A Method for Estimating Regional Vehicle Density Based on Dynamic Sampling Mechanism and RBF Neural Network

Abstract

The invention discloses a regional vehicle density estimation method based on a dynamic sampling mechanism and an RBF neural network, and the method comprises the steps of preliminarily constructing avehicle density database according to the sampling information of the vehicle density in a target region; comparing the estimation values of the vehicle density for many times, taking the stored sampling data as an input variable of the RBF neural network, defining a group of activation functions, and establishing an estimation model based on the RBF neural network; applying the Kalman filteringalgorithm to an RBF neural network estimation algorithm; estimating the vehicle density of any point in the target area according to the weight coefficient of each neural network and the correlation function of the vehicle density in the target area; and finally, dynamically estimating the vehicle density in the target area by judging whether the estimation result meets the task requirement or not. The method has the advantages of higher estimation efficiency, lower operation load and higher estimation precision, can effectively estimate the time-varying vehicle density in the target area in real time, and has wide application space and practical range.

Description

Technical field [0001] The invention belongs to the technical field of spatial information distribution, and in particular relates to a method for estimating regional vehicle density based on a dynamic sampling mechanism and an RBF neural network. Background technique [0002] With the rapid development of the social economy, the number of cars has grown rapidly year by year. The resulting traffic congestion problem has become more serious. As vehicle density information is the most direct indicator of road traffic conditions, the premise of alleviating traffic congestion is to grasp the vehicle density information in the target area. After mastering the vehicle density information in the area, the traffic in the target area can be effectively and promptly controlled and diverted, so as to alleviate congestion and avoid traffic accidents. [0003] At present, people generally use traditional estimation methods such as iterative least squares or Gaussian estimation to predict the ...

AI Technical Summary

Problems solved by technology

Such a sampling mode will waste the computing resources of the estimation system and increase the computing cost during the time period when the vehicle density information changes slowly.

In the case of rapid changes in vehicle density information, the estimation results and follow-up effect are poor, and often cannot reflect the real-time traffic status in time.

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