• International Journal of Automotive Technology
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• v.7 no.1
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• pp.25-34
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• 2006

For decades, simulation technique has been well validated in areas such as computer and communication systems. Recently, the technique has been much used in the area of transportation and traffic forecasting. Several methods have been proposed for investigating complex traffic flows. However, the dynamics of vehicles and diversities of driver characteristics have never been considered sufficiently in these methods, although they are considered important factors in traffic flow analysis. In this paper, we propose a traffic simulation tool called Multi-Agent for Traffic Simulation with Vehicle Dynamics Model (MATDYMO). Road transport consultants, traffic engineers and urban traffic control center managers are expected to use MATDYMO to efficiently simulate traffic flow. MATDYMO has four sub systems: the road management system, the vehicle motion control system, the driver management system, and the integration control system. The road management system simulates traffic flow for various traffic environments (e.g., multi-lane roads, nodes, virtual lanes, and signals); the vehicle motion control system constructs the vehicle agent by using various vehicle dynamic models; the driver management system constructs the driver agent capable of having different driving styles; and lastly, the integrated control system regulates the MATDYMO as a whole and observes the agents running in the system. The vehicle motion control system and driver management system are described in the companion paper. An interrupted and uninterrupted flow model were simulated, and the simulation results were verified by comparing them with the results from a commercial software, TRANSYT-7F. The simulation result of the uninterrupted flow model showed that the driver agent displayed human-like behavior ranging from slow and careful driving to fast and aggressive driving. The simulation of the interrupted flow model was implemented as two cases. The first case analyzed traffic flow as the traffic signals changed at different intervals and as the turning traffic volume changed. Second case analyzed the traffic flow as the traffic signals changed at different intervals and as the road length changed. The simulation results of the interrupted flow model showed that the close relationship between traffic state change and traffic signal interval.

• International Journal of Highway Engineering
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• v.16 no.4
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• pp.119-126
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• 2014

PURPOSES: Used in transportation planning and traffic engineering, almost traffic simulation tools have input variable values optimized by overseas traffic flow attribution because they are almost developed in overseas country. Thus, model calibration appropriated for internal traffic flow attribution is needed to improve reliability of simulation method. METHODS : In this study, the traffic flow model calibration is based on expressways. For model calibration, it needs to define each expressway link according to attribution, thus it is classified by design speed, geometric conditions and number of lanes. And modified greenshield model is used as traffic flow model. RESULTS : The result of the traffic model calibration indicates that internal congested density is lower than overseas. And the result of analysis according to the link attribution indicates that the more design speed and number of lanes increase, the lower the minimum speed, the higher the congested density. CONCLUSIONS: In the traffic simulation tool developed in overseas, the traffic flow is different as design speed and number of lanes, but road segment don't affect traffic flow. Therefore, these results need to apply reasonably to internal traffic simulation method.

• Journal of the Korean Institute of Plant Engineering
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• v.23 no.4
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• pp.21-27
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• 2018

This paper proposed the real-time monitoring methodology of a traffic signal controller. The proposed methodology is based on the simulation technology, and it is necessary to construct a simulation model imitating the behavior of a traffic signal controller. By executing the simulation model, we can obtain the 'nominal system trajectory' of the traffic signal controller. On the other hand, an IoT(Internet of Things)-based monitoring device is implemented in a traffic signal controller. Through the monitoring device, it is possible to obtain the 'actual system trajectory'. By comparing the nominal system trajectory and the actual system trajectory, we can estimate the degree of deterioration of a traffic signal controller.

• Proceedings of the Korea Society for Simulation Conference
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• 2002.05a
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• pp.163-168
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• 2002

The road sign in dynamic traffic system is an important element which affects on human cognitive performance on driving. Web-based vision system simulator was developed to examine the cognition time of the road sign in dynamic environment. This experiment was designed in within-subject design with two factors; vehicle speed and the amount of information of the traffic sign. It measured the cognition time of the road sign through two evaluation methods; the subjective test with vision system simulator and computational cognitive model. In these two evaluations of human cognitive performance under the dynamic traffic environment, it demonstrated that subject's cognition time was affected by both the amount of information of traffic sign and driving speed.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.5 no.6
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• pp.63-68
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• 1982

We can reduce delays and number of stops in the traffic area by means of optimal design of traffic signal system. A computer simulation model to simulate and predict the traffic signal system of Jong-Ro 4-th street was developed for determination of optimal cycle time. This simulation model was developed in relation to Jong-Ro 4-th street, but this model can be applied for other places with small modification.

