Real-Time Corrected Traffic Correlation Model for Traffic Flow Forecasting

Abstract EN

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.