Multi-Constraint Energy Consumption Optimization Model Of Urban Passenger Traffic Structure

In recent years, the urbanization process of our country continues to accelerate, economy is rapid developing, car ownership is increasing gradually, traffic congestion is getting worse than before. The urban traffic congestion intensifies bring a lot of negative effects, it increase the greenhouse gas emissions, it reduce the transportation efficiency and improve the proportion of transportation in the total energy consumption. In this paper, we select the urban passenger transportation structure as the research object, set reducing energy consumption as the goal. We analyze the issues from three angles of way to travel of residents, carbon emissions and the reach ability of residents travel. Then optimize the urban passenger transportation structure in order to reduce energy consumption and improve transport efficiency. Finally, make commendations for improving the situation of urban passenger transport.In this paper, firstly, the urban passenger traffic situation at home and abroad is summarized. We analyze various the passenger traffic structure optimization models specifically depending on the difference of passenger transport mode in various places. Then according to the past experience of passenger traffic structure optimization, constructing the lowest energy consumption optimization model about the urban passenger traffic structure. Secondly, through comparative and analyze of existing urban passenger transportation structure optimization method. We make the lowest energy consumption of passenger traffic as target. Making the passenger turnover, total carbon emissions and transport efficiency as constraints. We solve the model through artificial fish swarm algorithm. Refer to the optimization results of this mode as the decision basis when adjusting the proportion of urban passenger transport structure. Finally, select Harbin passenger traffic as the research object. Obtain the required data from the relevant transport sector, statistical yearbook and field survey as reference data for solving the mathematical model. We can get the results from the model solution. We need to increase the overall proportion of urban bus transport, reduce the sharing ratio of taxi, appropriately increase private cars modal share, develop the rail vigorously, stringent restrictions on the use of motorcycles, reduce the proportion of slow transport travel. But it is undeniable, in the real life, if the ambient conditions and the time are allowed. Slow transport is still to be adequately promoted.