基于遗传灰色GM(1,1,ρ)模型的短时交通流量预测

Based on the genetic of gray GM (1,1,ρ) of the model short-term traffic flow prediction

为了提高短时交通流量的预测精度,本文根据短时交通流量的数据特征,结合灰色模型在短时预测方面的优势,利用遗传算法根据已知数据优化灰色新陈代谢GM(1,1,ρ)模型的背景值参数ρ,对实时采集的交通流量数据进行仿真分析,实验结果验证了该模型的准确性、实时性和有效性。

In order to improve the accuracy of short-term traffic flow forecasting, this paper proposed a new forecast model, the genetic grey model. The model adopted genetic algorithm t0 optimize the proper background value of ρ based on the known short-tern traffic flow data. The model also used a metabolic structure to update its parameters. The experiments indicate that the genetic grey model has a high accuracy for the short-term traffic flow forecasting, and the model is effective and real time.