Abstract
The focus of this study is to explore the statistical distribution models of high-speed railway (HSR) train delays. Based on actual HSR operational data, the delay causes and their classification, delay frequency, number of affected trains, and space–time delay distributions are discussed. Eleven types of delay events are classified, and a detailed analysis of delay distribution for each classification is presented. Models of delay probability delay probability distribution for each cause are proposed. Different distribution functions, including the lognormal, exponential, gamma, uniform, logistic, and normal distribution, were selected to estimate and model delay patterns. The most appropriate distribution, which can approximate the delay duration corresponding to each cause, is derived. Subsequently, the Kolmogorov–Smirnov (K–S) test was used to test the goodness of fit of different train delay distribution models and the associated parameter values. The test results show that the distribution of the test data is consistent with that of the selected models. The fitting distribution models show the execution effect of the timetable and help in finding out the potential conflicts in real-time train operations.
Highlights
Since 2008, China’s high-speed railway (HSR) has grown significantly owing to its advantages over other modes of transportation; these include large transport capacity, low energy consumption, and high degree of punctuality
The focus of this study is to explore the statistical distribution models of high-speed railway (HSR) train delays
Guangzhou–Shenzhen High-Speed Railway (G–S HSR): the line of 175.1 km runs from South Guangzhou station to North Shenzhen station and has five stations;
Summary
Since 2008, China’s high-speed railway (HSR) has grown significantly owing to its advantages over other modes of transportation; these include large transport capacity, low energy consumption, and high degree of punctuality. Using the actual train operation data, this paper performs a statistical distribution analysis of the HSR train delays, including the various distribution functions of train delays caused by different delay events. The main contributions include (1) preliminary analysis of the causes of delays and the overall situation of HSR train delays as a foundation for further studies and (2) establishing distribution models and parameter estimations of delays to serve as the basis for timetabling and simulation studies of train operations. From the historical operation data of the Dutch railway, Yuan [11] found that the distribution of train arrival and departure delay fit a lognormal distribution curve. Studies of HSR train delays in China would contribute to an improvement in the management of train operation
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