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.
Highlights
High-speed railway as a kind of large volume passenger transportation mode has been well developed in Europe and Japan and has been developing in China in an even larger scale and has been planned to develop in American continent
As one of the most important basics for the decision-making on highspeed railway transportation pattern and train operation planning, passenger flow forecast is of essential importance, and short-term passenger flow forecast is the key to the success of daily operation management
The short-term passenger flow forecast has played a key role in high-speed railway intelligent transportation system
Summary
High-speed railway as a kind of large volume passenger transportation mode has been well developed in Europe and Japan and has been developing in China in an even larger scale and has been planned to develop in American continent. In [2], four models were developed and tested for the freeway traffic flow forecasting problem They were the historical average, time-series, neural network, and nonparametric regression models. The ARIMA model is a linear combination of time-lagged variables and error terms, which has been widely applied in forecasting short-term traffic data such as traffic flow, travel time, and speed. In [12], the forecast model of railway short-term passenger flow based on BP neural network was established based on analyzing the principle of BP neural network and time sequence characteristics of railway passenger flow. Fuzzy temporal logic based passenger flow forecast model (FTLPFFM) is proposed in this paper. The characteristics of short-term high-speed railway passenger flow are vitally important to forecast model which is used to improve predictive performance of fuzzy k-nearest neighbor by comparing with other predictive methods in short-term high-speed railway passenger flow forecast.
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