I can’t believe your attitude: a joint estimation of best worst attitudes and electric vehicle choice

The number of conventionally fuelled motor vehicles in use is increasing worldwide despite warnings about finite fossil fuel and the detrimental impacts of burning such fuels. While electric vehicles, the subject of much research, generate far less emissions and offer the potential for power from renewable sources, they are yet to significantly penetrate the market. Tangible barriers such as price and vehicle range still exist, but consumer attitudes also drive behaviour. This paper examines attributes in a framework relatively new to transportation and energy policy; best–worst scaling. This method is widely considered an improvement over traditional methods of eliciting attitudes and beliefs, where respondents select attitudes they find best or worst from a set of attitudinal statements. To avoid potential endogeneity bias, we jointly model attitudes and choice for the first time with best–worst data. It is found that energy crisis, air quality and climate change concerns influence behaviour with respect to vehicle range and that travel behaviour change and forms of government incentives are needed influences on behaviour with respect to vehicle emissions. It is argued that correctly modelling attitudes reduces the error term of the vehicle choice model and provides policy makers with an improved lens for assessing behaviour. Additionally, the methods described within can easily be adapted to other policy scenarios.     

Does the choice model method and/or the data matter?

The opportunity to have seven data sets associated with a stated choice experiment that are very similar in content and design is rare, and provides an opportunity to look in detail at the empirical evidence within and between each data set in the context of a range of discrete choice estimation methods, from multinomial logit to latent class to scale multinomial logit to mixed logit, and the most general model, generalized mixed multinomial logit that accounts for preference and scale heterogeneity. Given the problems associated with data from different countries and time periods, we estimate separate models for each data set, obtaining values of travel time savings that are then updated post estimation to a common dollar for comparative purposes. We also pooled all data sets for a scaled MNL model, treating each data set as a set of three separate utility expressions, but linked to the other data sets through scale heterogeneity. This is not behaviourally appropriate with MNL, latent class or mixed logit. The main question investigated is whether there exists greater synergy in the willingness to pay evidence within model form across data sets compared to across model forms within data sets. The evidence suggests that there is a relatively greater convergence of evidence across the choice models, with the exception of generalized mixed logit, after controlling for data set differences; and there is strong evidence to suggest that differences between data sets do matter.     

Changes in private car ownership associated with car sharing: gauging differences by residential location and car share typology

Transportation - While a large body of literature shows that car share encourages low car ownership, the evidence is rather limited in the context of different types of car share (fleet-based...     

Global versus localised attitudinal responses in discrete choice

Despite the increasing popularity of including attitudinal and perceptual indicators within discrete choice models, debate endures as to whether there exists a causative relationship between attitudes and behaviour, resulting in what has been termed the attitude behaviour gap. In attempt to understand its origins, attitudes have been categorised as global or localised according to whether or not they are related to a specific time, context and action. Under this framework, global attitudes (GA) typically result in poor predictions of specific overt behaviours, whilst attitudes toward behaviour, or localised attitudes (LA), tend to be better predictors of actual outcomes. Also, attitude strength, measured as the accessibility in memory, plays a determinant role in reducing the gap between attitudes and behaviour, with “memory-based” attitudes having a better prediction of overt behaviours than short-term attitudes constructed “on the spot”. The specific focus of the current paper is to examine the temporal stability and the nature of attitudes, being it critical to transportation planning and research considering the controversial link between attitudes and behaviour. An in depth analysis of the different types of attitudes towards satisfaction for train trips reveals that GAs and LAs provide moderately different outcomes. Also, a memory effect has been found, suggesting the connection between attitudes created on the spot and those stored in memory. Further, both GAs and LAs impact significantly on individual preferences. Finally, the omission of LAs, which are rarely employed within transport literature, may potentially lead to inconsistent estimates, as their contribution in explaining the choice will be absorbed by the error term.

