An Affinity Propagation-Based Clustering Method for the Temporal Dynamics Management of High-Speed Railway Passenger Demand

Wenxian Wang,Yongxiang Zhang,Qian Zhu,Tie Shi,Nirajan Shiwakoti

doi:10.1155/2021/7497094

Abstract

The number of passengers in a high-speed railway line normally varies significantly by the time periods, such as the peak and nonpeak hours. A reasonable classification of railway operation time intervals is essential for an adaptive adjustment of the train schedule. However, the passenger flow intervals are usually classified manually based on experience, which is subjective and inaccurate. Based on the time samples of actual passenger demand data for 365 days, this paper proposes an affinity propagation (AP) algorithm to automatically classify the passenger flow intervals. Specifically, the AP algorithm first merges time samples into different categories together with the passenger transmit volume of the stations, which are used as descriptive variables. Furthermore, clustering validity indexes, such as Calinski–Harabasz, Hartigan, and In-Group Proportion, are employed to examine the clustering results, and reasonable passenger flow intervals are finally obtained. A case study of the Zhengzhou-Xi’an high-speed railway indicates that our proposed AP algorithm has the best performance. Moreover, based on the passenger flow interval classification results obtained using the AP algorithm, the train operation plan fits the passenger demand better. As a result, the indexes of passenger demand satisfaction rate, average train occupancy rate, and passenger flow rate are improved by 7.6%, 16.7%, and 14.1%, respectively, in 2014. In 2015, the above three indicators are improved by 5.7%, 18.4%, and 14.4%, respectively.

Highlights

With the extension and integration of China’s high-speed railway network, it is becoming the preferred mode of travel
One of the most important tasks for high-speed railway passenger transport management is to adjust the line plan according to the characteristics of the fluctuations of the passenger flow over a year, so that the line plan is adapted to the passenger demand. erefore, it is obvious that the annual passenger flow interval classification serves as the foundation for the adjustment of the line plan [1, 2]. e usual method used by the railway bureau is to classify annual passenger flow intervals according to the subjective experience of the engineering and technical personnel
Clustering validity indexes are adopted to evaluate which result generated by the clustering algorithm is optimal and the number of clusters corresponding to the optimal result is taken as the optimal clustering number. e output of the affinity propagation (AP) algorithm is a series of clustering results that contain different numbers of clusters; the effectiveness of these clustering results needs to be evaluated

Summary

Introduction

With the extension and integration of China’s high-speed railway network, it is becoming the preferred mode of travel. One of the most important tasks for high-speed railway passenger transport management is to adjust the line plan according to the characteristics of the fluctuations of the passenger flow over a year, so that the line plan is adapted to the passenger demand. E usual method used by the railway bureau is to classify annual passenger flow intervals according to the subjective experience of the engineering and technical personnel. It is very similar to the time-of-day (TOD) interval identification problem when developing traffic signal timing plans.

Results

Discussion

Conclusion