In computer simulation of a large number of moving objects (MOs), how to enlarge Δt (the interval between the simulation time steps) without introducing causality errors is one of the primary keys to enhancing performance. Causality errors can be avoided by using the same Δt among related MOs when they are in the scene of detection (SoD). But in a large-scale MO simulation, MOs interact with one another in a complicated manner requiring a large calculation cost to predict the beginning time of SoD. In this paper we propose an event-aware dynamic time step synchronization method (DTSS) for distributed MO simulation, which increases Δt without introducing causality errors and speeds up the simulation. DTSS can be implemented with little calculation cost because: (1) DTSS does not calculate the beginning time of SoD exactly, but calculates the time for possible entry into SoD with a simple mechanisim, and (2) MO simulation consists of a "movement"-phase and a "detection"-phase in which the distance-calculation between MOs requires a heavy load, and DTSS utilizes the distance values to calculate Δt. In this paper, we also discuss a suitable HLA based time management mechanism to implement DTSS on a distributed computing environment. In the performance evaluation of DTSS, the calculation cost of DTSS is implemented by using the HLA suitable time management mechanism. The results show that DTSS can be executed within the ideal time plus its 1% over-cost when a basic scenario of war-game simulation is employed. Therefore if the ratio of SoD to the total simulation is small, the execution time is expected to decrease to nearly this ratio. We also introduce the criterion for determining when DTSS is superior to the conventional method by using the performance evaluation results. The results presented in this paper are effectively utilized when DTSS is applied to practical applications.

Although a number of car-traffic simulators have been developed for various purposes, none of the existing simulators enhance the simulation accuracy using sensor data or allow the system structure to re-configure the system structure depending on the application. Our goal was to develop a highly accurate, highly modular, flexible, and scalable micro-model car-traffic simulation system. The HLA (High Level Architecture) was applied to every system module as a standard interface between each module. This allows an efficient means for evaluating and validating a variety of micro-model simulation schemes. Our ongoing projects consist of running several identical simulations concurrently, with different parameter sets. By sending the results of these simulations to a manager module, which analyzes both the parameter sets and the simulated results, the manager module can evaluate the best-simulated results and determine the next action by comparing these results with the sensor data. In this system, the sensor data or the statistical data on the flow of traffic, obtained by monitoring real roads, is used to improve the simulation accuracy. Future systems are being planned to employ real time sensor data, where the input of the data occurs at almost real time speed. In this paper, we discuss the design of a HLA-based car-traffic simulation system and the construction of a sensor-data fusion algorithm. We also discuss our preliminary evaluation of the results obtained with this system. The results show that the proposed fusion algorithm can adjust the simulation accuracy to the logged sensor data within a difference of 5% (minimum 1.5%) in a specific time period. We also found that simulations with 500 different parameter sets can be executed within 5 minutes using 8 simulator modules.

Simulation based education and training, especially wargame simulations, are being used widely in the field of defense modeling and in simulation communities. In order to efficiently train students and trainees, the wargame simulations must have both high performance and high fidelity. In this paper, we discuss design and implementation issues for a prototype of a parallel and distributed wargame simulation system. This wargame simulation system is based on High Level Architecture (HLA) and employs some optimization to achieve both high performance and high fidelity in the simulation system. The results show that the proposed optimization method is effective when optimization is applied to 93.5% or less of the moving objects (PFs) within the range of detection (RofD) of both the red and blue teams. Specifically, when each team has 1000 PFs we found that if the percentage of PFs within RofD is less than 50% for both teams, our method is over two times better than for the situation where there is no optimization.

This paper proposes a Space-Time Object Model, an object oriented model that possesses space and time management mechanisms. The goal of this object model is to provide a common software infrastructure for implementing large-scale moving object simulations efficiently, such as car traffic simulations and disaster evacuation simulations, using a direct mapping scheme on a parallel and distributed computing environment. In this object model, the software infrastructure provides two principal functions, "Space Management" and "Time Management," which allows programmers to focus on application programming instead of parallel programming. Although there are several known infrastructure software, which provide the environment needed to develop and execute parallel and distributed simulations, they only provide a "Time Management" mechanism. In this paper, we present a Space-Time Object Model and an overview of a program called OSim, which is an implementation of the Space-Time Object Model. Then, we demonstrate the applicability and efficiency of this model by introducing the overview and evaluation results of a parallel car traffic simulation system using OSim.