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Reinforcement Learning to Reach Equilibrium Flow on Roads in Transportation System
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Orientador(es)
Resumo(s)
Traffic congestion threats the vitality of cities and
the welfare of citizens. Transportation systems are using various
technologies to allow users to adapt and have a different decision
on transportation modes. Modification and improvement of
these systems affect commuters’ perspective and social welfare.
In this study, the effect of equilibrium road flow on commuters’
utilities with a different type of transportation mode will be
discussed. A simple network with two modes of transportation
will be illustrated to test the efficiency of minority game and
reinforcement learning in commuters’ daily trip decision
making based on time and mode. The artificial society of agents
is simulated to analyze the results.
Descrição
Palavras-chave
Transportation system Minority game Reinforcement learning Multi-agent system Simulation