The predictive power of the Ajzen, Triandis, and Schwartz models are compared in the context of car use for university routes. Two hundred fifty-four students filled out a questionnaire designed to measure the components of the three models. In the prediction of intention to use a car, results indicated that one variable from the Trinandis model—role beliefs—increased the explanatory power offered by the components of the Ajzen model. In the prediction of self-reported car use, one variable of the Triandis model—car use habit—significantly increased the predictive power of the Ajzen model. The central variable of the Schwartz model—personal norm—exerted no significant effect either on intention or on behavior. The implications of the findings for interventions to reduce the car use of students for university routes are discussed.

Incentives, Morality, Or Habit? Predicting Students’ Car Use for University Routes With the Models of Ajzen, Schwartz, and Triandis:

The private car is fast, comfortable, and convenient. However, worldwide massive car use causes serious environmental problems. Although breakthroughs in clean automobile technology may be under way, reducing car use seems necessary in order to achieve a sustainable transportation system. Several travel demand management (TDM) measures have therefore been proposed and some have been implemented with this aim. The article reviews research addressing the question of how effective, acceptable to the public, and politically feasible such measures are. The conclusion is that noncoercive TDM measures alone are unlikely to be effective in reducing car use. Therefore, coercive TDM measures such as increasing cost for or prohibiting car use may be necessary but are difficult to implement because of public opposition and political infeasibility. If combined with noncoercive TDM measures providing attractive travel alternatives and communicating the benefits of car-use reduction to the public, coercive TDM measures are likely to become more effective, acceptable, and politically feasible.

Travel Demand Management Targeting Reduced Private Car Use: Effectiveness, Public Acceptability and Political Feasibility

This article presents an experimental, theory-driven evaluation of the effectiveness of an intervention that combines a free public transportation ticket and personal schedule information on the subsequent use of public transportation in an urban area. The time point when participants received this intervention is unusual. It was delivered to them shortly after a residential relocation. It is assumed that such a situation increases people’s responsiveness to the intervention. At their new living place, the intervention group shows a strong increase in public transportation use. The intervention effect on the individual choice process is modeled via Ajzen’s theory of planned behavior. Besides a main effect on intention, results indicate interactions between the intervention and the change intention existing prior to the move and higher objective public transport service quality after the move.

Is a Residential Relocation a Good Opportunity to Change People’s Travel Behavior? Results From a Theory-Driven Intervention Study

The effectiveness of soft transport policy measures: A critical assessment and meta-analysis of empirical evidence

Sustainable mobility: from technological innovation to societal learning

Publication of the UK Government's White Paper on. Transport, in July 1998, has raised public awareness of the problems associated with widespread car use. The authors argue that these problems, which largely result from the aggregated choices and behaviour of many individual car users, could be mitigated if the main determinants of car use and the underlying behavioural mechanisms were better understood. This article examines six strategies for changing social behaviour, and some important conditions for reducing car use. Examples are presented of how the insights gained can be applied in practice.

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Gerard Tertoolen

Papers

Sustainable transport policy: The contribution from behavioural scientists