Heiko Aydt

Symbiotic Simulation Systems: An Extended Definition Motivated by Symbiosis in Biology

Although various forms of symbiosis are known in biology, only mutualism has been considered in the context of symbiotic simulation systems. In this paper, we explain why the original definition of symbiotic simulation systems is narrow and why it is important to consider other forms of symbiosis as well. As a consequence we propose an extended definition of symbiotic simulation systems motivated by symbiosis in biology. By using this extended definition, we identify five different types of symbiotic simulation systems which can be applied in various applications. We describe how single systems can be combined and propose a hybrid symbiotic simulation system in the context of semiconductor manufacturing.

Research issues in symbiotic simulation

Symbiotic simulation is a paradigm in which a simulation system and a physical system are closely associated with each other. This close relationship can be mutually beneficial. The simulation system benefits from real-time measurements about the physical system which are provided by corresponding sensors. The physical system, on the other side, may benefit from the effects of decisions made by the simulation system. An important concept in symbiotic simulation is that of the what-if analysis process which is concerned with the evaluation of a number of what-if scenarios by means of simulation. Symbiotic simulation and related paradigms have become popular in recent years because of their ability to dynamically incorporate real-time sensor data. In this paper, we explain different types of symbiotic simulation and give an overview of the state of the art. In addition, we discuss common research issues that have to be addressed when working with symbiotic simulation. While some issues have been adequately addressed, there are still research issues that remain open.

SEMSim: A Distributed Architecture for Multi-scale Traffic Simulation

With the fast urbanization of our modern society, transportation systems in cities are facing increasing problems such as congestion, collisions, and high levels of emissions. Researchers have been searching for solutions by investigating better urban planning and transportation policies, introducing new technologies such as Intelligent Transportation System (ITS), or introducing more environmentally friendly vehicles such as electric vehicles (EVs). Traffic modeling and simulation is one tool adopted by researchers for more than half a century [1] to help authorities assess new infrastructure design, and new policies without impacting real traffic. City-scale nanoscopic traffic simulation is a challenging problem that requires parallelization and distribution. In this paper, we have given an overview of the architecture for our nanoscopic traffic simulator SEMSim. For efficient parallel simulation, reducing the dependencies between the various LPs is crucial. We have specified a multi-objective optimization problem concerned with the allocation of agents to clusters. In our future work, we will implement a nanoscopic traffic simulation and devise methods to solve this problem dynamically. Given the difficulty of the problem, these methods will have to make use of domain-specific knowledge, such as information about the topology of the road network.

A Partition-Based Match Making Algorithm for Dynamic Ridesharing

Ridesharing offers the opportunity to make more efficient use of vehicles while preserving the benefits of individual mobility. Presenting ridesharing as a viable option for commuters, however, requires minimizing certain inconvenience factors. One of these factors includes detours which result from picking up and dropping off additional passengers. This paper proposes a method which aims to best utilize ridesharing potential while keeping detours below a specific limit. The method specifically targets ridesharing systems on a very large scale and with a high degree of dynamics which are difficult to address using classical approaches known from operations research. For this purpose, the road network is divided into distinct partitions which define the search space for ride matches. The size and shape of the partitions depend on the topology of the road network as well as on two free parameters. This allows optimizing the partitioning with regard to sharing potential utilization and inconvenience minimization. Match making is ultimately performed using an agent-based approach. As a case study, the algorithm is applied to investigate the potential for taxi sharing in Singapore. This is done by considering about 110 000 daily trips and allowing up to two sharing partners. The outcome shows that the number of trips could be reduced by 42% resulting in a daily mileage savings of 230 000 km. It is further shown that the presented approach exceeds the mileage savings achieved by a greedy heuristic by 6% while requiring 30% lower computational efforts.

SEMSim Cloud Service: Large-Scale Urban Systems Simulation in the Cloud

Abstract Large-scale urban systems simulations are complex and with a large number of active simulation entities the computational workload is extensive. Workstation computers have only limited capabilities of delivering results for large-scale simulations. This leads to the problem that many researchers and engineers have to either reduce the scope of their experiments or fail to execute as many experiments as they would like in a given time frame. The use of high-performance computing (HPC) infrastructure offers a solution to the problem. Users of such simulations are often domain experts with no or little experience with HPC environments. In addition users do not necessarily have access to an HPC. In this paper we propose an architecture for a cloud-based urban systems simulation platform which specifically aims at making large-scale simulations available to typical users. The proposed architecture also addresses the issue of data confidentiality. In addition we describe the Scalable Electro-Mobility Simulation (SEMSim) Cloud Service that implements the proposed architecture.

