Module II - Efficient resource management in public transportation

 Motivated by the expected population growth in Vienna, Module II - Efficient resource management in public transportation aims to tackle the arising challenges in urban public transit. Our industrial partner, Vienna’s public transport provider Wiener Linien GmbH & Co KG, faces an increasing demand, i.e. more passengers use the public transit system. Consequently, the provider is interested in optimizing the headway by considering customer-oriented and cost-oriented objectives. Furthermore, the provider seeks guidelines and recommendations for dealing with service disruptions and breakdowns.

Disruption management

Vienna’s public transport provider occasionally faces disruptions in its service operations. These disruptions range from minor incidents, as small delays of metro trains, to a blockage of a metro line section. The measures to take in the context of serious disruptions include line planning of the replacement service and adjusting the frequencies of existing lines. For this topic an optimization model and heuristics are developed.

Maintenance scheduling

Maintenance scheduling deals with the strategic planning of maintenance work for the infrastructure of a large-scale urban transportation network. A mixed integer programming model has been developed to represent this planning problem. When solved via a commercial solver the model is able to generate good solutions with a small optimality gap only for small instances but it lacks the ability of generating maintenance plans for large scale networks. For solving large instances heuristic approaches have to be used.

Headway optimization

Headway optimization mainly focuses on adjusting the frequencies of the existing lines. For this purpose a simulation-based optimization approach is developed. The underlying simulation model utilizes real world data provided by our industrial partner, the Viennese public transportation provider. In order so solve the headway optimization problem for the Viennese subway system, evolutionary algorithms are applied.

Further benefits

In addition to the mentioned goals, there might be other recommendations the can be derived from the outcomes. By identifying the optimal headway of the lines, the crowdedness in stations can be analyzed and security concerns may be derived, which in turn gives the provider incentives to overcome these issues. The outcome of the analysis of the disruption management might suggest to install bus depots in order to install replacement services more quickly or to construct additional rails for establishing alternative routes in case of a blockage of some line segments. Consequently, the results may also have an effect on the infrastructure.