Carrying Capacity Of Land Resources Based On Road Transport System Optimization Model And Algorithm

Since the20th century, the rapid development of China’s transport sector, traffic had remarkable achievements in all aspects, but various constraints brought about contradictions can not be ignored as the development, such as underdeveloped infrastructure, traffic legal system lags behind, the efficient use of transport resources is low, industry issues such as excessive consumption of resources. To fundamentally ease the traffic caused by the development of these resource constraints and environmental and ecological pressure, thus taking into account the well-off society transport development objectives, we must adhere to the scientific development concept as a guide, combined with the development of resource-saving transport requirements, to achieve coordinated sustainable road transport.This paper describes the case of road traffic land and the significance of the research, based on the main land carrying capacity for road traffic and road traffic network optimization algorithms and models studied. First define the concept of road traffic carrying capacity of land and road traffic within the region proposed land carrying capacity evaluation system of principles, and then build a regional highway traffic carrying capacity of land resources evaluation system and ultimately chose fuzzy analytic hierarchy process road traffic carrying capacity of land resources analysis, modeling expert scoring method is considered to be the right value. Related indicators in Wuhan from2002to2011data analysis shows:Select Fuzzy AHP evaluation of regional road traffic carrying capacity of land resources is reasonable and Wuhan City, the bearing capacity of road transport value increased year by year except2006.Then set up both extremes highway development model, and propose ideas to solve linear programming ideas, that satisfy the constraints in the case of continuous adjustment sections in order to achieve the maximum amount of traffic the optimal path. Then established a system impedance and land development costs and the cost of the objective function as the upper model, to enable the system to achieve optimal in the transport hub and line investment allocation, consider the funding constraints limiting conditions and selecting a row investment; investment allocation based on the upper, lower road transport route choice models to meet user equilibrium model, and finally the use of genetic algorithm to solve this level programming model. Road through simulation analysis illustrates this model and the algorithm is feasible.