The transport sector contributes nearly 30% to the total worldwide energy consumption. Moreover, transport energy consumption has been growing steadily, and in many countries rapidly, over the last few decades. For instance, energy consumption by the transport sector in EU member states has increased by about 20% between 1990 and 2000, with most of this growth attributed to road transport. The increase in energy consumption by the transport sector is an issue not only from the socio-economic perspective of resource depletion, but also from the environmental perspective of air quality and climate change. It is, therefore, very important for nations to implement energy-sensitive policies. A review of the literature in transport-energy use indicates a distinct lack of methodology for effectively assessing behavioural responses to such energy-sensitive policies. While there have been several studies that estimate and/or project the levels of energy consumption by the transport sector, only a handful of studies have examined the factors impacting transport energy consumption. Moreover, very few studies have attempted to relate transport energy demand to individual activity-travel patterns. In this paper, we will present a detailed conceptual and modelling framework of transport energy use by individuals. The proposed framework is based on the activity-based modelling paradigm and will relate the activity-travel pattern of individuals to transport energy use. Such a model, in the true spirit of an activity-based model, can capture varied behavioural responses such as trade-offs between in-home and out-of-home activities, increased trip chaining, and tele-commuting. The implementation of such an activity-based energy-use modelling system has greater data requirements than typical activity-based travel demand modelling systems. For instance, the synthetic population required for forecasting with the proposed modelling system must include energy information in the form of specific vehicle holdings, residence types and their energy footprints. These data requirements will be discussed in detail in the paper. Finally, as a starting point in the development of the proposed energy-use modelling system, we will estimate a model of in-home versus out-of-home activity participation. This model will consider the effects of policies such as congestion pricing and increased fuel prices on the time spent in in-home and out-of-home activities, and generate an estimate of the corresponding change in energy consumption taking into account the differences in energy use patterns across in-home and out-of-home activities. We are currently examining the value of various datasets in undertaking this estimation, including the Mobidrive data, the Bay Area Travel Survey data and the London Area Transport Survey data. For the covering abstract see ITRD E137145.
Abstract