract
- Model misspecification is likely to occur when working with real datasets. However, previous studies showing the advantages of hybrid choice models have mostly used models where structural and measurement equations match the functions employed in the data generating process, especially when parameter biases were discussed. The aim of this study is to investigate the extent of parameter bias in misspecified hybrid choice models, and assess if different modelling assumptions impact the parameter estimates of the choice model. For this task, a mode choice model is estimated on synthetic data with efforts focus on mimicking the conditions present in real datasets, where the postulated structural and measurement equations are less flexible than the functions used to generate the data. 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 also indicate that misspecified hybrid choice models might still yield biased parameter estimates. Moreover, results suggest that hybrid choice models successfully isolate the source of model bias, preventing its propagation to other parameter estimates. Results also show that parameter estimates from hybrid choice models are sensible to modelling assumptions, and that parameter estimates of the utility function are robust given that errors are modelled.
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| 9. |
- Lorenzo Varela, Juan Manuel, 1987-, et al.
(författare)
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Quantifying errors in travel time and cost by latent variables
- 2018
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Ingår i: Transportation Research Part B. - : Elsevier. - 0191-2615 .- 1879-2367. ; 117, s. 520-541
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 10. |
- Lorenzo Varela, Juan Manuel, 1987-, et al.
(författare)
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User attitudes towards a corporate Mobility as a Service
- 2018
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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