Roads, railways and rail transit lines play major roles in transporting passengers and freight. In the design process for such transportation infrastructures, the alignment determination greatly affects their life-cycle performances. However, due to the great complexity associated with alignment design, conventional manual work is very time-consuming and laborious, which consequently prompts the flourishing development of intelligent alignment optimization (AO). Hitherto, with considerable invested efforts, numerous remarkable AO studies have been reported. In this work, we present the first-known literature review paper in this field from the past 25 years. First, a bibliometric visualization is conducted to show the overall characteristics of AO research. Then, existing mathematical models that formulate AO problems are reviewed. Particularly, GIS-based applications for AO are also analyzed in that part. Afterward, the intelligent solution methodologies for solving AO models are discussed. Moreover, two specific research topics, i.e., concurrent optimization of railroad alignments and station locations as well as the existing alignment recreation and redesign, are provided. Finally, we propose 12 possible research extensions and directions in this realm.

Sustainable railway construction and operation are threatened by densely occurring geological hazards in complex mountainous regions. Thus, during the alignment optimization process, it is vital to reduce the harmful impacts of geological hazards to a railway. However, current alignment-related studies solely consider such threats in existing geological hazard regions and, outside these regions, slight attention has been devoted to the assessment of potential hazardous impacts along the alignment. To this end, this paper proposes a novel railway alignment optimization model considering both existing and potential geological hazards based on quantitative geological hazard evaluation criteria from a sustainable safety perspective. More specifically, a geohazard zone classification method, within which an energy–slope model is integrated, is first developed. Three geohazard regions, namely the geohazard outbreak region, buffer region and fuzzy region, can then be obtained. Afterward, a spatial geological hazard assessment model is constructed considering the geological danger of three kinds of geohazards (debris flows, landslides and rockfalls) and railway construction vulnerability. This model is incorporated into a previous cost–hazard bi-objective alignment optimization model. Finally, the effectiveness of the proposed model is verified by applying it to a real-life case of the Sichuan–Tibet railway. The results show that this method can effectively optimize mountain railway alignments by concurrently reducing geological hazards and costs, which is beneficial to railway safety and sustainable construction and operation.

Railway alignment optimization is a large-scale and time-consuming civil engineering problem. To solve it, a three-dimensional distance transform (3D-DT) algorithm, which is a variant of the three-dimensional Euclidean distance transform (3D-EDT), was previously designed. However, that algorithm is quite computationally intensive. In addition, the 3D-DT is inherently sequential, and it is thus challenging to parallelize. Thus, this study focuses on improving the sequential 3D-DT by transforming it into a parallel one. First, existing representative parallel EDT methods are reviewed and assessed. Then the railway alignment optimization model and the sequential 3D-DT are described. After that, critical execution properties of the 3D-DT that significantly influence its parallelization are explored in depth. Lastly, a novel so-called parallel linkage method is presented. This parallel implementation, which is developed using the OpenMP library, is highly effective and scalable by fully exploiting the parallelism of the algorithm. Using this parallel 3D-DT method, a large-scale, real-world railway case is tested and analyzed in detail. The outcomes verify that the proposed parallel method can accelerate the optimization process significantly without reducing the quality of computation results.