Sojung Kim

An agent-based simulation approach for dual toll pricing of hazardous material transportation

A dual toll pricing is a conceptual policy in which policy maker imposes toll on both hazardous materials (hazmat) vehicles as well as regular vehicles for using populated road segments to mitigate a risk of haz-mat transportation. It intends to separate the hazmat traffic flow from the regular traffic flow via controlling the dual toll. In order to design the dual toll pricing policy on a highly realistic road network environment and detailed human behaviors, an extended BDI framework is employed to mimic human decision behaviors in great detail. The proposed approach is implemented in AnyLogic® agent based simulation software with using a traffic data of Albany, NY. Also, search algorithms in OptQuest® are used to determine the optimum dual toll pricing policy which results in the minimum risk and travel cost based on the simulation results. The result reveals the effectiveness of the proposed approach in devising a reliable policy under the realistic road network conditions.

Cognition-based hierarchical en route planning for multi-agent traffic simulation

Abstract The goal of this study is to model drivers’ cognition-based en route planning behaviors in a large-scale road network via the Extended Belief-Desire-Intention (E-BDI) framework. E-BDI is a probabilistic behavior modeling framework based on agents’ own preferences of multiple attributes (e.g., travel time and its variance) and daily driving experiences. However, it is challenging to use the E-BDI framework for the demonstration of drivers’ en route planning behavior in a large-scale road network due to its high computational demand. To handle the computation issue, a hierarchical en route planning approach is proposed in this study. The proposed E-BDI-based en route planning approach consists of three major procedures: (1) network partitioning, (2) network aggregation, and (3) E-BDI-based en route planning. The Java-based E-BDI module integrated with DynusT® traffic simulation software is developed to demonstrate the proposed en route planning approach in Phoenix, Arizona road network involving 11,546 nodes and 24,866 links. The demonstration results reveal that the proposed approach is computationally efficient and effective in representing various en route planning behaviors of drivers in a large-scale road network.

Impact of road environment on drivers’ behaviors in dilemma zone: Application of agent-based simulation

At a signalized intersection, there exists an area where drivers become indecisive as to either stop their car or proceed through when the traffic signal turns yellow. This point, called a dilemma zone, has remained a safety concern for drivers due to the great possibility of a rear-end or right-angle crash occurring. In order to reduce the risk of car crashes at the dilemma zone, Institute of Transportation Engineers (ITE) recommended a dilemma zone model. The model, however, fails to provide precise calculations on the decision of drivers because it disregards the supplemental roadway information, such as whether a red light camera is present. Hence, the goal of this study was to incorporate such roadway environmental factors into a more realistic driver decision-making model for the dilemma zone. A driving simulator was used to determine the influence of roadway conditions on decision-making of real drivers. Following data collection, each drivers decision outcomes were implemented in an Agent-Based Simulation (ABS) so as to analyze behaviors under realistic road environments. The experimental results revealed that the proposed dilemma zone model was able to accurately predict the decisions of drivers. Specifically, the model confirmed the findings from the driving simulator study that the changes in the roadway environment reduced the number of red light violations at an intersection.

A SysML-based simulation model aggregation framework for seedling propagation system

This paper proposes a Systems Modeling Language (SysML)-based simulation model aggregation framework to develop aggregated simulation models with high accuracy. The framework consists of three major steps: 1) system conceptual modeling, 2) simulation modeling, and 3) additive regression model-based parameter estimation. SysML is first used to construct the system conceptual model for a generic seedling propagation system in terms of system structure and activities in a hierarchical manner (i.e. low, medium and high levels). Simulation models conforming to the conceptual model are then constructed in Arena. An additive regression model-based approach is proposed to estimate parameters for the aggregated simulation model. The proposed framework is demonstrated via one of the largest grafted seedling propagation systems in North America. The results reveal that 1) the proposed framework allows us to construct accurate but computationally affordable simulation models for seedling propagation system, and 2) model aggregation increases the randomness of simulation outputs.

Simulation-based machine shop operations scheduling system for energy cost reduction

Abstract Owing to the ever increasing requirements in sustainability, manufacturing firms are trying to reduce their energy consumption and cost. In this paper, we propose a simulation-based machine shop operations scheduling system for minimizing the energy cost without sacrificing the productivity. The proposed system consists of two major functions: (1) real-time energy consumption monitoring (through power meters, a database server, and mobile applications) and (2) simulation-based machine shop operations scheduling (through a machine shop operations simulator). First, the real-time energy consumption monitoring function is developed to collect energy consumption data and provide real-time energy consumption status monitoring/electrical load abnormality warnings. Second, the simulation-based machine shop operations scheduling function is devised to estimate the energy consumptions and cost of CNC machines. In addition, an additive regression algorithm is developed to formulate energy consumption models for each individual machine as simulation inputs. The proposed system is implemented at a manufacturing company located in Tucson, Arizona state of USA. The experiment results reveal the effectiveness of the proposed system in achieving energy cost savings without sacrificing the productivity under various scenarios of machine shop operations.