Regression model for daily passenger volume of high-speed railway line under capacity constraint

Luo, Yongji; Liu, Jun; Sun, Xun; Lai, Qingying

doi:10.1007/s11771-015-2908-9

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…The point on the tread surface, which is 70 mm from the inside of the wheel, is defined as the base point, and the circle formed by the base point along the circumference of the wheel is called the rolling circle [20]. At present, the diameter of the rolling circle is manually measured using a wheel diameter gauge on the spot, and the tread wear is obtained by subtracting the current wheel diameter from the wheel diameter stored in the standard system.…”

Section: Methodsmentioning

confidence: 99%

Prediction of train wheel diameter based on Gaussian process regression optimized using a fast simulated annealing algorithm

Fan

et al. 2019

PLoS ONE

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An algorithm to predict train wheel diameter based on Gaussian process regression (GPR) optimized using a fast simulated annealing algorithm (FSA-GPR) is proposed in this study to address the problem of dynamic decrease in wheel diameter with increase in mileage, which affects the measurement accuracy of train speed and location, as well as the hyper-parameter problem of the GPR in the traditional conjugate gradient algorithm. The algorithm proposed as well as other popular algorithms in the field, such as the traditional GPR algorithm, and GPR algorithms optimized using the artificial bee colony algorithm (ABC-GPR) or genetic algorithm (GA-GPR), were used to predict the wheel diameter of a DF11 train in a section of a railway during a period of major repairs. The results predicted by FSA-GPR was compared with other three algorithms as well as the real measured data from RMSE, MAE, R2 and Residual value. And the comparisons showed that the predictions obtained from the GPR optimized using FSA algorithm were more accurate than those based on the others. Therefore, this algorithm can be incorporated into the vehicle-mounted speed measurement module to automatically update the value of wheel diameter, thereby substantially reducing the manual work entailed therein and improving the effectiveness of measuring the speed and position of the train.

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Section: Methodsmentioning

confidence: 99%

Prediction of train wheel diameter based on Gaussian process regression optimized using a fast simulated annealing algorithm

Fan

et al. 2019

PLoS ONE

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“…Therefore, there are 10 train legs and 55 OD pairs on the train. Such a number of stopping stations is very common for high‐speed trains on the Beijing–Shanghai high‐speed railway line (Luo et al., 2015). To see the performances of different controls under different train capacity, the number of seats on the train is taken from the collection

\{800,850,900,\text{\ensuremath{\cdots}},1200\}

.…”

Section: Numerical Experiencesmentioning

confidence: 99%

Hybrid nesting control strategy for passenger railway with one‐seat‐one‐ticket restriction

Luo

Zhang

Yan

et al. 2022

Int Trans Operational Res

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Nesting control is one of the most prevalent quantity‐based controls for the revenue management problem. A popular nesting control strategy for multi‐resource problem is the virtual nesting control, which sets nested booking limits on each resource. However, this control was originally developed for the airline and cannot be directly used in the passenger railway with one‐seat‐one‐ticket restriction. Therefore, this paper develops a new nesting control that is applicable to the railway. The proposed control can nest the capacity over different fare classes and origin‐destination pairs, which overcomes the shortcomings of existing railway booking‐limit controls. Numerical experiments are conducted in various scenarios to evaluate the performance. The results show that the hybrid nesting control outperforms the others in all situations. In addition, the revenue improvement increases with the randomness of demand and discount percentage.

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“…In order to optimize the utilization of this network and ensure its reliability, it is inevitable to establish additional procedures for strengthening the cooperation on alignment of international train journeys for freight trains among infrastructure managers [9][10][11].…”

Section: Specific Features Of Selected Corridors In the Selected Countrymentioning

confidence: 99%

Draft Methodology to Specify the Railway Sections Capacity

Abramović

Zitrický

Meško

2017

LOGI – Scientific Journal on Transport and Logistics

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This paper presents the individual parameters of the selected railway corridors in the selected country. Particularly, it describes the specific features of selected corridors in this country, summarizes their capacity, as well as proposes the methodology in order to specify the capacity of given railway section. The outcome is represented by the calculation of the appropriate railway section capacity implemented on the particular railway corridor in the selected EU country.

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Regression model for daily passenger volume of high-speed railway line under capacity constraint

Cited by 7 publications

References 19 publications

Prediction of train wheel diameter based on Gaussian process regression optimized using a fast simulated annealing algorithm

Prediction of train wheel diameter based on Gaussian process regression optimized using a fast simulated annealing algorithm

Hybrid nesting control strategy for passenger railway with one‐seat‐one‐ticket restriction

Draft Methodology to Specify the Railway Sections Capacity

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