• International Journal of Automotive Technology
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• v.7 no.2
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• pp.145-154
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• 2006

In the companion paper, the composition and structure of the MATDYMO (Multi-Agent for Traffic Simulation with Vehicle Dynamic Model) were proposed. MATDYMO consists of the road management system, the vehicle motion control system, the driver management system, and the integration control system. Among these systems, the road management system and the integration control system were discussed In the companion paper. In this paper, the vehicle motion control system and the driver management system are discussed. The driver management system constructs the driver agent capable of having different driving styles ranging from slow and careful driving to fast and aggressive driving through the yielding index and passing index. According to these indices, the agents pass or yield their lane for other vehicles; the driver management system constructs the vehicle agents capable of representing the physical vehicle itself. A vehicle agent shows its behavior according to its dynamic characteristics. The vehicle agent contains the nonlinear subcomponents of engine, torque converter, automatic transmission, and wheels. The simulation is conducted for an interrupted flow model and its results are verified by comparison with the results from a commercial software, TRANSYT-7F. The interrupted flow model simulation is implemented for three cases. The first case analyzes the agents' behaviors in the interrupted flow model and it confirms that the agent's behavior could characterize the diversity of human behavior and vehicle well through every rule and communication frameworks. The second case analyzes the traffic signals changed at different intervals and as the acceleration rate changed. The third case analyzes the effects of the traffic signals and traffic volume. The results of these analyses showed that the change of the traffic state was closely related with the vehicle acceleration rate, traffic volume, and the traffic signal interval between intersections. These simulations confirmed that MATDYMO can represent the real traffic condition of the interrupted flow model. At the current stage of development, MATDYMO shows great promise and has significant implications on future traffic state forecasting research.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.11 no.5
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• pp.62-69
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• 2012

Traffic simulation models have been used for assessing various transportation strategies. Through comparing results from a simulation model and real field data, researchers try to show how close the model can reproduce the real world traffic. This model verification step is one of the most essential tasks in modeling procedure. Traffic counts and speeds have been frequently used for the verification or validation. Authors modeled severe PM peak bottleneck situation on the I-40 corridor in Raleigh, North Carolina using DYNASMART-P, a mesoscopic traffic simulation tool and verified the model. NCDOT has Traffic Information Management System which has archive capability for the traffic speeds on the I-40 corridor. However, the authors selected travel time as the field measure for model verification and collected the data using a GPS equipment because the speed data from NCDOT speed detectors are spot speeds which are not appropriate for comparison with link average speed from the simulation model. This paper describes the GPS field data collection procedure, the model verification method, and the results.

• Journal of the Korea Society for Simulation
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• v.11 no.4
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• pp.43-55
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• 2002

Vessel traffic safety management means the managerial technical measures for improving the marine traffic safety in general terms. The main flow of vessel traffic safety management is that: 1) Traffic Survey, 2) Replay by Marine Traffic Flow Simulation, 3) Quantitative Assessment, 4) Policy Alternatives, 5) Prediction·Verification. In the management of vessel traffic safety, it is most important to establish assessment models that can numerically estimate the current safety level and quantitatively predict the correlation between the measures to be taken and the improvement of safety and the reduction of ship handling difficulties imposed on mariners. In this paper, the replay model for traffic flow simulation was made using marine traffic survey data, and the present traffic situation became replay in the computer. An attempt was made to rate the current safety of ports and waterways by applying the Environmental Stress model. And, as a countermeasure for traffic management, by taking of, the promotion of total traffic congestion in early morning rush hour, the correlation between traffic control rate and the reduction in ship handling difficulties imposed on mariners was predicted quantitatively.

• Journal of the Korea Society for Simulation
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• v.6 no.2
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• pp.15-29
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• 1997

Many studies have been performed and applied successfully for the control of undersaturated intersections, but most of them have been ineffective or invalid in oversaturated conditions. There has been relatively limited research in traffic control for oversaturated environments, and most has been too theoretical to be applied in a real system. Therefore, a traffic simulation model specifically designed for oversaturated arterial networks was developed. Two control objectives of traffic signal timing in oversaturated conditions were taken into consideration. One was to maximize the throughput, or the number of vehicles processed during a given time period. The other was to prevent queue spillback or to minimize the occurrence of queue spillback if inevitable.

• Journal of Korean Society of Transportation
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• v.14 no.1
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• pp.101-116
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• 1996

Increased attention has been paid in recent years to the need of traffic management for alleviating urban traffic congestion. This paper presents a discrete event modeling and simulation framework for analyzing the traffic flow. Traffic simulation models can be classified as being either microscopic and macroscopic models. The discrete event modeling and simulation technique can be basically employed to describe the macroscopic traffic simulation model. To do this, we have employed the System Entity Structure/Model Base (SES/MB) framework which integrates the dynamic-based formalism of simulation with the symbolic formalism of AI. The SES/MB framework supports to hierarchical, modular discrete event modeling and simulation environment. We also adopt the Symbolic DEVS (Discrete Event System Specification) to developed the automated analysis methodology for generating optimal signal light policy. Several simulation tests will demonstrates the techniques.