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Estimation of rail capacity using regression and neural network

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Abstract

Railroads have been experiencing traffic demand growth and increasing capacity constraints. Effective capacity management is thus crucial to the successful operation of railroads. The initial step in capacity management is to measure and monitor capacity and congestion. This research established a process for constructing parametric capacity models for single- and double-track railroads with simulation results using both regression and neural network (NN) techniques. Experimental results show that the NN models outperform the regression models in terms of predicting both single- and double-track capacity. Aside from traffic volume, average train speed and siding/crossover spacing were identified as the most sensitive factors relative to rail capacity. The use of this capacity evaluation tool can determine efficiency of current operations and can provide an objective basis to assess the need for capital expansion projects.

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Acknowledgments

The authors are grateful to Eric Wilson and Mark Dingler for their assistance in this research. This project was funded by National Science Council (NSC) of the Republic of China under Grant NSC 98-2221-E-002-114-MY3.

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Authors and Affiliations

  1. Department of Civil Engineering, National Taiwan University, Taipei, 10617, Taiwan

    Yung-Cheng Lai, Yung-An Huang & Hong-Yu Chu

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  1. Yung-Cheng Lai
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  2. Yung-An Huang
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  3. Hong-Yu Chu
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Correspondence to Yung-Cheng Lai.

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Lai, YC., Huang, YA. & Chu, HY. Estimation of rail capacity using regression and neural network. Neural Comput & Applic 25, 2067–2077 (2014). https://doi.org/10.1007/s00521-014-1694-x

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  • DOI: https://doi.org/10.1007/s00521-014-1694-x

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