|Table of Contents|

Short-term traffic flow prediction model

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

Issue:
2012年04期
Page:
114-119
Research Field:
交通信息工程及控制
Publishing date:

Info

Title:
Short-term traffic flow prediction model
Author(s):
FAN Na1;  ZHAO Xiang-mo1;  DAI Ming1; 2;  AN Yi-sheng1
1. School of Information Engineering, Chang’an University, Xi’an 710064, Shaanxi, China; 2. China Transportation Telecommunication and Information Center, Beijing 100011, China
Keywords:
short-term traffic flow perdiction;  hybrid model;  nonparametric regression;  BP neural network;  fuzzy control
PACS:
U491.14
DOI:
-
Abstract:
A new hybrid prediction model including two single models of nonparametric regression model and BP neural network model was proposed according to the periodicity and randomness properties of short-term traffic flow. Relevant historical traffic flow data were used in nonparametric regression model to make the prediction result abtained from the databases matching proceeding fully illustrate the cyclical stability of traffic flow. Three-tier BP neural network model was used to reflect the dynamic and nonlinear characters of traffic flow. Fuzzy control algorithm was adopted to get the weight coefficient of each model. New mixed model was constituted by the two single models according to different weight coefficients. The prediction effect of hybrid prediction model was verified by the traffic flow data in 30 d from a certain section in Xi’an. Experimental result indicates that the average relative error of mixed model is 1.26%, and its maximum relative error is 3.53%, so the prediction accuracy of mixed model is obviously higher than two single models, and can accurately reflect the real situation of traffic flow. 6 tabs, 5 figs, 16 refs.

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Last Update: 2012-08-30