Nicolai Mallig

A relational database for bibliometric analysis

In this article a relational database schema for a bibliometric database is developed. After the introduction explaining the motivation to use relational databases in bibliome-trics, an overview of the related literature is given. A review of typical bibliometric ques-tions serves as an informal requirement analysis. The database schema is developed as an entity-relationship diagram using the structural information typically found in scientific articles. Several SQL queries for the tasks presented in the requirement analysis show the usefulness of the developed database schema.

mobiTopp – A Modular Agent-based Travel Demand Modelling Framework

Abstract mobiTopp is an agent-based travel demand modelling framework designed in a modular fashion, so that exchange of individual modules is easy. This offers the possibility to start with quite simple models and implement the system in practice while at the same time providing the opportunity to develop more sophisticated models for research that can eventually be transferred into practice. Also, practical experience with the system can drive the need for further research. So the system helps bridging the gap between research and practice. The paper presents a detailed overview of mobiTopps structure and its modules. The practical applicability for large-scale simulations is demonstrated by an example of a study involving about 2.5 million agents.

Modeling Car Passenger Trips in mobiTopp

Abstract The transport mode car passenger accounts for a substantial share of the modal split. Travel demand models, however, often contain only a simplistic representation of this mode. Modeling car passenger trips realistically is complex, since the availability of this mode option depends on the presence of a car driver. Indeed an agent-based travel demand model is a solid foundation for a more realistic implementation of the car passenger mode. The paper describes the former implementation of the car passenger mode in the agent-based travel demand model mobiTopp and the weaknesses of this approach. It discusses different situations where car passenger trips occur, namely joint activities and ridesharing, and outlines a simple but flexible concept for the implementation of ridesharing. It describes the changes to mobiTopp that where necessary to implement the ridesharing concept and the experiences made with this new car passenger model.

Electric Vehicles with Range Extenders: Evaluating the Contribution to the Sustainable Development of Metropolitan Regions

AbstractElectric vehicles play a key role in strategic development plans of urban regions in Europe because they are seen as a promising technology to promote environmental quality, livability, and...

Large-Scale Application of a Combined Destination and Mode Choice Model Estimated with Mixed Stated and Revealed Preference Data

The diffusion of new modes of transportation, such as carsharing and electric vehicles, makes it necessary to consider them along with traditional modes in travel demand modeling. However, there are two main challenges for transportation modelers. First, the new modes’ low share of usage leads to a lack of reliable revealed preference data for model estimation. Stated preference survey data are a promising and well-established approach to close this gap. Second, the state-of-the-art model approaches are sometimes stretched to their limits in large-scale applications. This research developed a combined destination and mode choice model to consider these new modes in the agent-based travel demand model mobiTopp. Mixed revealed and stated preference data were used, and new modes (carsharing, bikesharing, and electric bicycles) were added to the mode choice set. This paper presents both challenges of the modeling process, mainly caused by large-scale application, and the results of the new combined model, which are as good as those of the former sequential model although it also takes the new modes into consideration.

Modelling the Weekly Electricity Demand Caused by Electric Cars

Abstract As transport is one of the big sources of carbon dioxide emissions, it is natural to seek for solutions reducing the carbon dioxide emissions in transport as well. Replacing cars powered by a combustion engine by battery electric vehicles may be one measure to achieve this goal, at least as long as the electricity consumed by these cars is produced carbon neutral or in a low-carbon manner. In Germany, the Federal Government aims at a stock of one million electric vehicles in the year 2020. This goal is very ambitious, since customers are reluctant to buy battery electric cars, probably most of all due to their limited range. A possible solution to the limited range problem is the use of Plug-in Hybrid Electric Vehicles or Extended Range Electric Vehicles (EREV), combining an electric battery with a combustion engine or a generator. These solutions overcome the range limitations while at the same time allowing driving on electric power for the majority of the total mileage. In this paper, we analyse the effects of an increased use of EREVs and battery electric vehicles using the travel demand model mobiTopp. For three scenarios with different rates of market penetration of electric vehicles, the travel demand and car usage is simulated over a simulation period of one week. The results show, that for 65 up to 70 percent of the mileage, EREVs can be driven in battery-only mode, demonstrating the usefulness of the EREV concept and indicating a substantial potential for the reduction of carbon dioxide emissions. The results, however, also show that with an uncontrolled charging strategy, i. e. every car recharges immediately after accessing a charging location, the peaks of electricity demand for charging the electric cars occurs when the general electricity demand is already high. During these periods, additional electricity demand is typically covered by gas-fuelled power plants, thus using fossil fuels. Therefore, the concept of introducing electric vehicles in order to reduce total carbon dioxide emissions can only succeed if combined with intelligent charging strategies.

Efficient Traffic Assignment for Public Transit Networks

We study the problem of computing traffic assignments for public transit networks: Given a public transit network and a demand (i.e. a list of passengers, each with associated origin, destination, and departure time), the objective is to compute the utilization of every vehicle. Efficient assignment algorithms are a core component of many urban traffic planning tools. In this work, we present a novel algorithm for computing public transit assignments. Our approach is based upon a microscopic Monte Carlo simulation of individual passengers. In order to model realistic passenger behavior, we base all routing decisions on travel time, number of transfers, time spent walking or waiting, and delay robustness. We show how several passengers can be processed during a single scan of the network, based on the Connection Scan Algorithm [Dibbelt et al., LNCS Springer 2013], resulting in a highly efficient algorithm. We conclude with an experimental study, showing that our assignments are comparable in terms of quality to the state-of-the-art. Using the parallelized version of our algorithm, we are able to compute a traffic assignment for more than ten million passengers in well below a minute, which outperforms previous works by more than an order of magnitude.

Sequential n-detection criteria: keep it simple!

The idea of n-detection is to exposure a target fault in n different ways. A major concern about the n-detection has been the question whether different test vectors really do excite the fault in different ways leading to different activation and propagation conditions. A special problem was the lack of methodology for answering this question. Our goal was to develop such a methodology and to evaluate its usefulness. In this work, we use a bridging fault-based non-target fault model for evaluating n-detect sequences.

Incorporating Stability of Mode Choice into an Agent-Based Travel Demand Model

Agent-based modelling is a promising technique, which allows to combine the advantages of different approaches to travel demand modelling. Agent-based modelling provides a framework that allows to easily substitute individual submodels. This paper shows, using the example of mobiTopp, how stability of mode choice can be integrated into an agent-based travel demand model. This has been achieved by replacing the submodel for mode choice by an extended variant, which takes stability of mode choice into account. The improved model reproduces this stability, as measured by two indicators based on mode usage and mobility styles, quite well.

Carsharing - ein neues Verkehrssystem!

Die Bedeutung von Carsharing als Verkehrsmittel nimmt zu. Insbesondere in grosstadischen Bereichen ist Carsharing Teil der stadtischen Mobilitat, Tendenz steigend. In Deutschland ist die Anzahl an Carsharing-Nutzern und der in den Carsharing-Systemen angebotenen Fahrzeuge in den letzten Jahren stetig gestiegen und Prognosen bestatigen diesen Trend.