714 research outputs found

    What have we learned about long term structural change brought about by COVID-19 and working from home?

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    March 2020 will forever be etched in our minds as the beginning of what has become the most concerning health pandemic faced by all generations of the living population. Almost two-and-three quarter years on, we are starting to see a number of signs for what the future might evolve into through structural change brought about by many events, and no more so than the burgeoning growth in working from home (WFH). No longer associated with negative stigma, working from home, or remote working more generally, has become almost folklore with all elements of society slowly recognising that it is to some extent here to stay, and we should start rethinking how this non-marginal change in the way we live, and work will be used to restructure the fabric of society. In this paper, we draw on the research undertaken as part of an ongoing project on WFH and its relationship to travel and work, since March 2020 to summarise the main evidence that we use to speculate on what we think are likely to be the big changes in the land transport sector that would not have been considered, at least to the same extent, pre-COVID-19

    Modelling gender perception of quality in interurban bus services

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    This paper models how women and men perceive the quality of interurban bus services and proposes a new methodology for detecting the highest priority service variables to act on. Service quality perception was modelled using both ordered logit and ordered probit models using data from revealed preference surveys. The methodology for detecting different priority levels uses the graphic representation of the relationships between influence in the model and average evaluation by users. The modification of certain variables increases the knowledge of how woman evaluate quality in bus services to help promote the use of interurban public transport. Statistical analysis of the data provides some conclusions such as: the proportion of users increases as age decreases for both men and women; and women seem to make shorter and more frequent trips than men. The best model for this data set was ordered logit. As expected, the most relevant variable is the relationship between quality and price. Other important variables are the condition of the bus and the frequency of service

    What have we learned about long term structural change brought about by COVID-19 and working from home?

    Get PDF
    March 2020 will forever be etched in our minds as the beginning of what has become the most concerning health pandemic faced by all generations of the living population. Almost two-and-three quarter years on, we are starting to see a number of signs for what the future might evolve into through structural change brought about by many events, and no more so than the burgeoning growth in working from home (WFH). No longer associated with negative stigma, working from home, or remote working more generally, has become almost folklore with all elements of society slowly recognising that it is to some extent here to stay, and we should start rethinking how this non-marginal change in the way we live, and work will be used to restructure the fabric of society. In this paper, we draw on the research undertaken as part of an ongoing project on WFH and its relationship to travel and work, since March 2020 to summarise the main evidence that we use to speculate on what we think are likely to be the big changes in the land transport sector that would not have been considered, at least to the same extent, pre-COVID-19

    Making use of respondent reported processing information to understand attribute importance: a latent variable scaling approach

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    In recent years we have seen an explosion of research seeking to understand the role that rules and heuristics might play in improving the predictive capability of discrete choice models, as well as delivering willingness to pay estimates for specific attributes that may (and often do) differ significantly from estimates based on a model specification that assumes all attributes are relevant. This paper adds to that literature in one important way‚ÄĒit explicitly recognises the endogeneity issues raised by typical attribute non-attendance treatments and conditions attribute parameters on underlying unobserved attribute importance ratings. We develop a hybrid model system involving attribute processing and outcome choice models in which latent variables are introduced as explanatory variables in both parts of the model, explaining the answers to attribute processing questions and explaining heterogeneity in marginal sensitivities in the choice model. The resulting empirical model explains how lower latent attribute importance leads to a higher probability of indicating that an attribute was ignored or that it was ranked as less important, as well as increasing the probability of a reduced value for the associated marginal utility coefficient in the choice model. The model does so by treating the answers to information processing questions as dependent rather than explanatory variables, hence avoiding potential risk of endogeneity bias and measurement error

    Practical solutions for sampling alternatives in large-scale models

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    Many large-scale real-world transport applications have choice sets that are so large as to make model estimation and application computationally impractical. The ability to estimate models on subsets of the alternatives is thus of great appeal, and correction approaches have existed since the late 1970s for the simple multinomial logit (MNL) model. However, many of these models in practice rely on nested logit specifications, for example, in the context of the joint choice of mode and destination. Recent research has put forward solutions for such generalized extreme value (GEV) structures, but these structures remain difficult to apply in practice. This paper puts forward a simplification of the GEV method for use in computationally efficient implementations of nested logit. The good performance of this approach is illustrated with simulated data, and additional insights into sampling error are also provided with different sampling strategies for MNL

    Delivering Value for Money to Government through Efficient and Effective Public Transit Service Continuity: Some Thoughts

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    This paper documents some thoughts on the reform agenda in public transit that is occurring throughout the world. The specific focus is on a growing commitment to competitive regulation through competitive tendering, and the efforts by a few governments (notably in Australia) to take control of the tangible assets used by private operators as a mechanism to exercise the opportunity, if so taken, to put services out to competitive tender. We review the theoretical arguments and the empirical evidence on contracting regimes and asset ownership, and the role that government and operator might play in a setting in which building trusting and collaborative partnerships has merit in delivering services that are in the main funded from the public purse

    Editorial: Thredbo 17 conference

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    This volume of Research in Transportation Economics collects the workshop reports as well as the best papers presented at the 17th International Conference on Competition and Ownership in Land Passenger Transport (the Thredbo Series) held in Sydney, Australia, in 2022 (after a brief hiatus due to the COVID-19 pandemic and with plans to go to Japan put on hold). The 17th conference in the series followed in the footsteps of previous conferences by bringing together academics, government officials, consultants, policymakers, politicians, students, and public transport operators to review trends and challenges in ownership and competition issues in public transport. It was attended by 125 delegates from 25 countries, with 78 papers presented. The conference included seven workshops. A summary of the history of the conference series, including the themes covered, can be seen in Table 1 at the end of this editorial

    Paying for parking : improving stated-preference surveys

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    This article describes an experiment which introduced random ranges into the variables used for the design of a stated preference survey and its effects on willingness to pay for parking. User behaviour at the time of parking was modelled to determine their willingness to pay in order to get to their final destination more quickly. Calculating willingness to pay is fundamental during the social and economic assessment of projects. It is important to correctly model how car parks and their users interact in order to get values which represent reality as closely as possible. Willingness to pay is calculated using a stated preference survey and by calibrating multinomial logit models, taking variable tastes into account. It is shown that a value with a low variability can be obtained for willingness to pay by correctly establishing the context of the choice and randomly changing the variables around an average value
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