A Stochastic User Equilibrium Traffic Flow Allocation Method Based on Variable-Scale Gradient Correction

Abstract

The invention discloses a random user balanced traffic flow allocation method based on variable-scale gradient correction. The method iterates in the reduced variable space, and dynamically adjusts the gradient information of the objective function by generating a correction matrix in each step of iteration. The method It has a super-linear convergence speed and does not need to calculate any inverse matrix, which can greatly reduce the complexity of the operation; and compared with the gradient projection method, this method can improve the convergence speed in the later stage of the iteration and greatly save the CPU time in the later stage of the iteration, which is very suitable for Solve large-scale unconstrained optimization problems. By using the stochastic user equilibrium traffic flow allocation method based on variable-scale gradient correction, the solution efficiency can be improved and the calculation time can be saved.

Description

technical field [0001] The invention relates to the technical field of traffic allocation, in particular to a random user balanced traffic flow allocation method based on variable-scale gradient correction. Background technique [0002] The traffic assignment model is one of the most basic tools in the analysis and design of urban traffic systems, and plays a central role in urban traffic planning. The traffic distribution model is used to predict the link flow or path flow in the traffic network under the equilibrium state. The traffic allocation model can be divided into a deterministic user equilibrium allocation model (User Equilibrium referred to as UE) and a stochastic user equilibrium allocation model (Stochastic User Equilibrium referred to as SUE). A certain randomness requirement is more realistic and instructive. The stochastic user equilibrium allocation model assumes that people's perception of the travel time of the route is erroneous. On the SUE solution poi...

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide a random user balanced traffic flow allocation method based on variable-scale gradient correction. This method iterates in the reduced variable space, and each iteration generates a correction Matrix to dynamically adjust the gradient information of the objective function. This method has a super-linear convergence speed and does not need to calculate any inverse matrix, which can greatly reduce the complexity of the operation. Compared with the gradient projection method, this method can improve the convergence speed in the later stage of iteration. , and greatly save the CPU time in the later stage of the iteration, which is very suitable for solving large-scale unconstrained optimization problems

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