Optimization Model for Rail Line Crossover Design Considering the Cost of Delay

In this study, we introduce a method to optimally select the crossover locations of an independent rail line from a set of possible crossover locations considering a fixed number of crossovers that must be used in the design. This optimal selection aims to minimize the cost of passenger delay. Previous research showed that including passenger delay in the decision of rail design choices could be beneficial from economic and societal perspectives. However, those studies were only able to evaluate a few alternatives, because the degraded schedules had to be determined manually. In this research, we introduced an integer nonlinear model to find the best crossover design. We further developed an algorithm to evaluate a set of crossovers and determine the cost of delays for all segments on a rail line given a set of potential disruptions. The monetized cost of passenger delays was used to analyze the tradeoff between the unreliability costs emerging from the delay of passengers in the case of disruptions, and the total number of required crossovers. Our model was applied on a light rail line in Bergen (Norway) resulting in 10% reduction in relation to passenger delays without increasing the number of crossovers; thus, ensuring that there were no additional costs.

Evaluation of Transportation Infrastructure: A Case Study of Gold Coast Light Rail Stage 1&2

Since the COVID-19 pandemic began, there has been increased reliance on new infrastructure projects to counter economic fallout and underpin employment security. Urban and inter-urban transportation projects, such as major road, rail and port facilities, are popular choices for national and state governments in Australia as they provide broad fiscal support across all sectors of the economy. The problem with stimulus is making sure that the quality of the new infrastructure provides collective utility to a community or region. Whether the benefits will be worthwhile and represent best use of resource inputs requires financial, social, ethical and environmental consequences to be evaluated in a comparable format. The aim in this paper is to analyse the Gold Coast Light Rail (GCLR) Stage 1&2 project using a method that is capable of merging tangible and intangible criteria using an ordinal ranking algorithm. While the GCLR case study is undertaken with the benefit of hindsight, normally these types of evaluations are performed in real time as a project progresses from initiation (design) to implementation (deliver) and influence (delight). The method adopted in this study represents a modern form of multi-criteria decision-making, which enables successful projects to be distinguished from unsuccessful ones using a time period from commencement until one full year of operation has occurred. The i3d3 model, developed by a team from Bond University, has the unique benefit of ranking projects from best to worst across an organisational portfolio, geographic region or industry sector. It also supports past project performance to inform new design through application of a continuous improvement process of recording lessons learned. The GCLR case study calculated 100% of the critical success factors in the model to be positive and produced an overall success ranking of 23 (on a scale of -100 to +100). This paper presents the approach taken to evaluate GCLR’s level of success and the calculations that took place to reach this finding. This is the first time i3d3 has been used on an Australian project.

Land Use as a Criterion for the Selection of the Trip Starting Locations of Park and Ride Mode Travelers

In the attempt to study Light Rail Transit (LRT) systems, and their necessary underlying components, such as Park and Ride (P&R) sub-systems, this article aims to showcase the importance of land-use as a criterion in the selection of trip starting locations (i.e., points), that can potentially be used as the basis for quantitative studies on LRT and P&R systems. In order to achieve this goal, a method is introduced for the selection of locations that produce P&R mode trips based on the land-use attributes of sub-zones or neighborhoods, as they are included in Sustainable Urban Mobility Plans (SUMPs). Those land-use attributes are utilized as sub-criteria for the classification and valid selection of trip starting locations out of a broader dataset of available locations. As a second supportive technique that needs to be utilized for this study, an algorithm is introduced, which allows us to test the effectiveness of the method and the importance of land use as a criterion. The algorithm enables the calculation and comparison of the attributes of the trips to be followed by P&R mode users starting from selected trip starting locations for each zone in a city and having as destinations the several available P&R facilities. Results for the methods introduced in this article are showcased based on a case study on the mid-sized city of Cuenca, Ecuador, in which, several metrics, such as traveling times considering different traffic scenarios, are examined for the potential P&R mode trips as they emerge from real-world data.