Designing urban rail transit systems is a complex problem, which involves the determination of station locations, track geometry, and various other system characteristics. Most of the previous rail transit route optimization studies have focused on the alignment design between predetermined stations, whereas a practical design process has to account for the complex interactions among railway alignments and station locations. This paper proposes a methodology for concurrently optimizing station locations and the rail transit alignment connecting those stations, by accommodating multiple system objectives, satisfying various design constraints, and integrating the analysis models with a geographical information system database. The methodology incorporates demand and station costs in the evaluation framework and employs a genetic algorithm for optimizing the decision variables for station locations, station types, and track alignments. It is expected that transit planners may greatly benefit from the proposed methodology, with which they can conveniently and efficiently optimize candidate alternatives. The Baltimore Red Line is used as a case study to demonstrate how the model can find very good solutions in regions with complex geography.

To provide efficient public transportation services in areas with high demand variability over time, it may be desirable to switch vehicles between conventional services (with fixed routes and schedules) during peak periods and flexible route services during low-demand periods. This option is called variable-type services. Conventional, flexible, and variable-type service alternatives optimized for various conditions are compared to explore when variable-type bus services might be preferable to purely conventional or flexible service. The optimization models used for purely conventional or flexible service are adapted from previous studies. These models are integrated into a new model for optimizing variable-type bus service. The results of sensitivity analyses show how demand variability over time and other factors affect the relative effectiveness of conventional, flexible, and variable-type bus services.

This paper presents a schedule-based passenger’s path-choice estimation model for a multioperator rail transit network, using automatic fare collection (AFC) data from both entry and exit stations. By introducing the train schedule connection network (TSCN), the path-choice estimation is converted into a set generation and weighted assignment problem for feasible TSCN paths (i.e., passenger trajectories). A major factor in path choice, the fail-to-board (FtB) phenomenon because of overcrowding in peak periods, is explicitly modeled. A method for estimating the FtB parameters is described and a weighted assignment function based on FtB parameters is provided. The case of a typical commuting origin-destination pair in the Beijing Subway is analyzed to demonstrate the capability of the proposed model. Results show that: (1) there can be multiple feasible trajectories for one pair of entry and exit records because of the FtB probability, especially in peak periods; and (2) the transfer penalty is more...

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.