Boris Jäggi

Modeling Household Fleet Choice as Function of Fuel Price by Using a Multiple Discrete-Continuous Choice Model

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

Efficiency Gains in Coordination in Information Poor Environments

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.

Comparison of Estimates of Travel Time Losses on High-Capacity Roads

This contribution shows that high-efficiency coordination via strategy sorting is possible even within a modest number of rounds and with minimal informational feedback. The results of our study challenge one of the widely established mechanisms to enhance coordination on high-efficiency equilibria, i.e. making them more “attractive” relative to lower efficiency equilibria (Devetag and Ortmann, 2007). In our study we show that there exist conditions under which the opposite is true, that is the more attractive the safer option is, the easier is to coordinate on high efficiency equilibria.

Modeling household fleet choice as a function of fuel price using a multiple discrete-continuous choice model

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 delay-related 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.

Stated response and multiple discrete-continuous choice models: Analyses of residuals

In this Paper, a new model of fleet choice for households is presented that uses the multiple discrete-continuous extreme value (MDCEV) model as a framework. The aim of the model 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 in addition to socioeconomic attributes of households, the choice is also influenced by fuel price. To model a range of fuel prices up to 20 USD/gallon a data base from a sophisticated stated adaption survey about mobility residential choice among approximately 400 Swiss households was used. The model had a choice set of 17 alternatives distinguishing car type and drive train. In the MDCEV model, a household chooses multiple car types and distributes an overall budget of vehicle miles traveled among the chosen alternatives. The model shows that fuel price has a much greater influence on the selection of the car type than on the use (VMT) of the car. In a certain range of fuel prices, households tend to switch from gasoline to diesel cars. The paper also contains an assessment of the residuals of the simulation that shows a reasonable performance of the model.