Analysis of Day-to-day Learning Processes and Travel Choice Dynamics in Traffic Networks Considering Real-time Information and Signal Control
抄録
A day-to-day dynamic framework is developed to study network dynamics under real-time information and responsive signal control systems. Two level of tripmaker decision-making processes are incorporated: (1) day-to-day dynamics and (2) real-rime dynamics. In day-to-day dynamics, each driver uses a disutility function of perceived travel time and perceived schedule delay to evaluate the alternative travel choices, then selects an alternative based on the utility maximization principle. Day-to-day dynamics are governed by tripmaker's daily learning processes. Learning model, applied the Bayesian theorem, consider the travelers update their travel time perceptions form one day to the next in light of information provided by ITS and their previous experience. Real-time dynamics consider en-route switching decisions in response to real-time information on prevailing traffic conditions. Traffic contorol are evaluated by the actuated control. The framework is illustrated through numerical experiments to investigate the day-to-day evolution of network flows under real-time information and responsive signal control, and assess the effectiveness of such information in a proper dynamic perspective.
高度交通管理を考慮した日々の学習過程と動的交通行動分析
.png)
nagano_nct-34-09.pdf
(551.53KB)