Simulation-assisted exploration of charging infrastructure requirements for electric vehicles in urban environments

Abstract High population densities in todays cities are leading to increasing congestion and air pollution. Sustainable cities of the future will require a large scale transition to electro-mobility. The development of electric vehicle charging infrastructure is necessary to enable this transition. Existing methods for determining charging infrastructure take an optimization approach that ignores existing traffic demands and infrastructure. Moreover, the dynamics of vehicle movement like stop-and-go traffic, congestion and the effect of traffic lights are not considered in determining energy consumption. In this paper, we propose a novel nanoscopic city-scale traffic simulation based method for determining charging infrastructure locations; subsequently, we demonstrate its usefulness in spatio-temporal planning through a case-study of Singapore. Through this method, existing traffic and road network data and the dynamics of individual vehicle movement can be taken into consideration in planning.

A Computational Model of Emotions for Agent-Based Crowds in Serious Games

Crowd behaviour is an interesting social phenomenon that emerges from complex interactions of individuals. An important aspect of individual behaviour is emotion which plays a significant role in all aspects of human decision making. For example, heightened emotional states can lead people to take highly unexpected or irrational actions. One popular motivation for simulation of virtual crowds is to generate believable characters in movies and computer games. Recently the concept of serious games has been introduced in both academic and industrial circles. In this paper, we propose an emotion engine, based on modern appraisal theory, that is able to model various emotional crowd characteristics. This appraisal engine is capable of capturing the dynamics of emotional contagion and we show how different crowd composition can lead to different patterns of emotional contagion. In addition, we describe a serious game designed for training military personnel in peaceful crowd control. We evaluate this engine in the context of a property protection protest scenario where the players or soldiers are tasked to maintain a peaceful protest without violence. A systematic evaluation is presented which supports the facial validity of the emotion engine and our model of emotional contagion.

A Multi-threaded Execution Model for the Agent-Based SEMSim Traffic Simulation

An efficient simulation execution engine is crucial for agent-based traffic simulation. Depending on the size of the simulation scenario the execution engine would have to update several thousand agents during a single time step. This update may also include route calculations which are computationally expensive. The ability to dynamically re-calculate the route of agents is a feature often not required in classical microscopic traffic simulations. However, for the agent-based traffic simulation which is part of the Scalable Electro-Mobility Simulation (SEMSim) platform, the routing ability of agents is an important feature. In this paper, we describe a multi-threaded simulation engine that explicitly supports routing capabilities for every agent. In addition, we analyse the efficiency and performance of our execution model in the context of a Singapore-based simulation scenario.

Symbiotic Simulation Control in Semiconductor Manufacturing

Semiconductor manufacturing is a highly complex and asset intensive process. Solutions are needed to automate control of equipment and improve efficiency. We describe a symbiotic simulation control system which uses reactive what-if analysis to find a stable configuration of a wet bench tool set. This control system is based on a generic framework for symbiotic simulation. We show that symbiotic simulation can be used to make control decisions in near real-time. Furthermore, we show that our approach yields a notable performance improvement over common practise.

An Asynchronous Synchronization Strategy for Parallel Large-scale Agent-based Traffic Simulations

Large-scale agent-based traffic simulation is a promising tool to study the road traffic and help solving traffic problems, such as congestion and high emission in megacities. Such simulation requires high computational resource which triggers the need for parallel computing. The parallelization of agent-based traffic simulations is generally performed by decomposing the simulation space into spatial subregions. The agent models contained by each subregion are executed by Logical Processes (LPs). As the simulated system evolves over the simulation time in individual LPs, synchronization among LPs is required due to data dependencies. Existing work has used global barriers for synchronization which is a type of synchronous synchronization method. However, global barriers have very low efficiency due to the waiting of processes at barriers. High synchronization overhead is still one of the major performance issues in parallel large-scale agent-based traffic simulations. In this paper, we proposed a novel asynchronous conservative synchronization strategy named Mutual Appointment (MA) to address this issue. MA removes global barriers and allows LPs to communicate individually. Since the efficiency of conservative synchronization relies on the lookahead of the simulated system, a heuristic was developed to increase the lookahead in agent-based traffic simulations. It takes advantage of the intrinsic uncertainties in traffic simulations. MA together with the lookahead heuristic forms the Relaxed Mutual Appointment (RMA) strategy. Its efficiency was investigated in the parallel agent-based traffic simulator SEMSim Traffic using real world traffic data. Experiment results showed that the MA strategy improved the speed-up of the parallel simulation compared to the barrier method, and the RMA strategy further improved the MA strategy by reducing the number of synchronization messages significantly.