2016
DOI: 10.1016/j.trc.2016.01.016
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A bi-objective optimization framework for three-dimensional road alignment design

Abstract: Optimization of three-dimensional road alignments is a nonlinear non-convex optimization problem. The development of models that fully optimize a three-dimensional road alignment problem is challenging due to numerous factors involved and complexities in the geometric specification of the alignment. In this study, we developed a novel bi-objective optimization approach to solve a three dimensional road alignment problem where the horizontal and vertical alignments are optimized simultaneously. Two conflicting … Show more

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Cited by 56 publications
(32 citation statements)
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“…As previously mentioned (in Equations (16) and (17)), the minimum curve length should satisfy the sight distance requirements. Therefore, the minimum curve length is based on sight distance and corresponding equations.…”
Section:  " Ls" Variation Restrictionsmentioning
confidence: 96%
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“…As previously mentioned (in Equations (16) and (17)), the minimum curve length should satisfy the sight distance requirements. Therefore, the minimum curve length is based on sight distance and corresponding equations.…”
Section:  " Ls" Variation Restrictionsmentioning
confidence: 96%
“…If there is more than one dependent variable in optimization, all variables convert into one mutual dependent value which is usually cost and the problem is solved as a single-objective problem. In some cases, bi-objective [17] and multi-objective [18] frameworks are proposed which provide a wider selection range for decision makers. With the evolution of models, more complicated and detailed cost functions and various road conditions embed into models to cover different settings and enhance the flexibility and versatility of models.…”
Section: Research Backgroundmentioning
confidence: 99%
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“…The usual goal of alignment optimization is to find an alignment with the lowest comprehensive cost between two given endpoints. In the literature, representative methods for optimizing alignments include particle swarm optimization (Shafahi & Bagherian, 2013;Babapour, Naghdi, Ghajar, & Mortazavi, 2018;Pu et al, 2019), two-stage method that combines global optimization methods with a gradient type algorithm (Vázquez-Méndez, Casal, Santamarina, & Castro, 2018), derivative-free algorithms (Mondal, Lucet, & Hare, 2015), discrete algorithms (Hirpa, Hare, Lucet, Pushak, & Tesfamariam, 2016;, dynamic programming (Hogan, 1973;Li, Pu, Zhao, & Liu, 2013), mixed integer programming (Easa & Mehmood, 2008), linear programming (Revelle, Whitlatch, & Wright, 1996;Chapra & Canale, 2006), network optimization (Trietsch, 1987a(Trietsch, , 1987b), heuristic neighborhood search with mixed integer programming (Cheng & Lee, 2006;Lee, Tsou, & Liu, 2009), calculus of variations (Howard, Bramnick, & Shaw, 1968), numerical search (Robinson, 1973), enumeration (Easa, 1988), average-end-area method for improving earthwork calculation accuracy from 2D to 3D (Cheng & Jiang, 2013), genetic algorithms (Maji & Jha, 2009, and distance transforms (DTs) (de Smith, 2006;Li et al, 2016;Li et al, 2017;Pu et al, 2018).…”
Section: Introductionmentioning
confidence: 99%
“…Network optimization Turner and Miles (1971), OECD (1973), Athanassoulis and Calogero (1973), Parker (1977), and Trietsch (1987a,b) Dynamic programming Hogan (1973), Nicholson et al (1976), Puy Huarte (1973), Murchland (1973), Goh et al (1988), Fwa (1989), Li et al(2013) Mixed integer programming Easa and Mehmood (2008) Neighborhood search heuristic with mixed integer programming Cheng and Lee (2006), Lee et al (2009) Distance transform Mandow and Perez-de-la-Cruz (2004) and De Smith (2006) Discrete algorithms Mondal et al (2015), Hirpa et al (2016), Pushak et al (2016) Among direct methods, genetic algorithms (GAs) have been the most popularly adopted methods for optimizing railway or highway alignments in the past decade (Shafahi and Bagherian, 2013). They have been developed by a University of Maryland research group.…”
Section: Introductionmentioning
confidence: 99%