A CyberGIS approach is presented in this chapter where microscopic traffic simulation and gas dispersion simulation systems are combined in order to estimate atmospheric pollution for different scenarios. The combination of these two simulation models allows for detailed investigations of different situations such as the investigation of pollution impacts of different traffic infrastructure variants, as well as for prediction of expected pollution and whether pollutant thresholds will be exceeded. For different case studies, real data about traffic movements provided by the state government, a digital terrain model of the area as well as real measurements of atmospheric data have been used. The evaluation of the approach shows that variations in the settings, regarding traffic or atmospheric conditions, lead to different patterns of observed pollution. The CyberGIS environment described is used to run multiple simulations on a distributed cyberinfrastructure, where the high-end computational resources are available on servers in Europe and in North America.

Traffic routing is a well-established optimization problem in traffic management. Here, we address dynamic routing problems where the load of roads is taken into account dynamically, aiming at the optimization of required travel times. We investigate ant-based algorithms that can handle dynamic routing problems, but suffer from negative emergent effects like road congestions. These negative effects are inherent in the design of ant-based algorithms. In this article we propose an inverse ant-based routing algorithm to (a) maintain the positive features of ant-based algorithms for dynamic routing problems, while (b) avoiding the occurrence of negative emerging effects, like road congestion. We evaluated the performance of the proposed algorithm by comparing its results with two alternative routing algorithms, namely, A*, which is a static routing algorithm, and an iterative approach. In particular, the iterative approach is used for providing an upper bound, as it uses routing knowledge in a number of calibration runs, to determine the actual load, before the effective routing is done. For the evaluation we used the agent-based traffic simulation system MAINSIM. The evaluation was done with one synthetic and two real-world scenarios, to outline the practical relevance of our findings. Based on these evaluations, we can conclude that the inverse ant-based routing approach is particularly suited for a scenario with a high traffic density, as it can adapt the routing of each vehicle, while avoiding the negative emerging effects of conventional ant-based routing algorithms.

Simulation is applied in various domains to investigate different variants, to study effects, or to optimize models with respect to some performance measurement. Simulation models can exhibit a high level of complexity and thus, consist of many components and potentially many adjustable input parameters as well as a large number of output measurements. In this habilitation thesis, different methods in the fields of automation, data mining, and optimization in the context of simulation are developed. The underlying motivation is to increase the degree of automation and to increase the efficiency when performing simulation studies. The proposed methods are evaluated using three different simulation systems: Manufacturing simulation, traffic simulation, and gas dispersion simulation.

Simulation is applied in various domains to investigate different variants, to study effects, or to optimize models with respect to some performance measurement. Simulation models can exhibit a high level of complexity and thus, consist of many components and potentially many adjustable input parameters as well as a large number of output measurements. In this habilitation thesis, different methods in the fields of automation, data mining, and optimization in the context of simulation are developed. The underlying motivation is to increase the degree of automation and to increase the efficiency when performing simulation studies. The proposed methods are evaluated using three different simulation systems: Manufacturing simulation, traffic simulation, and gas dispersion simulation.

Providing assistance systems for simulation studies can support the user by performing monotonous tasks and keeping track of relevant results. In this paper we present approaches to estimate, if – and when – statistically significant results are expected for certain investigations. This information can be used to control simulation runs or to provide information to the user for interaction. The first approach is used to classify if significance is expected to occur for given samples and the second approach estimates the needed replications until significance is expected be reached. For an initial evaluation of the approaches, experiments are performed on samples drawn from normal distributions.

This work describes an approach for the computation of function features out of optimization functions to train a decision tree. This decision tree is used to identify adequate parameter settings for Particle Swarm Optimization (PSO). The function features describe different characteristics of the fitness landscape of the underlying function. We distinguish between three types of features: The first type provides a short overview of the whole search space, the second describes a more detailed view on a specific range of the search space and the remaining features test an artificial PSO behavior on the function. With these features it is possible to classify fitness functions and to identify a parameter set which leads to an equal or better optimization process compared to the standard parameter set for Particle Swarm Optimization.

