TY  - JOUR
PY  - 2014//
TI  - Fuzzy temporal logic based railway passenger flow forecast model
JO  - Computational intelligence and neuroscience
A1  - Dou, Fei
A1  - Jia, Limin
A1  - Wang, Li
A1  - Xu, Jie
A1  - Huang, Yakun
SP  - e950371
EP  - e950371
VL  - 2014
IS  - 
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 1687-5265
UR  - http://dx.doi.org/10.1155/2014/950371
ID  - ref1
ER  -