Modeling of Vehicle Emission Pricing Strategy Using Multi-agent System

Abstract

This study models the vehicle emission pricing strategy using the multi-agent system (MAS). A two-level hierarchical MAS is adopted. The manager agents are cognitive agents that give instructions while the worker agents carry out the tasks assigned. The agents can also be categorized into two groups. The agents in the air quality group continuously monitoring and collecting air pollution data at the roadside or at the buildings vicinity to the roadways. While the agents in the emission pricing group analyze the pollution data provided to decide when to activate the emission pricing. The evaluation of the proposed framework and strategy is carried out in the microscopic traffic simulation environment. An illustrative case study of a new town in Singapore is adopted. It is shown that the proposed methodology yields promising results. The air pollution level at the roadside or vicinity buildings is reduced.