Fuzzy Temporal Logic Based Railway Passenger Flow Forecast Model
نویسندگان
چکیده
منابع مشابه
Fuzzy Temporal Logic Based Railway Passenger Flow Forecast Model
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 t...
متن کاملVisualizing Passenger Flow in Railway Station Using Laser Scanners
Our goal in this study is to examine the feasibility for analyzing and visualizing passenger flows using laser scanners in a railway station. A network of laser scanners is located on different places and scan pedestrian's feet at a horizontal plane about 16 cm above the ground. Motion trajectories are extracted from the laser points on moving feet. They are analyzed to find the pattern of pass...
متن کاملPassenger Flow Forecast Algorithm for Urban Rail Transit
To exactly forecast the urban rail transit passenger flow, a multi-level model combining neural network and Kalman filter was proposed. Firstly, ELAN neural network model was introduced to implement a preliminary forecast of the passenger flow. Then the Kalman filter was used to correct the preliminary forecast results, so as to further improve the accuracy. Finally, in order to validate the pr...
متن کاملAn Artificial Neural Networks Approach to Forecast Short-term Railway Passenger Demand
This paper experiences a three-phrase back-propagation neural network approach to forecast short-term railway passenger demand. The first phase involves the selection of variables, the size of training data set, and the modification of stochastic outliers, under a specific origin-destination (O/D) pair of a given train service. In the second phase, in order to verify the robustness of developed...
متن کاملDesign of Dynamic Neural Networks to Forecast Short-term Railway Passenger Demand
This paper develops two dynamic neural network structures to forecast short-term railway passenger demand. The first neural network structure follows the idea of autoregressive model in time series forecasting and forms a nonlinear autoregressive model. In addition, two experiments are tested to eliminate redundant inputs and training samples. The second neural network structure extends the fir...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computational Intelligence and Neuroscience
سال: 2014
ISSN: 1687-5265,1687-5273
DOI: 10.1155/2014/950371