Christoph Dobler

Integration of Activity-Based and Agent-Based Models

This study explored the possibility of integrating an operational activity-based model and a dynamic traffic assignment framework. The Tel Aviv, Israel, activity-based model and parts of the functionality of the MATSim agent-based framework were used in an attempt to draw on the best features of both approaches: the disaggregate demand representation from the activity-based model and the disaggregate supply representation of the agent-based framework. The study used the person–activity schedule produced by the activity-based model directly and thus eliminated the need to aggregate origin–destination matrices. This paper compares results produced by this combination with those of a static assignment of the Tel Aviv model, which showed a good fit at the aggregate level. The purpose of the paper is to advance the fully disaggregate implementation of activity-based models. The paper represents a step toward this general goal.

Within-Day Replanning of Exceptional Events

Typical software used in the simulation of traffic behavior focuses on scenarios describing common situations, such as an ordinary working day without remarkable incidents. To simulate such scenarios, iterative approaches are used. They assume that the people simulated adapt to previous iterations’ results. Such iterative approaches produce meaningful results for various scenarios only when typical, repetitive situations are modeled. However, a scenario may also contain incidents that occur randomly, and thus substantially increase a models complexity. In such scenarios, an iterative approach would produce illogical and even erroneous results. Within-day replanning is an attempt to handle such scenarios. This paper describes problems that arise when an iterative simulation approach is applied to a scenario with exceptional events. The within-day replanning technique is introduced and implemented in the multiagent transport simulation framework, allowing simulated agents to replan the routes between their activities while they are traveling. By doing this, agents can take current traffic conditions into account, an important requirement for scenarios containing unpredictable incidents such as road accidents. The implementation capability is demonstrated by conducting experiments in which capacities of several arterial roads in the city center of Zurich, Switzerland, are reduced as the result of an exceptional event. It is demonstrated that agents affected by those events are able to reduce their travel times if they replan their routes by using within-day replanning.

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

Driver’s Choice and System Outcomes in Congested Traffic Networks

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.

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

Our study is investigating the effects of spatial orientation in the representative scenario of congested traffic in the Zurich metropolitan area. Each driver has planned his itinerary with the help of an off-the-shelf navigation device and sticks to his shortest route. The research question is: How much will the traffic situation improve if part of the drivers use real-time navigation information (such as may be available via smartphone)?

Plug-in hybrid electric vehicles and smart grids: Investigations based on a microsimulation

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.