A methodology to optimise a rail network specification for maximum passenger satisfaction and reduced initial investment. (March 2022)

• Record Type: Journal Article
• Authors: Hickish, Bob; Fletcher, David I.; Harrison, Robert F.
• Is Part Of: Journal of rail transport planning & management. Volume 21(2022)
• Publication Date: 2022-03
• Subjects: Optimisation -- Rail -- Network -- Value engineering -- Passenger satisfaction; Railroads -- Periodicals; Railroads -- Planning -- Periodicals; Railroads -- Management -- Periodicals; Railroads; Railroads -- Management; Railroads -- Planning; Periodicals; 385.068
• DOI: 10.1016/j.jrtpm.2021.100279
• Languages: English
• ISSNs: 2210-9706

Abstract

Abstract: This paper considers the design and construction of new passenger rail networks – a common and expensive task worldwide. Optimising the allocation of initial investment between components of a proposed network is a challenge for planners who wish to reduce the initial investment required whilst maintaining key objectives, such as providing high levels of passenger satisfaction. Previous decision support tools to assist with this challenge typically do not consider the influence of the high-level network specification on passenger satisfaction. Here, a novel optimisation methodology is presented that includes the effects of various factors, e.g. permissible line-speed, train performance, train comfort, and station comfort. The methodology combines Agent-Based Modelling, Bayesian Optimisation, and a model which quantifies passenger experience. To demonstrate its effectiveness, the methodology is illustrated in a hypothetical case-study where it identifies a network specification which substantially improves the satisfaction of the virtual passengers. Furthermore, the network specification reduces the modelled cost of network construction by £5 billion from £64 billion. The case-study demonstrates that the methodology is computationally tractable for realistically sized tasks, and captures the trade-offs between investment and component performance, making it potentially useful to network planners concerned with the satisfaction of passengers. Highlights: Bayesian Optimisation of agent-based passenger experience simulation. Presented methodology demonstrated in a case-study. Effects of varying speed and comfort evaluated, considering required capital. 'Good' solution identified for 'reasonable' computational cost. Findings support application of presented methodology for network optimisation.