机场巴士运行过程子空间建模与优化

针对机场巴士运行过程影响因素复杂、难以预测运行时间的问题,建立了一种基于子空间辨识算法的机场巴士运行时间预测模型.首先根据运行过程中所产生的多源大数据,考虑不同时段的乘坐人数、发车间隔、道路拥挤度等因素,建立机场巴士运行过程状态空间模型;然后提取适合描述机场巴士运行过程的特征变量作为模型的输入输出,通过子空间辨识方法对模型进行求解;最后以首都机场巴士的一条实际运营路线作为案例进行仿真分析.计算结果表明,该模型预测平均绝对百分误差和均方误差分别为2.25％和4.77,表现均好于传统的BP神经网络预测模型和最小二乘法辨识模型,具有较好的预测精度,有一定的实际应用价值.

Subspace modeling and optimization for airport bus operation process

To solve the problem that the influencing factors of the airport bus operation process are complicated and the operation time is difficult to predict, this paper established an airport bus operation time prediction model based on the subspace identification algorithm. Firstly, based on the multi-source big data generated during the operation process, considering the number of passengers, departure interval, road congestion and other factors at different times, it established the state space model of the airport bus operation. Then it extracted the characteristic variables suitable for describing the operation process of the airport bus as the input and output of the model, and solved the model by subspace identification method. Finally, this paper took an actual operating route of the Capital Airport Bus as a case for simulation analysis. The calculation results show that the mean absolute percentage error and mean square error of the model are 2.25% and 4.77, respectively, which are better than the traditional BP neural network prediction model and the least square identification model. The model has good prediction accuracy and has certain practical application value.