Hierarchical elimination-by-aspects and nested logit models of stated preferences for alternative fuel vehicles

1. INTRODUCTION Since the late 1960s, transport demand analysis has been the context for significant developments in model forms for the representation of discrete choice behaviour. Such developments have adhered almost exclusively to the behavioural paradigm of Random Utility Maximisation (RUM), first proposed by Marschak (1960) and Block and Marschak (1960). A common argument for the allegiance to RUM is that it ensures consistency with the fundamental axioms of microeconomic consumer theory and, it follows, permits interface between the demand model and the concepts of welfare economics (e.g. Koppelman and Wen, 2001). The desire to better represent observed choice, which has driven developments in RUM models, has been somewhat at odds, however, with the frequent assault on the utility maximisation paradigm, and by implication RUM, from a range of literatures. This critique has challenged the empirical validity of the fundamental axioms (e.g. Kahneman and Tversky, 2000; Mclntosh and Ryan, 2002; Saelensmide, 1999) and, more generally, the realism of the notion of instrumental rationality inherent in utility maximisation (e.g. Hargreaves-Heap, 1992; McFadden, 1999; Camerer, 1998). Emanating from these literatures has been an alternative family of so-called non-RUM models, which seek to offer greater realism in the representation of how individuals actually process choice tasks. The workshop on Methodological Developments at the 2000 Conference of the International Association for Travel Behaviour Research concluded: 'Non-RUM models deserve to be evaluated side-by-side with RUM models to determine their practicality, ability to describe behaviour, and usefulness for transportation policy. The research agenda should include tests of these models' (Bolduc and McFadden, 2001 p326). The present paper, together with a companion paper, Batley and Daly (2003), offer a timely contribution to this research priority. Batley and Daly (2003) present a detailed account of the theoretical derivation of RUM, and consider the relationships of two specific RUM forms; nested logit [NL] (Ben-Akiva, 1974; Williams, 1977; Daly and Zachary, 1976; McFadden, 1978) and recursive nested extreme value [RNEV] (Daly, 2001 ; Bierlaire, 2002; Daly and Bierlaire, 2003); to two specific non-RUM forms; elimination-by-aspects [EBA] (Tversky, 1972a, 1972b) and hierarchical EBA [HEBA] (Tversky and Sattath, 1979). In particular, Batley and Daly (2003) establish conditions under which NL and RNEV derive equivalent choice probabilities to HEBA and EBA, respectively. These findings would seem to ameliorate the concern that the application of RUM models to data generated by non-RUM choice processes could introduce significant biases. That aside, substantive issues remain as to how non-RUM models can best be specified so as to yield useful and robust information in both estimation and forecasting contexts, and how their empirical performance compares with RUM models. Such issues are the focus of the present paper, which applies non-RUM models to a real empirical context

Estimating local car ownership models

Many studies in the transport demand literature have shown that income is an important factor in determining how many cars a household owns. When the models used to measure the strength of this relationship are estimated on cross-sectional data, they typically yield one overall value as the estimate. Local circumstances will, however, vary. This paper illustrates the use of the Geographically Weighted Regression technique to estimate the individual strength of this relationship for each of the United Kingdom electoral wards. Use of this type of model enables a wards’ income elasticity to be based on both the local estimate of the strength of this relationship and the current local level of car ownership. How the use of this local elasticity changes future forecasts of the size of the vehicle fleet is illustrated

EU-Rent car rentals specification

EU-Rent is a widely known case study being promoted as a basis for demonstration of product capabilities. However, no in-depth case analysis neither specification has been developed. Therefore, it was considered interesting, useful and even necessary to develop a complete study of the case, which would lead to its whole specification. On the other hand, it was considered a good opportunity to test the application of some proposals, such as alternate mechanisms to define integrity constraints and derivation rules, as well as an alternative approach to model events.Postprint (published version

The UK Transport Carbon Model : An integrated life cycle approach to explore low carbon futures

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Quantifying and Transferring Contextual Information in Object Detection

