A next step in disruption management : combining operations research and complexity science

Railway systems occasionally get into a state of being out-of-control, meaning that barely any train is running, even though the required resources (infrastructure, rolling stock and crew) are available. Because of the large number of affected resources and the absence of detailed, timely and accurate information, currently existing disruption management techniques cannot be applied in out-of-control situations. Most of the contemporary approaches assume that there is only one single disruption with a known duration, that all information about the resources is available, and that all stakeholders in the operations act as expected. Another limitation is the lack of knowledge about why and how disruptions accumulate and whether this process can be predicted. To tackle these problems, we develop a multidisciplinary framework combining techniques from complexity science and operations research, aiming at reducing the impact of these situations and—if possible—avoiding them. The key elements of this framework are (i) the generation of early warning signals for out-of-control situations, (ii) isolating a specific region such that delay stops propagating, and (iii) the application of decentralized decision making, more suited for information-sparse out-of-control situations

Railway disruption management challenges and possible solution directions

<p>This paper investigates the challenges of railway traffic controllers in dealing with big disruptions and the kind of support tools that could help to improve their task in terms of performance, lead time and workload. The disruption handling process can be partitioned into three phases resembling a bathtub. For each phase the essential decision making process has been identified. Currently, the support to rail traffic controllers in case of severe disruptions is limited to predefined contingency plans that are not always feasible or applicable. In the literature, models and algorithms have been identified that could be used in the different parts of the three phases of the disruption handling process. This paper investigates the processes of disruption management in practice and the challenges that traffic controllers are facing during a disruption. The literature of models applicable to disruption management is reviewed and classified based on the three phases of the traffic state during disruptions. Finally, a rescheduling optimization model is applied to a case of complete blockage on a corridor of the Dutch railway network. The case study shows how a microscopic model could support the traffic controllers by providing real-time solutions for different phases of a disruption.</p

Data driven improvements in public transport: the Dutch example

Due to reduced budgets, higher political expectations and increasing competition between operators, there is growing pressure on public transport companies and authorities to improve their operational efficiency. It is thus of utter importance for them to be able to identify inefficiencies, bottlenecks and potentials in their public transport service. Recorded operational data, which has quickly become more widespread in the last decade, aids greatly in this process and enables operators and authorities to continually improve. In this paper we identify some of the arising possibilities. We first describe the state of publicly available transit data, with an emphasis on the Dutch situation. Next, the value of insights from Automatic Vehicle Location data is demonstrated by examples. Thereafter, a software tool is presented that enables operators and authorities to quickly perform comprehensive operational analyses, and which was able to identify several bottlenecks when applied in practice