title: Fuzzy temporal logic based railway passenger flow forecast model.
abstract: Passenger flow forecast is of essential importance to the organization of railway transportation and is one of the most important basics for the decision-making on transportation pattern and train operation planning. Passenger flow of high-speed railway features the quasi-periodic variations in a short time and complex nonlinear fluctuation because of existence of many influencing factors. In this study, a fuzzy temporal logic based passenger flow forecast model (FTLPFFM) is presented based on fuzzy logic relationship recognition techniques that predicts the short-term passenger flow for high-speed railway, and the forecast accuracy is also significantly improved. An applied case that uses the real-world data illustrates the precision and accuracy of FTLPFFM. For this applied case, the proposed model performs better than the k-nearest neighbor (KNN) and autoregressive integrated moving average (ARIMA) models.
authors.affiliation: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China ; Subway Operation Technology Centre, Mass Transit Railway Operation Corporation LTD, Beijing 102208, China.
authors.forename: Fei
authors.initials: F
authors.lastname: Dou
authors.affiliation: State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China.
authors.forename: Limin
authors.initials: L
authors.lastname: Jia
authors.affiliation: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China ; State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China.
authors.forename: Li
authors.initials: L
authors.lastname: Wang
authors.affiliation: State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China.
authors.forename: Jie
authors.initials: J
authors.lastname: Xu
authors.affiliation: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China.
authors.forename: Yakun
authors.initials: Y
authors.lastname: Huang
mesh_terms: D005544:Forecasting; D017143:Fuzzy Logic; D006801:Humans; D008962:Models, Theoretical; D011890:Railroads; D013997:Time Factors; D014195:Travel