Research On Evalution Of Port Dredging Highway Traffic Status

The mobility of the port-dredging highway determines the course of survival,competition and development of the port area.Accurately grasping its traffic status is a reliable basis for traffic managers to make decisions,and can promote the sustainable development of the port area.Traditional qualitative evaluation methods are highly subjective and quantitative evaluation methods are computationally complex,making it difficult to obtain scientific evaluation results.The BP neural network technology based on learning algorithm not only overcomes the shortcomings of strong subjectivity of qualitative evaluation method,but also solves the shortcomings of complex calculation and poor operability of quantitative evaluation method,so it can more accurately evaluate highway traffic conditions.The heuristic global optimization ability of particle swarm optimization algorithm is used to optimize and improve,but most of the methods of particle swarm optimization to remedy defects are adjusted by the inertia weight coefficient W associated with the number of iterations,and the inertia weight W is seldom improved from the perspective of adaptation value.Therefore,this paper comprehensively considers that the combination of fitness value and iteration number will make the particle more optimized and updated,and considers changing the adjustment factor value will improve the inertia weight coefficient W,so as to establish a better evaluation model of BP neural network optimized by particle swarm optimization algorithm,in order to accelerate the convergence speed and improve the prediction accuracy.The evaluation method is supplemented in theory,and the traffic status of dredging highway is accurately evaluated in practice.Firstly,combined with the selection principles of evaluation indicators and the research results of previous scholars,the indicators under the two dimensions of road network operation quality and structural performance are mainly considered.By comprehensively considering the traffic status of dredging highway network and combining with the selection principle of evaluation indexes,some indexes with inclusion or cross relationship are removed,a scientific and objective evaluation index system of dredging highway network traffic status is established.Secondly,an evaluation model of BP neural network based on improved inertia weight coefficient W was proposed.Dynamic adjustment is made according to the distance change between the iterative particle fitness value and the particle optimal fitness value,and the inertia weight coefficient W of each particle is updated in time through the local adjustment factor α value for particle swarm optimization;then,the representative traffic status evaluation index values were screened from the two target layers of structural performance and operation quality as the input,and the traffic status evaluation result grade is the output.The comparison curve of iterative adaptive value and error comparison curve were obtained by matlab software,and the prediction results under different α values were obtained,through the comparison of the curves,the mean absolute error,mean square error and mean relative error are used as evaluation criteria to compare the influence of different adjustment factor α on the prediction accuracy of the model,and build a more accurate evaluation model.Finally,this paper takes the dredging port highway of Tianjin Port as the research object and conducts empirical research.By collecting the traffic data of Tianjin port-dredging highway,the improved model and algorithm designed in this paper are used to evaluate the traffic status of the highway.The research shows that the model established in this paper has certain rationality and scientificity,and the solution algorithm is also feasible.