Research On Consumers' Driving Routes In The Simulation Of Multi-Agent Based New Energy Vehicles Diffusion

Under both pressures of energy crisis and environmental, developing new energy vehicle is indispensable to our country's sustainable development. The relationship between new energy vehicles diffusion and their fuel-filling station construction is just like a "chicken and egg" problem. In addition, technology learning of new energy vehicles is hard to start spontaneously. Therefore, promotion of new energy vehicles usually requires governments to establish demonstration projects as seeds of technology learning. Up to now, diffusion models of new energy vehicles which integrate GIS and multi-agent based modeling concept have already taken shape. But most of these studies are on the basis of abstract, without actual data like geographic, demographic data supporting the initial spatial distribution of the model. Also the diffusion model not based on actual data can't provide support for policy-making.Consumers' driving route is one of the key factors in new energy vehicles diffusion simulation. In daily life, consumers'driving routes differ from person to person, how to simulate all consumers'driving routes in a region (like a city) becomes a problem. Based on the existing statistical data, this paper proposes solutions to consumers' driving routes, which include both routes of commute and routes leading to hot spots (places of leisure like supermarket, shopping mall, etc.). Because commute is the main purpose of citizens'travel, routes of commute become main focus of this paper. Take Shanghai as an example, this study simulates consumers' driving routes in Shanghai, and prove the feasibility of commute routes in Shanghai. In addition, taking jobs and housing balance into consideration, this paper put forward two other scenarios of jobs and housing spatial distribution. Simulate consumers' driving routes under different scenarios, and compare different results of new energy vehicles diffusion under these three scenarios. Finally recommendations for policy are given to government.