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A Model for optimizing railway alignment considering bridge costs, tunnel costs, and transition curves

Ghoreishi, B ; Sharif University of Technology | 2019

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  1. Type of Document: Article
  2. DOI: 10.1007/s40864-019-00111-5
  3. Publisher: Springer , 2019
  4. Abstract:
  5. Owing to wide-ranging searches (there are various alignments between two points) as well as complex and nonlinear cost functions and a variety of geometric constraints, the problem of optimal railway alignment is classified as a complex problem. Thus, choosing an alignment between two points is usually done based on a limited number of alignments designed by experts. In recent years, the study of railway alignment optimization has shown the importance of optimization and the introduction of various algorithms and their usefulness in solving different problems. It is expected that applying meta-heuristic optimization algorithms such as methods based on swarm intelligence can lead to better alignments. In this study, we tried to modify models based on previous studies in order to design and develop a model based on a single framework to provide three-dimensional optimization of alignments applicable in the real world. To obtain this, the particle swarm algorithm is used and a geographic information system is incorporated as a means of search in three-dimensional space. In particular, the cost function used in previous studies considering the costs related to structures (bridges and tunnels) are improved regarding hydraulic structure alignments. Furthermore, the transition curve of horizontal alignment and slope restrictions of curves are considered in this project by using the penalty function in order to obtain the most practical results possible. Finally, this study examines three problems for which the results are acceptable in cases of railway alignment geometry and its application in the real world. © 2019, The Author(s)
  6. Keywords:
  7. Bridges and tunnels ; Geographic information systems ; Optimization ; Particle swarm optimization (PSO) algorithm ; Railway alignment ; Transition curves
  8. Source: Urban Rail Transit ; Volume 5, Issue 4 , 2019 , Pages 207-224 ; 21996687 (ISSN)
  9. URL: https://link.springer.com/article/10.1007/s40864-019-00111-5