A Method for Automatically Recreating the Horizontal Alignment Geometry of Existing Railways

Abstract

Periodic recreation of existing railway horizontal alignment geometry is needed for smoothing the deviations arising from train operations. It is important for calibrating track and rebuilding existing railways to ensure safety and comfort. Track calibration repairs the existing distorted track centerline to match the smoothed recreated alignment, which may differ considerably from the originally designed track centerline. Identifying the boundaries of all the geometric elements including tangents, circular curves, and transition curves is the key problem. Existing methods recreate the horizontal alignment semi‐automatically and can only generate a locally optimized solution while considering a few constraints. Based on the principle that the attributions of all the measured points to geometric elements should be consistent with the ranges of recreated geometric elements (i.e., for points‐alignment consistency), a method called swing iterations is proposed to reclassify point placements and identify all the tangents, circular curves, and transition curves simultaneously. In a swing iteration, the boundary of a geometric element segment repeatedly changes from left to right, then from the right to left, and finally stabilizes. Before the swing iterations, preliminary tangents and curves are identified based on the heading gradient (i.e., the rate of change of heading), and are set as initial values for the swing iterations. A genetic algorithm is developed to further refine the entire recreated alignment after the swing iterations. In the above processes, multiple constraints are handled. Applications demonstrate that this method can identify all horizontal geometric elements automatically and generate an optimized recreated alignment geometry for an existing railway while satisfying all the applicable constraints.

Generating Compact Geometric Track-Maps for Train Positioning Applications

“…Those maps may be extracted from existing site plans. However, these site plans can differ significantly from the real track situation [23]. Two possible ways to create compact geometric track-maps based on measurement data are presented in [23], [24].…”

A three‐dimensional distance transform for optimizing constrained mountain railway alignments

“…A GA is a heuristic algorithm based on analogies with natural selection and survival of the fittest. Up to now, it has been widely used to solve various engineering optimization problems (e.g., Adeli & Cheng, 1993, 1994Adeli & Kumar, 1995a, 1995bSarma & Adeli, 2001;Kociecki & Adeli, 2015;Li et al, 2019). Starting with Jong (1998), a research group at the University of Maryland has developed a series of GA-based methods for alignment optimization which have advanced this field in recent decades.…”

Mountain railway alignment optimization considering geological impacts: A cost‐hazard bi‐objective model

“…Ideally, railway alignments should bypass existing spatial geological hazards to thoroughly avoid their disastrous impacts. However, alignment design is a complex process that should consider many factors, such as topography (Li, Pu, Schonfeld, Zhang, & Zheng, 2016;Yi, 2018), geology (Karlson, Karlsson, Mörtberg, Olofsson, & Balfors, 2016), environment (Kang, Jha, & Schonfeld, 2012), ecology (Davey, Dunstall, & Halgamuge, 2017), construction (Vázquez-Méndez, Casal, Santamarina, & Castro, 2018), sociology (Yang, Kang, Schonfeld, & Jha, 2014), and economy and safety (Jha & Schonfeld, 2004), to find the optimal solution among the infinite potential alignment alternatives (Shafahi & Bagherian, 2013), while handling multiple constraints (Li et al, 2019). Thus, the resulting optimized alignment solution, which takes into account various design factors, is very likely to intersect with specific geological hazard regions.…”

“…H. Zhang et al (2020) combined cost, vegetation loss, and soil erosion to build a tri-objective model and then solved it using a particle swarm optimization. Meanwhile, despite their successes in finding optimized alignments with low costs in flat plain regions, few of the above algorithms perform satisfactorily in complex mountainous regions, as discussed in our previous studies (Li et al, 2017;Pu, Song, Schonfeld, Li, Zhang, Wang et al, 2019). In addition, Karlson et al (2016) integrated geological and ecological design criteria, found optimized railway corridors' centerlines using a Least-Cost Path analysis method, and evaluated the generated paths with a spatial multicriteria analysis approach.…”

“…To solve these problems, we have proposed a two-stage optimization strategy (Li et al, 2017). In the first stage (Li et al, 2016;Pu et al, 2018;Pu, Song, Schonfeld, Li, Zhang, Wang et al, 2019;Song et al, 2020), we determined several promising railway paths from the broad study area using distance transform (DT) algorithms, which are shortest-path algorithms. Then, these paths were used as centerlines to generate a set of railway corridors (Area-corridor stage).…”