Xie Mei-Quan, Li Xia-Miao, Zhou Wen-Liang, Fu Yan-Bing
School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China.
Comput Intell Neurosci. 2014;2014:375487. doi: 10.1155/2014/375487. Epub 2014 Nov 4.
Short-term passenger flow forecasting is an important component of transportation systems. The forecasting result can be applied to support transportation system operation and management such as operation planning and revenue management. In this paper, a divide-and-conquer method based on neural network and origin-destination (OD) matrix estimation is developed to forecast the short-term passenger flow in high-speed railway system. There are three steps in the forecasting method. Firstly, the numbers of passengers who arrive at each station or depart from each station are obtained from historical passenger flow data, which are OD matrices in this paper. Secondly, short-term passenger flow forecasting of the numbers of passengers who arrive at each station or depart from each station based on neural network is realized. At last, the OD matrices in short-term time are obtained with an OD matrix estimation method. The experimental results indicate that the proposed divide-and-conquer method performs well in forecasting the short-term passenger flow on high-speed railway.
短期客流预测是交通系统的重要组成部分。预测结果可用于支持交通系统的运营和管理，如运营规划和收益管理。本文提出了一种基于神经网络和起讫点（OD）矩阵估计的分治方法，用于预测高速铁路系统的短期客流。该预测方法包括三个步骤。首先，从历史客流数据中获取到达各车站或从各车站出发的乘客数量，本文中这些数据即OD矩阵。其次，基于神经网络实现对到达各车站或从各车站出发的乘客数量的短期客流预测。最后，采用OD矩阵估计方法获得短期时间内的OD矩阵。实验结果表明，所提出的分治方法在预测高速铁路短期客流方面表现良好。
