基于GEP的高速公路通行费预测方法研究

高速公路通行费未来收入状况的预测对于高速公路运营管理、建设规划有着重要的指导意义。然而，通行费收入水平的变化受到多方面因素的影响，具有较强的非线性和复杂性，传统预测模型无法准确表达通行费收入的发展规律。本文针对复杂的高速公路通行费预测问题，建立了基于基因表达式编程算法(GEP)的高速公路通行费预测模型。该模型利用GEP算法建立通行费当前收入与历史数据之间复杂的函数关系，准确地刻画通行费收入随时间的发展规律。此外，针对节假日期间通行费减免政策的影响，提出了有效的修正模型。最后，本文采集了浙江沪杭甬高速公路股份有限公司等12家公司通行费收入的历史数据进行仿真实验，对比传统的ARIMA以及神经网络预测模型，结果充分验证了本文算法的有效性和准确性。

Research on freeway toll prediction method based on GEP

The prediction of the future income of highway toll has great guiding significance for the management and construction planning. However, the change of toll income is influenced by many factors. It has strong nonlinearity and complexity. The traditional prediction model cannot accurately express the development law of the toll income. In this paper, a highway toll prediction model based on gene expression programming algorithm (GEP) is established. The GEP algorithm is used to establish a complex functional relationship between current income and historical data, which accurately characterize the development rule of toll income over time. In addition, an effective correction model is proposed for the influence of toll reduction policies during holidays. Finally, this paper collects the historical data on the toll revenue of 12 companies such as shanghai-hangzhou-ningbo Expressway Co., Ltd. Compared with traditional ARIMA and neural network prediction model, and the results fully verify the effectiveness and accuracy of the proposed algorithm.