基于BP神经网络的机场安检旅客流量预测模型

机场安检服务资源智能分配及调度是提高机场旅客服务水平及运营效率的有效途径之一,而准确的机场安检旅客流量预测则是实现机场安检服务资源动态分配及调度的前提。以天津机场安检旅客流量的历史数据为研究对象,利用BP神经网络算法建立机场安检旅客流量预测模型,并将该预测模型通过天津机场实际旅客流量进行验证。结果表明:该基于BP神经网络的机场安检旅客流量预测模型的预测精度可达90%以上,证明其具有较高的预测精度,能很好地应用到机场安检流量预测中,为机场运营者动态调度安检服务资源提供高效的解决方案。

The Prediction Model Based on BP Neural Network about Airport Security-check Passenger Flow

Intelligent allocation and scheduling of airport security-check service resources is one of the effective ways to improve passenger service level and operational efficiency within the airport, while the accurately prediction about the security passenger traffic is the prerequisite for dynamic allocation and scheduling. This paper takes the historical passenger data at Tianjin airport security inspection as the research object, and puts forward a prediction method based on BP neural network so as to establish a prediction model of security-check passenger flow. Besides the proposed model by this paper is verified by the actual passenger flow of Tianjin airport, as we can know from the comparison results, the accuracy of the proposed algorithm can reach to above ninety percent, So the BP neural network prediction model behaves with higher forecasting accuracy, can be well applied to the security-check flow prediction in the airport terminal, which can support a high efficiency solution for the airport operators to dynamically allocate security-check services resources.