Discrete-event simulation has been established as an important methodology in various domains. In particular in the automotive industry, simulation is used to plan, control, and monitor processes including the flow of material and information. Procedure models help to perform simulation studies in a structured way and tools for data preparation or statistical analysis provide assistance in some phases of simulation studies. However, there is no comprehensive data assistance following all phases of such procedure models. In this article, a new approach combining assistance functionalities for input and output data analysis is presented. The developed tool -- EDASim -- focuses on supporting the user in selection, validation, and preparation of input data as well as to assist the analysis of output data. The proposed methods have been implemented and initial evaluations of the concepts have led to promising feedback from practitioners.

For a proper simulation of urban traffic scenarios, besides cars other road users, namely bicycles and pedestrians, have to be modeled. In scenarios where a whole city is modeled, a detailed actor-based simulation of pedestrians leads to expensive extra computational load. We investigate to what extent it is possible to capture traffic effects imposed by simulated pedestrians and then perform simulations without pedestrians. We propose to collect information about pedestrian impacts in a simulation with pedestrians, estimate underlying probability distributions and finally, use a simplified model where only these effects are generated probabilistically. We investigate two approaches – a best-fit distribution fitting and a histogram-based distribution approximation – using synthetic data as well as simulated traffic scenarios. The experiments show that using the proposed approximations can lead to similar average cars’ travel times.

In this article an approach is presented where machine learning classifiers are used to drive an ensemble modeling method of multiple atmospheric transport and dispersion simulations. The goal is to achieve a higher spread of the results with a lower number of ensemble simulations. Symbolic machine learning algorithms are used to define choices for the variation of meteorological input data, model parameters, model physics, based on their combined effects on the final dispersion calculations (i.e., construction of ensembles). The methodology uses an iterative approach with the aim to identify ensemble members leading to a more balanced distribution of results. The methodology is tested using real meteorological data from Istanbul, Turkey, simulating atmospheric releases along the Bosphorus channel. In an extensive evaluation, different settings of the approach are compared in a series of experiments. The results indicate that the desired effect of more balanced results of the ensemble members can be achieved by the approach.

Traffic simulation systems are widely used for the prediction of certain effects like traffic jam formation or the analysis of performance for new traffic light systems. Fuel consumption and CO2 emissions are important measurements. This work integrates a physical model for fuel consumption into a microscopic traffic simulation system for urban scenarios. The parameters of the model are discussed and practical values are given. The model is evaluated on different scenarios with focus on innercity simulation. The results indicate that the simulation of bicycles and pedestrians as well as the usage of a Digital Terrain Model (DTM) increase fuel consumption of simulated cars. A map of CO2 emissions for the chosen simulation area is calculated and provides an insight on how emissions are distributed in cities.

Many studies in the context of traffic simulation investigate effects of new regulation strategies on travel times. However, in most cases it is assumed, that these regulations are followed by all actors. This work investigates the effects of non-compliance of simulated road user agents. In particular it is analyzed to what extent rule breaking agents have advantages on the cost of the remaining road users. For evaluation we take into account three different scenarios: Overtaking prohibition for trucks on motorways, bicycles pushing to the front at traffic lights and pedestrians crossing roads in aggressive manner. In all scenarios, the fraction of rule breaking agents is varied. Simulation results show that rule breaking does not always lead to disadvantages for the remaining road users.

Simulations are widely used for modeling, analysis, planning, and optimization of traffic flows and phenomena. Every human moving in a city participates at least to some extent as a pedestrian in urban traffic. Nevertheless, pedestrians usually are not part of traffic simulations. This work presents a model for pedestrian movement, taking into account interactions with other road users and among pedestrians on pedestrian crossings. The components of the model are evaluated separately and in a city scenario with an accumulated road length of about 550km. Experimental results indicate an influence of pedestrians on urban traffic. This leads to the finding, that the consideration of pedestrians in urban traffic simulation may lead to a gain of knowledge.

