Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2015, Article ID 348036, 7 pages
http://dx.doi.org/10.1155/2015/348036
Research Article

Real-Time Corrected Traffic Correlation Model for Traffic Flow Forecasting

1Institute of Transportation Engineering, Tsinghua University, Beijing 100084, China
2National Defense Transportation Department, Military Transportation University, Tianjin 300161, China

Received 2 August 2014; Accepted 27 February 2015

Academic Editor: Emilio Insfran

Copyright © 2015 Hua-pu Lu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper focuses on the problems of short-term traffic flow forecasting. The main goal is to put forward traffic correlation model and real-time correction algorithm for traffic flow forecasting. Traffic correlation model is established based on the temporal-spatial-historical correlation characteristic of traffic big data. In order to simplify the traffic correlation model, this paper presents correction coefficients optimization algorithm. Considering multistate characteristic of traffic big data, a dynamic part is added to traffic correlation model. Real-time correction algorithm based on Fuzzy Neural Network is presented to overcome the nonlinear mapping problems. A case study based on a real-world road network in Beijing, China, is implemented to test the efficiency and applicability of the proposed modeling methods.