Household consumption, apart from governmental consumption, is the main driver of worldwide economy. Attached to each household purchase are economic activities along the preceding supply chain, with the associated resource use and emissions. A method to capture and assess all these resource uses and emissions is life cycle assessment. We developed a model for the life cycle assessment of housing and land-based mobility (excluding air travel) consumption of individual households a small village in Switzerland. Statistical census and dwelling register data are the foundations of the model. In a case study performed on a midsized community, we found a median value of greenhouse gas emissions of 3.12 t CO2 equiv and a mean value of 4.30 t CO2 equiv per capita and year for housing and mobility. Twenty-one percent of the households in the investigated region were responsible for 50% of the total greenhouse gas emissions, meaning that if their emissions could be halved the total emissions of the community would be reduced by 25%. Furthermore, a cluster analysis revealed that driving factors for large environmental footprints are demands of large living area heated by fossil energy carriers, as well as large demands of motorized private transportation.

No abstract

In sophisticated transport models choice modeling is used to capture a wide range of behaviors, such as mode choice, fleet choice or route choice. A newly developed approach to improve realism is the multiple discrete-continuous extreme value (MDCEV) model, which allows to model the allocation of continuous amounts of a consumption good. Before using this models in overall frameworks, knowledge about the accuracy of the forecasting procedure is important. In this paper a MDCEV model of fleet choice based on data collected in a Stated Adaptation survey is presented. A forecast of the model predicting annual mileage of households to 17 different car types was made and the results were compared to the actual data calculating the residuals. The residual analysis shows that the model performs significantly better than a totally random model, but the share of wrongly allocated mileage, 70% of total, remains high. However an assessment of the result is difficult with only one model. The differences between two sub-models, one without public transport, another including it, regarding the distribution of the residuals indicate that the model specification has a big influence on its performance. Therefore, following work forecasting additional MDCEV models will be necessary to have a base for comparison. We compare two further MDCEV models to obtain a fuller understanding of their performance.

In this paper a new model of fleet choice for households uses the multiple discrete–continuous extreme value (MDCEV) model as a framework. The aim is to establish a model to allocate car types to activity-based microscopic agent-based transport simulations. What is new in the presented model is that choice is influenced by fuel price in addition to socioeconomic attributes of households. To model a range of fuel prices up to US$20/gal, a database from a sophisticated stated adaption survey about residential mobility choice in approximately 400 Swiss households was used. The model had a choice set of 17 alternatives distinguishing car type and drivetrain. In the MDCEV model, a household chooses multiple car types and distributes an overall budget of vehicle miles traveled to chosen alternatives. The model shows that fuel price has a much greater influence on the selection of the car type than on the use (vehicle miles traveled) of a car. In a certain range of fuel prices, households tend to switch from gasoline to diesel cars. When fuel prices become too high, alternative fuel technologies are considered.

No abstract

No abstract

This paper compares two approaches to estimating travel time losses on freeways that are based on a whole-year analysis. Both approaches are intended to improve the design process for freeways by accounting for the annual traffic demand profile as well as short time variance of demand and capacity. One approach estimates breakdown probability and delayrelated time losses by using a probabilistic model. The other approach uses Monte Carlo simulation to estimate travel time losses at freeway bottlenecks on the basis of a queuing model with stochastic demand and capacity. Both approaches are based on annual hourly demand profiles. The approaches are compared for demand profiles and deliver similar but not equal results. Travel time losses for both approaches increase exponentially with traffic volume, with the simulation-based approach consistently generating higher time losses for higher traffic volumes. Both approaches are a substantial improvement on current design procedures, are easy to apply, and are recommended for adoption in traffic analysis guidelines.

No abstract