基于灰关联与少数据云推理的短时交通流预测模型

oncerning the fuzziness and randomness characteristics and quasi-periodic regularity in short-term traffic flow, this study develops a short-term traffic flow forecasting model using grey relational analysis and few data cloud inference. Firstly, the most similar sequence in the history is extracted by gray relational analysis; Then, the backward cloud algorithm of few data is developed; Finally, the prediction cloud is generated by a one-dimensional cloud inference of historical and current information. The results show that this model is used in forecasting short-term traffic flows and the accuracy is considerably improved. This model solves the confirmation to parameters of forward cloud under few data conditions, reducing the data processing workload and extending the application scope of the traditional cloud model.