A modified motion planning algorithm for horizontal highway alignment development

Determinations of alignments and station locations are major tasks in railway design, which jointly and fundamentally influence the construction and operation of the whole project. Currently, although considerable efforts have been invested into the separate optimization of alignments or station locations, the complex interactions between alignments and stations, as well as their concurrent impacts on railways still need further investigations. Therefore, this study focuses on modeling and optimization of passenger railway alignments and station locations simultaneously. A novel optimization model is formulated for maximizing net present value (NPV) considering comprehensive costs and revenue from riders over the railway's design life. Specifically, the comprehensive cost includes construction and operation costs of alignments and stations. The railway ridership is quantified by combining travel time and trip coverage using a logit function. Then, the railway revenue is estimated and combined with costs through an economic growth model to obtain the NPV. To solve this model, two customized and mutually exclusive methods, that is, the station‐alignment‐integration (SAI) and alignment‐station‐integration (ASI) methods, are developed based on a particle swarm algorithm. The SAI primarily searches for possible stations in the landscape and then generates alignments to connect adjacent stations. The ASI directly yields the main alignment, on which potential stations are located by handling specific constraints. The model and methods are applied to an actual high‐speed railway. Results show that both SAI‐ and ASI‐generated railways have higher NPV values compared to the corresponding railway designed by experienced designers. Sensitivity analysis reveals that SAI is more flexible than ASI in solving actual cases. The convergence of the proposed methods is also discussed. With some changes in input parameters based on locally applicable design standards, the proposed methods are generally applicable to other cases or countries.

Section: Introductionmentioning

confidence: 99%

Simultaneous optimization of 3D alignments and station locations for dedicated high‐speed railways

Schonfeld

Zhang

et al. 2021

“…Song et al, 2021;Z. Song et al, 2021;Sushma & Maji, 2020). Regardless of the design variables F I G U R E 1 Diagram of an HA: main components and notation used, the objective desired, or the restrictions imposed, these optimization problems are usually nonconvex (with several local minima), and they must be solved with a global optimization method.…”

Section: Introductionmentioning

confidence: 99%

“…Classical optimization methods need initial alignments to start the algorithm (Li et al, 2016;Mondal et al, 2015). Usually, these alignments are obtained manually using the characteristic of each case study, which greatly hinders the automation of the process (Sushma & Maji, 2020). Random generation of HA verifying preset geometric constraints, named admissible horizontal alignments (AHA), is a very useful tool to automate the use of many of the classic optimization methods.…”

Section: Introductionmentioning

confidence: 99%

An algorithm for random generation of admissible horizontal alignments for optimum layout design

Vázquez-Méndez

Casal

Castro

et al. 2021

This work deals with the design of horizontal alignments (HAs) for its application in intelligent civil systems. Two consecutive tasks are performed: the random generation of admissible horizontal alignments (AHAs; alignments verifying some geometric constraints set in advance) and the optimal design of layouts joining two given points. A rigorous mathematical analysis of the first task leads to a novel algorithm for random generation of alignments, which is used to develop the global optimization method proposed for the second task. The usefulness of this approach is shown on some practical applications. First, two problems with applications in robotics, pipe layout design, and forest road design are solved in realistic situations. Next, the random generation of AHAs in mountainous terrain is addressed. Finally, the model is extended to 3D alignments and a real case study is presented, designing a bypass on the N-640 Spanish national road, surrounding the urban area of Monterroso.

“…To overcome these problems, researchers have invested considerable efforts into automated alignment optimization methods. Corresponding state‐of‐the‐art studies can be classified into (a) horizontal alignment optimizations, for example: Jong, Jha, and Schonfeld (2000) integrate a geography information system with a genetic algorithm (GA) to solve preliminary highway design problems; Mondal, Lucet, and Hare (2015) solve a horizontal alignment optimization model using two derivative‐free optimization algorithms; Sushma and Maji (2020) propose an algorithm based on customized motion‐planning, which performs flexibly in finding appropriate horizontal alignments; (b) vertical alignment optimizations, for example: Bababeik and Monajjem (2012) use a GA to optimize construction and operating costs of vertical alignments; Beiranvand, Hare, Lucet, and Hossain (2017) design a multihaul quasi‐network flow model to improve the accuracy of vertical alignment optimization processes; Monnet, Hare, and Lucet (2019) extend a mixed integer linear program for modeling the vertical alignment design; and (c) 3D alignment optimizations, for example: de Smith (2006) first applies a distance transform to solve constrained 3D alignment optimizations; Pushak, Hare, and Lucet (2016) compare five discrete algorithms in finding 3D alternative paths to generate potential corridors for railways and roads. Although the effectiveness of these studies in solving their problems has been verified, most of them focus on optimizing alignments in relatively flat regions.…”

Section: Introductionmentioning

confidence: 99%

Bi‐objective mountain railway alignment optimization incorporating seismic risk assessment

Song

Schonfeld

et al. 2020

Mountain railway alignment optimization is known as a very complex engineering problem that should consider many factors, such as drastically undulating terrain, geological hazard impacts, and additional constraints. Moreover, many mountain railway projects are located in earthquake‐prone regions and hence are greatly threatened by seismic activity. Thus far, most alignment optimization studies aim at finding the least‐cost solutions within budget but slight attention has been paid to reducing the complex seismic risk through optimization. In this paper, the first known quantitative seismic risk assessment model for railway alignment optimization is presented, which combines probabilistic seismic fragility analysis and probabilistic seismic loss analysis. Three methods for fragility analysis of bridge, tunnel, and earthwork sections are designed and a specific event tree is developed for seismic loss analysis. Moreover, multiple preliminary constraints are specified for alignments traversing active faults. Afterwards, the seismic risk assessment model is combined with a least‐cost model to formulate a bi‐objective optimization model. To solve it, a particle swarm optimization algorithm is improved by blending the crowding distance computation (CDC) and, especially, a novel marginal benefit analysis (MBA) to search for pareto‐optimal solutions during optimization. A prescreening and repairing operator is also designed to handle the fault constraints. Finally, when applying the proposed procedure to a complex realistic railway case, the results show that the hybrid CDC+MBA bi‐objective solver can find better pareto‐optimal solutions than the generic CDC method. Besides, detailed data analysis shows that the present method can produce less expensive as well as safer solutions than the best alignment designed by experienced human engineers.