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Research Outputs
A Game-theoretic Decision-making Model with Probabilistic Payoffs in Vehicle Interactions
While traveling on roads, vehicles frequently encounter sections where different traffic streams converge due to geometric structures. In these sections, interactions between vehicles can create safety issues. Based on this, this study aims to examine two representative sections where mandatory lane-changing occurs, namely merging sections and roundabouts, and proposes a game-theoretic model to determine the vehicle’s strategy by considering probabilistic payoffs. To this end, the payoffs for the choices were defined as travel time or passing time, and the decision probabilities and equilibrium were evaluated. As a result of the model verification, the predicted results showed a fairly high level of accuracy compared to actual decisions. This study provides a micro-level exploration of lane-changing behavior, allowing for a realistic understanding of decision-making.
Identifying Functional Characteristics of Urban Areas Based on Travel Demand Patterns
Understanding the actual functions of places is a critical aspect of urban planning, transportation management, and regional development. While planned functions are determined by the built environment and land use, revealed functions result from the interaction between people and places, observable through travel demand patterns. Based on the assumption that places with similar activity types exhibit similar travel demand patterns, we propose a two-step data mining approach to identify revealed functions: i) identifying place clusters based on similar travel demand patterns for buses and taxis, and ii) inferring functional characteristics of each place cluster based on distinctive spatial context. Using travel demand data from buses and taxis in Daejeon Metropolitan City, South Korea, in 2019, we identify six place clusters, including residential, industrial, business/education/research, commercial, and mixed-functional areas. The goal is to provide insights into regional functions revealed by travel demand, emphasizing the significance of spatiotemporal features for informed urban decision-making.
The Analysis of Driving Characteristics on Internal Resistance Degradation in Electric Vehicle Battery
This study analyzes driving characteristics that contribute to the degradation of internal resistance in electric vehicle (EV) batteries under various traffic scenarios and road gradient conditions. To achieve this, a multi-level Battery-in-the-Loop System (BILS) simulator is developed. The simulator consists of a driving scenario generation module, a load current calculation module, and a real battery state observation module. It is designed to analyze battery degradation phenomena based on driving scenarios generated under diverse road gradient and traffic conditions.
