Nikola Bešinović

A simulation-based optimization approach for the calibration of dynamic train speed profiles

Predictions of railway traffic are needed by planners and dispatchers for the design of robust timetables and real-time traffic management of perturbed conditions. These tasks can be effectively performed only when using train running time models which reliably describe actual speed profiles. To this purpose calibration of model parameters against field data is a necessity. In this paper a simulation-based optimization approach is introduced to calibrate the parameters of the train dynamics equations against field data collected at the level of track sections. A genetic algorithm is used to minimize the error between simulated and observed speed profiles. Furthermore, a procedure for the estimation of train lengths has been developed. This method has been applied to trains with different rolling stock running on the Rotterdam-Delft corridor in the Netherlands. The model parameters were calibrated for a significant number of trains of different compositions. We also derived probability distributions for each parameter which can be usefully employed for simulations. The results show that the train length estimation model obtained good computation accuracy. The effectiveness of the calibration method in giving a reliable estimation of the real train path trajectories is shown. It has been observed that some of the parameters of tractive effort and resistance do not affect the train behaviour significantly. Also, the braking rate is significantly smoother than the default value used by the railway undertaking while calibrated resistance parameters tend to have lower mean than defaults. Finally, the computational efficiency of the approach is suitable for real-time applications.

Microscopic Models and Network Transformations for Automated Railway Traffic Planning

This article tackles the real-world planning problem of railway operations. Improving the timetable planning process will result in more reliable product plans and a higher quality of service for passengers and freight operators. We focus on the microscopic models for computing accurate track blocking times for guaranteeing feasibility and stability of railway timetables. A conflict detection and resolution model manages feasibility by identifying conflicts and computing minimum headway times that provide conflict-free services. The timetable compression method is used for computing capacity consumption and verifying the stability according to the UIC Capacity Code 406. Furthermore, the microscopic models have been incorporated in a multilevel timetabling framework for completely automated generation of timetables. The approach is demonstrated in a real-world case study from the Dutch railway network. Practitioners can use these microscopic timetabling models as an important component in the timetabling process to improve the general quality of timetables.

Supporting tools for automated timetable planning

To satisfy the growing demand in railway transportation, infrastructure managers have the necessity to design more effective timetables. To this aim it is necessary to rely on automatic timetabling support tools that can provide feasible timetables with improved performance. In this paper the authors propose a hierarchical framework for timetable design that includes a microscopic, mesoscopic and macroscopic model of the network. These three models interact with each other in a closed-loop in order to generate an optimal timetable that is feasible at the level of track detection sections. An iterative adjustment of train running and minimum headway times is performed in the framework which stops when a feasible timetable is generated. Different from the other approaches in literature, this framework always guarantees timetable feasibility. Additional timetable performance is also realized in terms of stability, robustness, and energy efficiency. The application to an area of the Dutch railway network shows the ability of the framework in checking the feasibility of a timetable and evaluating its stability by determining the corresponding capacity occupation. In this sense practitioners can use this framework either for effective timetabling and postevaluation of existing timetables.

Solving large-scale train timetable adjustment problems under infrastructure maintenance possessions

Abstract During infrastructure maintenance possessions, commonly not all trains can operate, and the original timetable may have to be adjusted accordingly. To deliver the best service to passengers, operators have to coordinate adjustment measures dealing with multiple possessions at the network level. In this paper, we consider the Train Timetable Adjustment Problem (TTAP) and present a mixed integer programming (MIP) model for solving TTAP. In order to solve large-scale problems, such as national Dutch network, and design high-quality solutions, modelling extensions are needed. First, we apply three network aggregation techniques to decrease the problem size, which enables to solve instances on the complete Dutch network within satisfactory computation times. Second, we model turnaround activities for short-turned trains and test different strategies. Third, we introduce flexible short-turning possibilities to the MIP to possibly reduce the number of cancelled train lines. We test the proposed model on real-life cases of Netherlands Railways (NS) and assess the effect on computation times and solution quality. Also, we identify differences with current planners’ practice. Planners were positive about the quality of generated solutions and the computation speed. The current model can also be used to decide on combinations of time windows for possessions.

Capacity Assessment in Railway Networks

Capacity assessment is essential for densely utilized railway networks. To guarantee stable operations, it is necessary to evaluate the capacity occupation and determine possible infrastructure bottlenecks. This requires accurate microscopic models that incorporate detailed infrastructure characteristics, signalling and interlocking logic, train characteristics, and driver behaviour. This chapter presents capacity assessment models based on a novel algebraic approach that builds on accurate running and blocking time computations. The capacity assessment should be undertaken on corridors, station areas, and networks, and as such, support a better understanding of the existing timetable constraints and possible infrastructure investments.

Calibrating dynamic train running time models against track occupation data using simulation-based optimization?

In the last decades advanced simulation models have been more and more used by railway timetable designers and dispatchers to support both the off-line planning and the real-time management of traffic. Fundamental requirements for these models are the accuracy and reliability of describing real train dynamics. To this aim it is necessary to calibrate train running time models against real data collected from the field. In this paper a simulation-based calibration approach is proposed to fine-tune the parameters of the different phases of train motion (acceleration, deceleration, coasting and cruising) against track occupation data. A customized genetic algorithm is developed to minimize the error between observed and simulated data. The model has been calibrated for different classes of trains against a significant number of observed trains running on the Dutch corridor Rotterdam-Delft. A probability distribution is then estimated for each parameter to understand how driver behavior affects their variations and to identify the most probable value for each of the parameters. The results show the ability of the proposed model to calibrate train parameters robustly and reproduce observed train trajectories accurately. It is observed that the coasting phase is not applied frequently on the case corridor. Also, drivers adopt a braking rate that is significantly smoother than the default value used by the railway undertaking.