Passenger Flow Forecast in Guangzhou-Shenzhen (Hong Kong) High Speed Railway

ICTE 2011 ◽  
2011 ◽  
Author(s):  
Hongguo Shi ◽  
Hanying Guo
Keyword(s):  
Hong Kong ◽  
High Speed ◽  
High Speed Railway ◽  
Passenger Flow ◽  
Flow Forecast

scholarly journals Fuzzy Temporal Logic Based Railway Passenger Flow Forecast Model

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Fei Dou ◽  
Limin Jia ◽  
Li Wang ◽  
Jie Xu ◽  
Yakun Huang
Keyword(s):  
Temporal Logic ◽  
High Speed ◽  
Nearest Neighbor ◽  
Forecast Accuracy ◽  
Forecast Model ◽  
Railway Transportation ◽  
Real World Data ◽  
High Speed Railway ◽  
Passenger Flow ◽  
Flow Forecast

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 thek-nearest neighbor (KNN) and autoregressive integrated moving average (ARIMA) models.

scholarly journals A Hybrid Temporal-Spatio Forecasting Approach for Passenger Flow Status in Chinese High-Speed Railway Transport Hub

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Zhengyu Xie ◽  
Limin Jia ◽  
Yong Qin ◽  
Li Wang
Keyword(s):  
High Speed ◽  
Rapid Development ◽  
Distribution Center ◽  
Railway Transport ◽  
High Speed Railway ◽  
Safety Hazard ◽  
Passenger Flow ◽  
Potential Safety ◽  
Correlation Degree ◽  
The Status

With the rapid development of high-speed railway in China, high-speed railway transport hub (HRTH) has become the high-density distribution center of passenger flow. In order to accurately detect potential safety hazard hidden in passenger flow, it is necessary to forecast the status of passenger flow. In this paper, we proposed a hybrid temporal-spatio forecasting approach to obtain the passenger flow status in HRTH. The approach combined temporal forecasting based on radial basis function neural network (RBF NN) and spatio forecasting based on spatial correlation degree. Computational experiments on actual passenger flow status from a specific bottleneck position and its correlation points in HRTH showed that the proposed approach is effective to forecast the passenger flow status with high precision.

Key Issues on Train Line Planning for Beijing-Shanghai High Speed Railway

2010 ◽  
Author(s):  
L. Nie ◽  
D. B. Fei ◽  
S. D. Zhou ◽  
H. L. Fu ◽  
L. Tong
Keyword(s):  
High Frequency ◽  
High Speed ◽  
Travel Demand ◽  
Time Window ◽  
Long Distance ◽  
Line Planning ◽  
High Speed Railway ◽  
Passenger Flow ◽  
Train Operation ◽  
Service Plan

The Beijing-Shanghai High speed railway line (Hereinafter referred to as “Jing-Hu HSL”) is one of the most important railway lines in the Chinese rapid passenger transportation network and will be put into operation at the end of 2011. Train line planning directly reflects the quality and competition ability of train services. The characteristics of operational conditions and passenger flow of this corridor HSL bring about a few new issues on train line planning like night train operation, train OD sets, cyclic operation, and train stop schedule. For the first issue, a large amount of long distance travel demand put forward the demand for night services, which causes great conflict with the time-window for maintenance work. The confliction can be solved by harmoniously utilizing the parallel lines. For the second one, in view of the differences of technical and economic factors between HSL and conventional railways, high frequency and medium-long distance train will dominate HSL’s train service plan rather than low frequency and long distance trains on conventional railways. Thus, part of long-distance passenger flow has to transfer at some stations. Considering the whole possible ODs over Jing-Hu HSL and the transfer condition of related stations, the optimal OD sets and corresponding transfer plan is suggested. High frequency makes HSL possible to operate trains cyclically to improve service quality. However, with too many train ODs and some special trains, e.g. night train, an incomplete cyclic train operation mode is more practical. The ODs which can provide cyclic service for Jing-Hu HSL need to be identified. For the last issue, although non-stop long-distance train is a very popular kind of service in China, it should be reconsidered for Jing-Hu HSL line because of massive intercity travel demand and regular stops required. Each of the above issues is very complex. What is more, they have close relationship between each other. Due to limited space, the methods used to solve these issues are given in conceptual way rather than detailed description of mathematical model. The research paves the way for future integration study to design an efficient, economic, convenient, and regular train service plan for Jing-Hu HSL.

Short-Time Fluctuation Characteristic and Combined Forecasting of High-Speed Railway Passenger Flow Based on EEMD

2013 ◽  
Vol 409-410 ◽  
pp. 1071-1074
Author(s):  
Xiu Shan Jiang ◽  
Rui Feng Zhang ◽  
Liang Pan
Keyword(s):  
Support Vector Machine ◽  
Empirical Mode Decomposition ◽  
High Speed ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
High Speed Railway ◽  
Passenger Flow ◽  
Mode Decomposition ◽  
Railway Passenger ◽  
Short Term Forecasting

Take Wuhan-Guangzhou high-speed railway for example. By adopting the empirical mode decomposition (EMD) attempt to analyze mode from the perspective of volatility of high speed railway passenger flow fluctuation signal. Constructed the ensemble empirical mode decomposition-gray support vector machine (EEMD-GSVM) short-term forecasting model which fuse the gray generation and support vector machine with the ensemble empirical mode decomposition (EEMD). Finally, by the accuracy of predicted results, explains the EEMD-GSVM model has the better adaptability.

Optimal Allocation of High-Speed Railway Mobile Equipment Based on the Passenger Flow

2014 ◽  
Vol 505-506 ◽  
pp. 471-476
Author(s):  
Qing Jie Zheng ◽  
Bao Ming Han ◽  
Hua Li
Keyword(s):  
High Speed ◽  
Optimal Allocation ◽  
Allocation Problem ◽  
Load Factor ◽  
Transport Capacity ◽  
High Speed Railway ◽  
Passenger Flow ◽  
The Relationship ◽  
Passenger Load Factor ◽  
Multiple Units

Based on the relationship between passenger flow and demand of electric multiple units (EMU), a new methodology to calculate the EMU demand and allocate the EMU is proposed, which can meet the demand of passenger as well as avoid the waste of transport capacity. An offline allocation plan of EMU is designed by analyzing the fluctuation in passenger flow, passenger average haul distance, passenger load factor and so on. Using the methodology, the EMU allocation problem is solved through program, which is used in Beijing-Shanghai High-speed Railway to obtain the EMU allocation plan.

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