Optimization of grade-separated road and railway crossings based on a distance transform algorithm

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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: Search Methodsmentioning

confidence: 99%

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

Schonfeld

Zhang

et al. 2021

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“…Song et al, 2020) and station locations Pu et al, 2021). In addition to DT, researchers have also developed many other optimization methods combined with gridbased techniques for designing railway or road alignments, such as the deep reinforcement learning method (T. and DA (Hirpa et al, 2016;Pushak et al, 2016;T. Song, Pu, Schonfeld, Liang, et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

“…In addition to the HAS algorithm that inspired this paper, other nature-inspired computing and optimization algorithms proposed in recent years, such as the neural dynamic model (Park & Adeli, 1997), PSO (Imran et al, 2019;T. Song, Pu, Schonfeld, Zhang, et al, 2022), ACO (Sushma et al, 2022), harmony search algorithm (Siddique & Adeli, 2015), simulated annealing (Siddique & Adeli, 2016b), water drop algorithms (Siddique & Adeli, 2014b), gravitational search algorithm (Siddique & Adeli, 2016a), bacteria foraging algorithm (Wang et al, 2018), spider monkey optimization (Akhand et al, 2020), and spiral dynamics algorithm (Siddique & Adeli, 2014a), have been enhanced and improved to varying degrees in terms of spatial optimizations and ideas for solving optimization problems, all of which have promising prospects for improvement and application to the field of road or railway design.…”

Section: Introductionmentioning

confidence: 99%

A sequential exploration algorithm for the design optimization of horizontal road alignment

Zhang

Gao

et al. 2023

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Most computer‐aided optimization procedures for horizontal alignment optimization of roads require the use of information such as horizontal points of intersection (PIs) to determine an alignment. In these methods, to obtain parameters such as the radius of the curve corresponding to a specific PI, the previous and next PIs must be known. In this paper, a sequential exploration algorithm (SEA) is proposed, and the algorithm continuously explores the entire optimization space through certain steps. Only the parameters of the previous node are required to determine the current node's parameters during the exploration process, avoiding the tight coupling between PIs in traditional optimization algorithms. Furthermore, the proposed SEA does not require assumptions about the positions and numbers of the PIs, and it can design near‐optimal road alignments that match geometric restrictions and automatically take transition curves into account. Another feature of the proposed algorithm is that it directly optimizes the geometric element parameters based on the actual milepost, and it is a fully collaborative optimization approach that does not require secondary optimization nesting during the optimization process. Analyses comparing the optimization effects of different algorithms are performed on a numerical case, that is, a problem of avoiding obstacles, and two actual cases from the literature, that is, a new road design problem and an existing road reconstruction problem. It is discovered that the proposed SEA results in an approximately 3% to 10% improvement in optimization effects when compared to two current cutting‐edge optimization algorithms. This work offers a new perspective on road alignment optimization by merging discrete and continuous optimizations, with a discrete component handling optimization accuracy and a continuous component handling real optimization.

“…(2) automated solution searching based on intelligent methods, such as genetic algorithms (Jong & Schonfeld, 2003;Kang et al, 2009;Kim et al, 2007), particle swarm optimizations (PSOs; Babapour et al, 2018;Shafahi & Bagherian, 2013), mixed integer linear program (Monnet et al, 2020), motion planning (Sushma & Maji, 2020), and distance transforms (de Smith, 2006;Pu, Liang, et al, 2021;.…”

Section: Introductionmentioning

confidence: 99%

“…To handle the above issues, automated alignment optimization has attracted much interest from researchers, who largely focused on two aspects: () mathematical modeling of various factors affecting alignments (Kang et al., 2012) by considering, for example, costs (Hare et al., 2014; Kim et al., 2004; Vázquez‐Méndez et al., 2018), driving safety (C. Li et al., 2019), traffic demands (Lai & Schonfeld, 2016; Song, Pu, Schonfeld, Zhang, Li, & Hu, 2021), and environmental impacts (Jha & Schonfeld, 2004; Yang et al., 2014) as different objective functions; () automated solution searching based on intelligent methods, such as genetic algorithms (Jong & Schonfeld, 2003; Kang et al., 2009; Kim et al., 2007), particle swarm optimizations (PSOs; Babapour et al., 2018; Shafahi & Bagherian, 2013), mixed integer linear program (Monnet et al., 2020), motion planning (Sushma & Maji, 2020), and distance transforms (de Smith, 2006; Pu, Liang, et al., 2021; Pu, Song, Schonfeld, Li, Zhang, Wang, et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

Mountain railway alignment optimization integrating layouts of large‐scale auxiliary construction projects

Schonfeld

Liang

et al. 2022

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Mountain railway alignment design is an important but complex civil engineering problem. To overcome the drastically undulating terrain, long tunnels and high bridges are major structures used along a mountain railway, which poses great challenges for railway design and construction. Unfortunately, despite being studied for many years, the crucial construction factors of complex structures have received slight attention in alignment optimization. In this paper, for the first time, the layout of large‐scale auxiliary construction projects (LACPs), including tunnel shafts and access roads, is incorporated into the alignment design process in order to consider construction practicability and economy. Primarily, an alignment–LACPs concurrent optimization model is built. After defining the comprehensive design variables, the alignment–LACPs total construction cost is formulated as the objective function. Besides, the separate constraints for designing the alignment and LACPs are considered. Also, a construction duration computation is proposed for constraining the alignment–LACPs integration. To solve the model, a four‐step hybrid solution method is developed. Specifically, the alignment is first generated with a particle swarm optimization (PSO). Afterward, a new divide and conquer approach is devised to search for shaft alternatives along the alignment. Then, a customized Dijkstra algorithm is developed to search for complex access roads. Finally, a novel polynomial mechanism for time‐varying acceleration coefficients (TVAC) is designed for PSO to evolve the alignment–LACPs solutions. The above model and methods have been applied to two complex actual mountain railway examples. Their effectiveness is demonstrated through detailed analysis of resulting railway solutions and control experiments with contemporary TVAC‐based methods.