1,721,040 research outputs found
Factors Influencing Consumer Likelihood of Purchasing a Flexible-Fuel or Hybrid Automobile
Full text linkDeveloping fuels and vehicles that reduce our reliance on fossil fuels has become a priority due to the threat of global climate change and desire for reduced dependence on oil imports. Flexible-fuel vehicles that can run on ethanol/gasoline blends of up to 85% ethanol and hybrid electric vehicles present two such opportunities. While production of both flexible-fuel and hybrid vehicles is increasing, there is still a great deal of uncertainty about how consumers will respond to these products. To address this uncertainty, data was collected through an online survey of automobile owners that asked respondents how likely they were to choose either a flexible-fuel or hybrid vehicle as their next vehicle. A bivariate probit model was used to jointly analyze responses to these two questions. The results show that, while there was some overlap in the factors correlated with perceived likelihood of choosing one of these two types of automobiles, there were also clear differences. These results should benefit policymakers, marketers and academics seeking a better understanding of the respective markets for these vehicles.flexible-fuel vehicles, ethanol, E85, hybrid electric vehicles, Demand and Price Analysis, Environmental Economics and Policy, Resource /Energy Economics and Policy,
Micromobility and shared mobility
No full textShared mobility is a transformative breed of travel alternatives to conventional cars and public transport aiming to maximise the utilisation levels of our finite mobility resources by disengaging their usage from ownership-bound limitations. Shared mobility schemes like car-sharing, ride-sharing, bike-sharing, scooter-sharing (or a mix of them with other modes typically described as mobility hubs) provide vehicle fleets that can be accessed and ridden by their subscribers on an as-needed basis typically for a modest fee directly associated with usage criteria or in a subscription basis. The Holy Grail of these systems is the ‘still in embryonic form’ Mobility-as-a-Service; a digital app-enabled transport eco-system that may be a genuine game-changer for travel behaviour in theory at least. Micromobility on the other hand, refers to low-speed, short-distance transportation provided by lightweight, usually single-person vehicles such as bicycles, e-bikes, scooters, e-scooters, powered self-balancing boards and skateboards. This is a flexible, congestion-reducing, tech-celebrating family of modes that represent a relatively low-cost (but possibly low-economic return) investment in travel behavioural change. Micromobility typically has two complementing elements: the use of battery-powered electricity and its integration with shared mobility schemes. The latter link makes this twin presentation of shared mobility and micromobility the most effective way to describe them both. This chapter elaborates on the diverse dimensions of these two mobility interventions identifying and contextualising their respective potential (and limitations) to support a transition to a more sustainable transport paradigm
Modelling travel behaviour: A choice modelling perspective
No full textChoice models have been applied to explain and predict the transportation choices of individuals for half a century. The advent of big data brings about new opportunities and poses new challenges for forecasting. This chapter discusses the major methodological contributions and the most recent developments in the field of choice modelling in transportation. Advanced choice models have been proposed to accommodate unrestricted substitution patterns between alternatives, unobserved taste variations, serial correlation between repeated observations, and latent constructs as attitudes and perceptions. In recent years, data-driven methods have gained traction to improve the prediction accuracy and to assist the analyst in the model specification. Choice models have also been incorporated into optimization problems to account for the interactions between the choices of individuals and the planning decisions under evaluation. To estimate these advanced models, fast and computationally efficient methods are required.TRANSP-O
Handbook of travel behaviour
No full textThis insightful Handbook offers a comprehensive and diverse understanding of the determinants of travel behaviour, looking at the ways in which it can be better understood, modelled and forecasted. Dimitris Potoglou and Justin Spinney bring together an international range of esteemed academics who explore the origins of the field, research analysis methods, environmental considerations, and social factors. This title contains one or more Open Access chapters
Vehicle-type choice and neighbourhood characteristics: An empirical study of Hamilton, Canada
No full textThe popularity of light-duty trucks has increased with important implications for air quality, traffic accidents and gasoline demand. While previous studies have shed light on vehicle-typechoice at the household level, little work has been done that examines the role of the built environment on these choices. This paper reports empirical findings on the relationship between vehicle-typechoice and neighbourhood characteristics within the Census Metropolitan Area of Hamilton in Canada. The analysis incorporates proximity and urban form measures derived from high-resolution spatial data and geographic information systems technology. Estimates from discrete choice models of households’ latest vehicle-typechoice suggest that preferences for less fuel-efficient vehicles are marginally affected by the diversity of land-uses at the place of residence, after controlling for travel to work attitudes and socio-demographic characteristics of individuals and households
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