Conference article

Railway networks are often operated close to their full capacity due to limited infrastructure expansion and increasing traffic demand. Hence, basic timetables are fairly vulnerable to random operational disturbances. In consequence of this, the service level for passengers decreases through a combination of delay propagation and delay accumulation. To solve this problem, a possibility widely used in research is to add extensive recovery and buffer times. Nevertheless, the resulting robust basic timetables would lead to a deterioration of the operating capacity, especially in congested areas. Another approach to reduce the impact of operational disturbances on railway operation is to use conventional dispatching algorithms. Unfortunately, most of them ignore further potential disturbances during the dispatching process, which is why the generated dispatching solution might even worsen train’s punctuality. In this context, at the Institute of Railway and Transportation Engineering (IEV) at the University of Stuttgart a proactive dispatching algorithm has been developed, that generates dispatching solutions under consideration of random disturbances in dynamic circumstances. The algorithm is divided into two main processes. First, the block sections are classified depending on their specific operational risk index by simulating numerous timetables with random disturbances generated in a Monte Carlo scheme and the related negative impacts in the studied railway network are calculated. Second, near-optimal dispatching solutions are automatically generated based on Tabu Search algorithm. This is achieved within a rolling time horizon framework, taking risk-oriented random disturbances in each block section into account.

Disturbance management, proactive dispatching, punctuality, capacity research, vulnerability of block sections