The objective of this research is to study the extent to which information on pollution and individual stress has on\ud the choice to shift from private car to Park and Ride. A Stated Preference experiment was built where the reduction\ud of CO2 and stress are attributes of the experimental design. Results showed that the utility to Park and Ride increases\ud with the level of awareness, 2) the more individuals consider receiving information about stress useful, the more\ud they tend to behave sustainably, 3) aspects associated with stress appear to have a greater influence on travel choice\ud than environmental aspects

The need to reduce private vehicle use has led to the development of soft measures aimed at re-educating car users through information processes that raise their awareness regarding the benefits of environmentally friendly modes, encouraging them to voluntarily change their mode choice behaviour (level-of-service characteristics being equal). It has been observed, though not scientifically demonstrated, that these measures can produce changes, being the result of mindful decisions. However, in some cases, as demonstrated by numerous environmental psychology studies such measures are not sufficient to overcome the effect of cognitive dissonance, one of the main factors hindering change. In fact it is not unusual to find discrepancies between attitudes and behaviour in travel behaviour research.The objective of the present work is to understand the relationship between awareness, attitude and behaviour in the context of mode choice and to measure the effect of awareness after the implementation of a soft measure after controlling for individual environmental attitudes. Using a dataset gathered in two weeks, before and after individuals are informed of the benefits of using park and ride (P&R) instead of their car, we estimated a hybrid mode choice model

This paper proposes an activity-based methodology for representing the allocation of time to discretionary activities during their programming and scheduling, based on the premise that both phases are to be considered contextually and two aspects of the same decision process. The aim of this work is to extend the treatment of utility maximization associated with carrying out two activities to J activities, so as to be able to segregate the time spent traveling from the total amount of time dedicated to out-of-home activities. The global structure of the model takes the form of a nested Tobit, particularly suited for reproducing a sequence of coupled choices. The first choice concerns dividing up overall discretionary time between activities inside and outside the home, then the second choice, subordinate to the first, involves rebudgeting the time between in- and out-of-home activities and trips. Thus the proposed model enables us to analyze the effects that each explicative variable exerts on trips segregated from activities outside the home and, last, during demand forecasting, the direct consequences of allocating discretionary time to trips following changes to an individual’s time budget. A database created from a large-scale time-use survey (ISTAT 1988–1991) has been used for calibrating the model coefficients.

The objective of this work is to contribute to the debate on sustainable policies aimed at reducing personal carbon emissions from the transport sector. The proposed research describes an experiment extending the cap-and-trade system, employed in manufacturing-based emission curb programs, to Voluntary Travel Behavioral Change (VTBC) program. In particular, a VTBC program is proposed that relies on opportune changes in individual activity-travel patterns after observing actual behavior recorded using an innovative device. In this regard, the methodology developed includes: (1) the design of a new behavioral strategy called "Cap and Save" and (2) the implementation of a new device for daily individual activity travel patterns collection called "Activity Locator". The two aspects are closely interrelated, since behavioral strategies are usually difficult to evaluate; indeed, data regarding individual behavior before and after policy intervention are rarely collected.</p...