Railway traffic rescheduling approaches to minimise delays in disturbed conditions

Fan, Bo (2012). Railway traffic rescheduling approaches to minimise delays in disturbed conditions. University of Birmingham. Ph.D.

Preview

Fan12PhD.pdf
PDF
Download (2MB)

Abstract

The advent of modern railway signalling and train control technology allows the implementation of advanced real-time railway management. A number of researchers throughout the world have previously considered the problem of minimising the costs of train delays and have used various optimisation algorithms for differing scenarios. However, little work has been carried out to evaluate and compare the different approaches.

Firstly, this thesis compares and contrasts a number of optimisation approaches that have been previously used and applies them to a series of common scenarios. It is found that simple disturbances (i.e. one train delayed) can be managed efficiently using straightforward approaches, such as first-come-first-served. For more complex scenarios, advanced methods are found to be more appropriate. For the scenarios considered in this comparison, ant colony optimisation performed well.

Secondly, in order to improve the currently available algorithm so that it can more reliably find optimal or close to optimal results within a practical computation time, a new hybrid algorithm, based on ant colony optimisation, has been developed. In order to evaluate the new approach 100 randomly generated delay scenarios are tested, and a comparison is made between the results of the new algorithm and first-come-first-served, brute force and standard ant colony optimisation. It is shown that the hybrid algorithm has improved performance in terms of optimality and computation speed.

Finally, a new multi-stage rescheduling approach for finding an optimal solution over multiple junctions is proposed. A case study is considered, and it is shown that the proposed approach performs well.

Type of Work:

Thesis (Doctorates > Ph.D.)

Award Type:

Doctorates > Ph.D.

Supervisor(s):