Due to the complexity of production and logistics systems, more and more expert know-how is necessary for building adequate simulation models and for designing the right experiments for the requested aims of investigation. To support simulation studies as efficient and high-quality projects, there are different development activities like specialised unit libraries, model generation algorithms, procedure models or decision support systems. Based on the results of the joint research project AssistSim, this article presents a new approach of designing and executing experiments as one innovative possibility for support while performing simulations studies.

Simulations are widely used for modeling, analysis, planning, and optimisation of traffic flows and phenomena. For realistic traffic simulations within urban scenarios, the following tasks have to be solved: (1) modeling of the road structure; (2) specification of the behaviour on the road. In our days, very detailed road models for almost any major city exist in Geographic Information Systems (GIS). In the last two decades, the Nagel-Schreckenberg model (NaSch) has been established as de facto standard for car behaviour in freeway traffic due to its efficient and realistic simulations. Within urban scenarios, NaSch lacks of flexibility to integrate heterogeneous road users like cars and bicycles. The tasks mentioned before are addressed in this paper, i.e., we propose an approach for modeling and specification of urban mixed traffic simulations. As a first step (1), an extended graph as basis for traffic simulation has to be designed. For a concrete scenario, it will be automatically generated on basis of OpenStreetmap cartographical material. The specification of road user behaviour (2) has been influenced by the NaSch model. However, the model has been extended to cover the lack of NaSch in urban scenarios: A non cell-based approach is chosen for traffic movement. Furthermore, the routing of traffic users is based on either probability or A* based routing. In this paper, details on the modeling and specification are presented and experimental results are provided.

In this paper we introduce a new approach for automatic parameter configuration of Particle Swarm Optimization (PSO) by using features of objective function evaluations for classification. This classification utilizes a decision tree that is trained by using 32 function features. To classify different functions we compute features of the function from observed PSO behavior. These features are an adequate description to compare different objective functions. This approach leads to a trained classifier which gets as input a function and returns a parameter set. Using this parameter set leads to an equal or better optimization process compared to the standard parameter settings of Particle Swarm Optimization on selected test functions.

The increase of road users and traffic load has lead to the situation that in some regions road capacities appear to be exceeded regularly. Although there is natural capacity limit of roads, there exist potentials for a dynamic adaptation of road usage. Finding out about useful rules for dynamic adaptations of traffic rules is a costly and time consuming effort if performed in the real world. In this paper, we introduce an agent-based traffic simulation model and present an approach to learning dynamic adaptation rules in traffic scenarios based on supervised learning from simulation data. For evaluation, we apply our approach to synthetic traffic scenarios. Initial results show the feasibility of the approach and indicate that learned dynamic adaptation strategies can lead to an improvement w.r.t. the average velocity in our scenarios.

Simulation is widely used in order to evaluate system changes, to perform parameter optimization of systems, or to compare existing alternatives. Assistance systems for simulation studies can support the user by performing monotonous tasks and keeping track of relevant results. In this paper we present an approach to significance estimation in order to estimate, if – and when – statistically significant results are expected for certain investigations. This can be used for controlling simulation runs or providing information to the user for interaction. We introduce two approaches: one for the classification if significance is expected to occur for given samples and another for the prediction of needed replications until significance might be reached. Experiments are performed on normal distributions for an initial evaluation of the approaches.

Der Logistik kommt seit Anfang der 90er Jahre für die Planung und Implementierung global verteilter Produktion eine Schlüsselrolle zu. Die hierbei zu bewältigenden Aufgaben beinhalten durch die Integration unterschiedlichster Akteure eine inhärente Komplexität, die mit konventionellen Ansätzen nicht oder nur mit erheblichen Kosten beherrschbar ist. Daher stellt die Logistik eine herausfordernde Domäne für die Untersuchung und Entwicklung von Methoden der Künstlichen Intelligenz dar. Dies schließt sowohl die Identifikation von theoretischen Fragestellungen sowie die Anwendung von State-of-the-Art-Technologien ein.