     

Estimating the willingness to pay and value of risk reduction for car occupants in the road environment

In recent years there has been a re-focus on the valuation of a statistical life from the ex post or human capital method to an ex ante willingness to pay (WTP) approach. This is in part a recognition that we may have been undervaluing the cost of fatalities and injuries to society associated with crashes, but also a strong belief in the need to focus on establishing the amount, ex ante, that individuals are willing to pay to reduce the risk of exposure to circumstances that might lead to death or degree of injury on the road network. This study has developed a framework in which to identify the degree of preference heterogeneity in willingness to pay by individuals who are drivers or passengers in cars to avoid being killed or injured. A stated choice experiment approach is developed. The empirical setting is a choice of route for a particular trip that a sample of individuals periodically undertakes in Australia. The particular trip is described in enough detail to provide the respondent with a familiar market environment, providing all the relevant background information required for making a decision. Mixed logit models are estimated to obtain the marginal (dis)utilities associated with each influence on the choice amongst the attribute packages offered in the stated choice scenarios. These estimates are used to obtain the WTP distributions for fatality and injury avoidance, which are then aggregated to obtain estimates of the value of risk reduction (VRR), of which the fatality class is also known as the value of a statistical life (VSL).     

E-bikes and urban transportation: emerging issues and unresolved questions

A range of factors, including improvements in battery and motor technology coupled with innovative industrial design, are contributing to the emergence of electric bicycles (E-bikes) with greater range and enhanced performance. This paper examines this emerging vehicle type within the context of sustainable transport. Mobility, safety and environmental impacts are considered. While governments could employ a range of policy instruments to respond to the opportunities and threats presented by these vehicles, the primary focus internationally is on vehicle standards. A review of regulations in different countries highlights little consistency in the parameters used to regulate E-bikes. Unresolved issues are identified to guide future research on this vehicle type.     

金钱的力量 The Power of Money

在某种状态下,大家对赛车最关心的不是车辆改装的怎样,也不是改装技术达到了怎样炉火纯青的地步,而是车队今年究竟有多少预算。预算多意味着你的赛车可以尽情改装,夺得比赛冠军的机会大。     

The implications on willingness to pay of respondents ignoring specific attributes

Individuals processing the information in a stated choice experiment are typically assumed to evaluate each and every attribute offered within and between alternatives, and to choose their most preferred alternative. However, it has always been thought that some attributes are ignored in this process for many reasons, including a coping strategy to handle ones perception of the complexity of the choice task. Nonetheless, analysts typically proceed to estimate discrete choice models as if all attributes have influenced the outcome to some degree. The cognitive processes used to evaluate trade-offs are complex with boundaries often placed on the task to assist the respondent. These boundaries can include prioritising attributes and ignoring specific attributes. In this paper we investigate the implications of bounding the information processing task by attribute elimination through ignoring one or more attributes. Using a sample of car commuters in Sydney we estimate mixed logit models that assume all attributes are candidate contributors, and models that assume certain attributes are ignored, the latter based on supplementary information provided by respondents. We compare the value of travel time savings under the alternative attribute processing regimes. Assuming that all attributes are not ignored and duly processed, leads to estimates of parameters which produce significantly different willingness to pay (WTP) to that obtained when the exclusion rule is invoked.     

A simulation of the simple Mohring model to predict patronage and value of resources consumed for enhanced bus services

Choosing between the competing approaches to providing enhanced bus services requires the ability to predict the effects on patronage and overall benefits to society in terms of Value of Resources Consumed (VRC) of any proposed change. One common approach uses the Mohring model of 1972 and its successors to optimise one or more aspects of the supply of transit services given various level of demand. This paper simulates the operation of a simple circular bus route using the equations of a basic Mohring type model and a Decision Support System (DSS) to endogenise demand under the assumption that there is a fixed demand for travel but commuters can choose between the bus and private vehicle. The selected approach recognises that demand for a particular mode of travel is predicated on the speed of the vehicle in service which is itself determined by the demand for that vehicle (in this model or boarding and alighting time) rather than being determined exogenously. This creates an iterative feedback mechanism whereby changes to the attributes of the bus and car alter mode choice and the speed in service of the bus which, in turn, alters mode choice. The simulation is used to predict changes to patronage and VRC under scenarios regarding changes to the transport network (in terms of fares and running costs, frequency and spacing of bus stops) using parameters from both a Multinomial Logit (MNL) and a Mixed Multinomial Logit (MMNL). The results demonstrate that there are large differences in the size and, in some cases, direction of predicted changes to patronage, VRC and revenue, depending on whether the parameters of the MNL or MMNL model are used.