@article{ref1,
title="Fuzzy temporal logic based railway passenger flow forecast model",
journal="Computational intelligence and neuroscience",
year="2014",
author="Dou, Fei and Jia, Limin and Wang, Li and Xu, Jie and Huang, Yakun",
volume="2014",
number="",
pages="e950371-e950371",
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.<p /> <p>Language: en</p>",
language="en",
issn="1687-5265",
doi="10.1155/2014/950371",
url="http://dx.doi.org/10.1155/2014/950371"
}