title: Forecasting the short-term passenger flow on high-speed railway with neural networks.
abstract: 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.
authors.affiliation: School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China.
authors.forename: Mei-Quan
authors.initials: MQ
authors.lastname: Xie
authors.affiliation: School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China.
authors.forename: Xia-Miao
authors.initials: XM
authors.lastname: Li
authors.affiliation: School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China.
authors.forename: Wen-Liang
authors.initials: WL
authors.lastname: Zhou
authors.affiliation: School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China.
authors.forename: Yan-Bing
authors.initials: YB
authors.lastname: Fu
mesh_terms: D005544:Forecasting / Q000379:methods*; D006801:Humans; D016571:Neural Networks, Computer; D011890:Railroads / Q000706:statistics & numerical data; D013997:Time Factors; D014195:Travel / Q000706:statistics & numerical data*