(c) 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other work

Extended Object Tracking: Introduction, Overview and Applications

This article provides an elaborate overview of current research in extended object tracking. We provide a clear definition of the extended object tracking problem and discuss its delimitation to other types of object tracking. Next, different aspects of extended object modelling are extensively discussed. Subsequently, we give a tutorial introduction to two basic and well used extended object tracking approaches - the random matrix approach and the Kalman filter-based approach for star-convex shapes. The next part treats the tracking of multiple extended objects and elaborates how the large number of feasible association hypotheses can be tackled using both Random Finite Set (RFS) and Non-RFS multi-object trackers. The article concludes with a summary of current applications, where four example applications involving camera, X-band radar, light detection and ranging (lidar), red-green-blue-depth (RGB-D) sensors are highlighted.Comment: 30 pages, 19 figure

Taxation Futures for Sustainable Mobility: final report to the ESRC

The existing transport tax and charging regime has stimulated limited behavioural change and has been politically problematic (as demonstrated by the September 2000 fuel duty protests). This project synthesised a range of research that has explored ways in which road user charging could replace the present regime based on taxing fuels and car ownership. In 2002, when this project was proposed, this was a fringe transport policy issue. Throughout 2003 the subject achieved a sudden prominence, with a government working party being established to explore the possibility of long-term area-wide road user charging. A tax regime change towards a car road user charge for cars has occurred, or is being considered, in societies as contrasting as Oregon State in the USA, the Netherlands, Switzerland and the UK, reflecting a range of policy considerations. For the UK, these include: the ongoing failure of transport policy measures to achieve adequate cuts in congestion and emissions; the success of the London Congestion Charge; the rise in the cost of transport policy interventions; the reduction in Treasury income of eco-reforms to the current tax regime; and the difficulties of, and equity issues relating to, taxing fuel in a future multi-fuel transport sector. The project developed tax change scenarios in conjunction with the project's user group (including policymakers, NGOs and researchers). Five scenarios were modelled using an adaptation of the Dutch Mobility Explorer program. An 'opt-in' transitional policy mechanism involved replacing VED with a small flat-rate kilometre charge for cars of 0.77 p/km. The model suggested it would have little policy impact, but could be used to familiarise car drivers with the concept of a distance charge. A fiscally neutral scenario involved the replacement of VED and Fuel Duty with a banded kilometre charge for cars of between 2.3 and 8.5 p/km (varied by the environmental performance of the vehicle type). This induced little behaviour change, reducing car driver mobility by only 4%. A further scenario, restored the tax revenues lost from post-2000 tax changes, generating an additional £3 billion or £6b per annum. These reduced car driver mobility by 9% - 14%, and total CO2 emissions were predicted to drop by 6% - 9% by 2015, compared to the base scenario. The type of change involved in the revenue-raising scenarios is significant. There would be only a small increase in the use of public transport, with the predominant response being the better utilisation of cars with higher occupancy and more linking of trips to cut distances driven. The project results suggest that road user charging may deliver more revenue stability than fuel taxation. However, clarity is needed over the policy goals – congestion reduction, emission reduction, revenue stability – for a national road user charge, because the goals are not necessarily complementary. It should also be emphasised that a change of tax regime would not remove the need the hard political decisions in this area

Modelling transport energy demand : a socio-technical approach

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Modelling source- and target-language syntactic Information as conditional context in interactive neural machine translation

In interactive machine translation (MT), human translators correct errors in auto- matic translations in collaboration with the MT systems, which is seen as an effective way to improve the productivity gain in translation. In this study, we model source- language syntactic constituency parse and target-language syntactic descriptions in the form of supertags as conditional con- text for interactive prediction in neural MT (NMT). We found that the supertags significantly improve productivity gain in translation in interactive-predictive NMT (INMT), while syntactic parsing somewhat found to be effective in reducing human efforts in translation. Furthermore, when we model this source- and target-language syntactic information together as the con- ditional context, both types complement each other and our fully syntax-informed INMT model shows statistically significant reduction in human efforts for a French– to–English translation task in a reference- simulated setting, achieving 4.30 points absolute (corresponding to 9.18% relative) improvement in terms of word prediction accuracy (WPA) and 4.84 points absolute (corresponding to 9.01% relative) reduc- tion in terms of word stroke ratio (WSR) over the baseline

A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars