Egidio Quaglietta

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.

Stability analysis of railway dispatching plans in a stochastic and dynamic environment

Abstract In the last decade simulation models and optimization environments have been developed that are able to address the complexity of real-time railway dispatching. Nevertheless, actual implementations of these systems in practice are scarce. Essential for implementation of an advanced dispatching system is the trust of traffic controllers into a stable working of the system. Nervous systems might change advice suddenly, and even switch back to a solution previously discarded, as time and knowledge of the perturbation progress. To this end, we propose several metrics and a framework to assess the stability of railway dispatching solutions under incomplete knowledge, and report on the evaluation of the state-of-the-art dispatching system ROMA, coupled with the simulation environment EGTRAIN, here considered as a surrogate of the real field. Rescheduling plans calculated at different control stages have been compared for different prediction horizons of the rescheduling tool. This setup has been applied to the Dutch Utrecht–Den Bosch corridor. Results show that the instability increases as stochastic disturbances propagate. Shorter prediction horizons give plans which are more stable over time in terms of train reordering, but tend to manage perturbations mostly by retiming. Larger horizons instead allow to manage traffic essentially by reordering trains but lead to more unstable plans. Enlarging the prediction horizon over a given threshold does not alter neither the structure of plans nor their variation over time.

A simulation-based approach for the optimal design of signalling block layout in railway networks

Abstract To meet the growing demand in railway transportation, practitioners are more and more required to upgrade or substitute the signalling system in order to increase the capacity of the network. Existing approaches for the design of the signalling layout, usually tend to maximize the technological efficiency of the system by shortening the length of block sections, thus reducing the minimum line headway and the energy consumption but increasing investment costs. This paper presents a design approach addressed to identify the signalling layout which minimizes the investment and management costs, while respecting the required level of capacity. To solve this problem an innovative design framework is introduced which integrates a stochastic multi-train simulation model within a “black-box” optimization loop. Results obtained from an application to a real metro line confirm the effectiveness of such method in finding the solution which minimizes total costs for the line manager. A comparison with the block layout which maximizes the technological efficiency highlights that the obtained solution constitutes a satisfying trade-off between total costs and network performances.

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.

Efficient formalization of railway interlocking data in RailML

Railways wish to reduce the costs of engineering interlocking systems by simplifying the exchange of technical information between stakeholders. Exchanging interlocking data in a machine-readable and standardized format removes inefficiency due to misinterpretation and conversion of data from non-standard, often paper-based formats. This paper proposes an UML class representation of the interlocking data that maps the characteristics of the interlocking system. These data model represents both tabular and geographical interlocking. The model reflects the topology of the railway network that the interlocking controls, routes, relations between signal aspects, speed indication signals, automatic train protection (ATP) and the state of movable and non-movable track elements. The data are machine-readable RailML, an incarnation of XML that is gaining the status of standard in railway modelling. We applied our approach to model the Dutch Santpoort Noord station, starting from paper-based track and signal plans. The resulting database captures all the features of the interlocking, whilst removing data redundancy. The model has general validity and it is easily extendible to other interlocking and ATP systems. Our approach contributes to create standard, electronic interlocking databases, directly from technical documents currently used in the practical world. We propose a database of railway interlocking mapping logical and physical features.The database builds on a novel UML formalization of interlocking elements.The standard machine-readable language RailML is used to structure the database.Our formalization overcomes the limits of previous approaches proposed so far.Using a standard database eases the communication process among stakeholders.

Impact of a stochastic and dynamic setting on the stability of railway dispatching solutions

Automatic decision support systems for the real-time dispatching of railway traffic have not reached yet real operations. The main concern with these systems is that they must guarantee a stable control strategy that prevents nervous behaviors of continuously changing solutions which might confuse human dispatchers. In this paper we investigate the stability of optimal dispatching plans against the dynamic evolution of randomly disturbed traffic conditions. A framework is developed that couples the state-of-the-art dispatching system ROMA, with the microscopic simulation environment EGTRAIN. Optimal plans are computed in a rolling horizon setup on the basis of updated traffic information and evaluated over multiple disturbed scenarios for different prediction horizons. Experiments on the Dutch railway corridor Utrecht-Den Bosch show that the instability increases as random perturbations propagate. Shorter prediction horizons give a more stable but less effective control strategy since mostly retiming is suggested by optimal plans. Larger horizons consent to manage traffic more effectively by reordering trains but lead to more unstable solutions. Enlarging the prediction horizon over a given threshold does not alter the structure of the control strategy also in terms of stability.

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.

Automated real-time railway traffic control: an experimental analysis of reliability, resilience and robustness

ABSTRACT Railway transportation provides sustainable, fast and safe transport. Its attractiveness is linked to a broad concept of service reliability: the capability to adhere to a timetable in the presence of delays perturbing traffic. To counter these phenomena, real-time rescheduling can be used, changing train orders and times, according to rules of thumb, or mathematical optimization models, minimizing delays or maximizing punctuality. In the literature, different indices of robustness, reliability and resilience are defined for railway traffic. We review and evaluate these indices applied to railway traffic control, comparing optimal rescheduling approaches such as Open Loop and Closed Loop control, to a typical First-Come-First-Served dispatching rule, and following the timetable (no-action). This experimental analysis clarifies the benefits of automated traffic control for infrastructure managers, railway operators and passengers. The timetable order, normally used in assessing a-priori reliability, systematically overestimates unreliability of operations that can be reduced by real-time control.

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.