|Table of Contents|

Forecasting method of expressway traffic volume based on NPCA and GA-RBF(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2018年03期
Page:
210-217
Research Field:
交通运输规划与管理
Publishing date:

Info

Title:
Forecasting method of expressway traffic volume based on NPCA and GA-RBF
Author(s):
LEI Ding-you;  MA Qiang;  XU Xin-ping;  LIU Qing-yi
School of Traffic and Transportation Engineering, Central South University, Changsha 410075, Hunan, China
Keywords:
traffic volume;  forecasting method;  expressway;  RBF neural network;  nonlinear principal component analysis;  genetic algorithm
PACS:
U491
DOI:
-
Abstract:
To improve expressway traffic volume forecasting accuracy, on the basis of the features of expressway traffic volume which was obviously nonlinear and affected by many factors, a forecasting method based on the nonlinear principal component analysis and GA-optimized RBF neural network(NPCA-GA-RBF)method was presented. The main influencing index of expressway traffic volume was determined, and the nonlinear principal component analysis method was used to reduce the dimensionality and correlation of influencing factors. The original multiple indexes were replaced by a few principal components to simplify the structure of the neural network. The GA was used to optimize the parameters of the RBF neural network and further improve the forecasting accuracy of traffic volume. An expressway in Puer City was taken as the example to verify the traffic volume forecasting method. Analysis result shows that the average relative errors of the GA-RBF and NPCA-GA-RBF methods in two experiments are 1.62%, 3.53% and 2.27%, 3.32% smaller than that of the RBF model, respectively, so the GA-optimized RBF neural network improves the traffic volume forecasting accuracy of the RBF method. Compared to the GA-RBF method, the average relative error of the NPCA-GA-RBF method decreases by 1.91% and 1.05% in two experiments, respectively, its traffic volume forecasting result is closer to the actual traffic volume and more reliable, which demonstrates that the nonlinear principal component analysis eliminates the correlation of the indexes to further improve the traffic volume forecasting accuracy and reduces the forecasting complexity of traffic volume. So, the NPCA-GA-RBF method has higher traffic volume forecasting accuracy, can provide the reliable decision basis for expressway operations management, and satisfies the objective requirements of the reasonable management for expressway. 3 tabs, 6 figs, 27 refs.

References:


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Last Update: 2018-07-14