•Daytime charging at electric vehicle charging stations alleviates parking pressure.•Daytime charging at EV charging stations has a negative impact on EV purchase intention.•Free parking for electric ...vehicles offers a positive incentive for potential owners.•Free parking for electric vehicles increases the connection time to charging stations.•Cross-pollinations between EV charging and adaptation policies are relevant for policy design.
Policy makers are looking for effective ways to promote the adoption of electric vehicles (EVs). Among the options is the roll-out and management of charging infrastructure to meet the EV drivers’ refuelling needs. However, policies in this area do not only have a long-term effect on the adoption of EVs among prospective owners, they also have short-term impacts on the usage of public charging infrastructure among current EV owners and vice versa. Presently, studies focusing on both effects simultaneously are lacking, missing out on possible cross-pollination between these areas. This study uniquely combines stated and revealed preference data to estimate the effect of particular policy measures aimed at EV adoption, on the one hand, and charging behaviour, on the other. Using a large dataset (1.7 million charging sessions) related to charging behaviour using public charging infrastructure in the Netherlands we quantify the effects of (i) daytime-parking (to manage parking pressure) and (ii) free parking (to promote purchase of EVs) policies on charging behaviour. To estimate the effects of these particular policies on EV purchase intentions, a stated choice experiment was conducted among potential EV-buyers. Results show that cross-pollinations between EV charging and adaptation policies exist and should be taken into account when designing policies for EV adoption.
•Both general and specific attitudes have limitations in explaining and predicting travel behavior.•General attitudes are exogenous, but are weakly correlated with specific travel behaviors.•Specific ...attitudes are strongly correlated with specific travel behaviors, but are endogenous to the behaviors.•It is very difficult to identify and measure attitudinal variables that satisfy two necessary criteria for causal inference, i.e. empirical association and exogeneity.
Attitudes are increasingly used in travel behavior research to help explain and predict travel behavior. In such studies, empirical correlations between attitudes and behavior are routinely interpreted as causal effects, which paves the way for policy interventions aimed at changing attitudes and thereby, ultimately, behavior. This paper contributes to a recent and growing body of work which points at the shaky foundations underlying this attitude-behavior conceptualization. In contrast to previous work in this direction, we distinguish between general attitudes and specific attitudes and we study their potential and limitations in explaining and predicting travel behavior. We build and empirically confirm a set of hypotheses which argues that neither of these two types of attitudes is capable of providing empirical evidence for a causal effect of attitudes on behavior. General attitudes, which have the advantage of being relatively exogenous to the behavior being studied, only have a weak empirical association with specific travel behaviors. Specific attitudes towards these travel behaviors overcome this problem as they are much more strongly correlated with behavior, but this comes at the cost of a severe loss in exogeneity; in other words, the causal relation from specific behavior to specific attitudes is considerably stronger than the opposite effect. In combination, our findings suggest that it is very difficult, if not impossible, to identify and measure attitudinal variables that satisfy two necessary criteria for causal inference: empirical association and exogeneity. Implications for travel behavior researchers and transport policy makers are likely to be far-reaching.
This study aims to establish whether or not bicycle commuting and cycling for other purposes (e.g. shopping, visiting friends) are related over time. Using previously gathered panel data (the Dutch ...mobility panel) these relationships are revealed by (1) a series of conditional change models and (2) a latent transition model. The conditional change models indicate that, with a lag of 1 year and controlling for a range of background characteristics, bicycle commuting and non-work cycling (in number of weekly trips) have a positive reciprocal influence on each other. The models show that work-related factors, such as the distance to work or whether a person receives a travel allowance, affect not only bicycle commuting but also non-work cycling. The latent transition model indicates that people can be clustered into four groups: non-cyclists, non-work cyclists, all-around cyclists and commuter cyclists. This model shows that people with a consistent propensity to not cycle at all (non-cyclists) or to cycle for both work and non-work purposes (all-around cyclists) are most stable in their travel behavior. Non-work cyclists and commuter cyclists are less stable in travel behavior. The model also shows that all-around cyclists are not (significantly) affected by a change in the distance to work. The article concludes with several directions for future research.
We used microparticles under hypergravity conditions, induced by a centrifuge, in order to measure nonintrusively and spatially resolved the electric field strength as well as the particle charge in ...the collisional rf plasma sheath. The measured electric field strengths demonstrate good agreement with the literature, while the particle charge shows decreasing values towards the electrode. We demonstrate that it is indeed possible to measure these important quantities without changing or disturbing the plasma.
•Relations between attitudes and behaviour were weaker during COVID-19 pandemic.•Separation of between-persons correlations from within-persons effects is relevant.•Within-person effects are weaker ...after between-person correlations are separated.•Policies targeting attitudes to influence travel behaviour are likely less effective.
Attitudes have been used as explanatory variables of travel behaviour for decades, typically under the assumption that there is a causal effect of attitudes on behaviour. However, recent research has shown that the relationship between attitudes and travel behaviour is bi-directional. In this study we use a longitudinal modelling technique on panel data to 1) separate within-person effects from between-person associations and 2) test whether the within-person effects changed during the COVID-19 pandemic. We find that the within-person effects were weaker during the pandemic than they were before the pandemic. In addition, the within-person effects were much smaller than would be expected based on methods that do not separate within-person effects from between-person associations. This means that researchers should be careful when basing policy recommendations on cross-sectional correlations between attitudes and behaviour for two reasons: first, the problem of endogeneity, and second, the highly relevant separation of within-person effects from between-person relations.
Abstract
Policies to increase the amount of time people spend working from home were widely used during the COVID-19 pandemic. Since research suggests that the resulting increase in working from home ...will outlast these policies themselves, policymakers want to understand the relations between working from home and travel behaviour. We apply longitudinal modelling techniques to estimate the relations between working from home and travel behaviour using panel data from the Netherlands Mobility Panel spanning the years 2017 through 2021. This allows us to separate between-persons and within-persons relations and effects and to see whether these effects changed during the pandemic. We find a negative effect of working from home on commute travel time both before and during the pandemic and a positive effect on leisure travel time only before the pandemic. The sizes of these effects remained roughly similar during the pandemic, although the extent to which working from home affected commute travel time increased during the pandemic. The net effect of working from home on travel time is negative, indicating that working from home policies could be used to reduce travel time. The results also show that some of the relationships between working from home and travel behaviour have changed during the pandemic. As a result, policymakers and transport operators should be careful when estimating future travel demand based on extrapolations of relationships found only before or during the pandemic.
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