Ensuring safety, enhancing reliability

Europe’s railways are among the safest in the world. However, due to the growing complexity of rail, maintaining this status is becoming increasingly challenging.  To help maintain rail’s high safety standards, the Europe’s Rail Joint Undertaking (JU) is taking a global approach to railway safety – an approach that starts with understanding available risk assessment methods and how they apply to safety management.

Safety starts with assessing the risks

The developed safety risk management model is based on a risk assessment method and uses a decision support tool.

Risk assessment

Although there are already some risk assessment methods developed for railways, the JU developed a method that focusses on the human centric aspects of safety and includes system functionalities to create scenarios as close to reality as possible. The method uses a decision model to identify the main parameters, which can then be applied to optimise safety, functionalities (e.g. availability, capacity), and system costs.

Decision support tool

By identifying risks and taking a holistic view, the developed decision support tool allows decision makers to quickly react to a safety situation. The parametric approach can be generalised and applied to a wide range of decisions across the entire railway system – meaning both the design and operation of a system can be adapted to the risk assessment.

The tool can also combine safety with costs and/or the level of service provided, meaning users can optimise cost efficiency without sacrificing safety.

Discussion

To develop the safety risk management model, qualitative and quantitative indicators were identified and applied, either individually or in combination, to define the risk requirements. Factors that strongly influence the properties of a safety evaluation were also determined. These included the number and complexity of the systems involved in the scenario, along with the interactions between operators and passengers.

Based on this, three representative use cases, each featuring different levels of complexity, involving human interactions, and highlighting errors that could result in fatal consequences, were developed:

• The complexity level of the components was evaluated using a trackside signal failure use case.
• At the application level, a use case involving the failure of an ERTMS on-board system and resulting braking was used.
• For the overall system level, the evacuation process of a driverless metro system with or without fire was analysed.

Based on these use cases and taking into consideration the properties and parameters required for evaluation, the safety-critical processes and faulty conditions and consequences were described. With a comprehensive picture of imaginable railway applications in hand, the default behaviour, along with behaviour in case of failure, were specified.

The decision model developed from the identified methods was then applied to the individual use cases. This allowed for the identification of the parameters that could be influenced by the operator based on the safety requirements, costs, and system functionalities.

Key findings

• Although the validity of the system is heavily dependent on the availability and reliability of the input data, even when there is a lack of data, the developed method can contribute to the objectification of safety-critical decisions.
• The developed global approach will enable the evaluation of new safety standards and rules for automatic train operations by reducing train headways and automating human factors.
• The developed system can help improve maintenance and operation processes and increase asset health, availability, punctuality, and speed.

Conclusions

The developed safety risk management model, which achieved a technology readiness level (TRL) 3 (experimental proof of concept), has opened the door to a new holistic view on safety approaches in the railway system that make use of digital technology.

Next steps

Although the daily application of risk assessment-based safety management can promote innovation in the management of subsystems and bring the overall system to a higher TRL, doing so requires bringing industrial partners, operators, and infrastructure managers into the development process.

 

Smart planning at the crossroads of safety and reliability

 Optimised network and traffic management contribute to increasing the safety, reliability, and performance of the European railway system.

Introducing PROTON

PROTON, which stands for Punctuality and Railway Operation Simulation, is a macroscopic simulation tool designed to improve the planning quality of timetables. By integrating planning activities, external influences, and status information from various actors across the railway system, the tool gives infrastructure managers and railway undertakings the ability to predict the expected operational quality of the planned timetables on a given infrastructure.

PROTON can be applied for both short- and mid-term timetable planning, allowing one to identify potentially critical situations in the plan. Furthermore, by providing results within a few minutes of simulation runtime, the tool enables planners to optimally use existing infrastructure capacity.

Discussion

Operational planning requires long collaboration processes, which make it difficult to adapt to short-term events. Simulation could serve as a possible solution.

To develop this idea, the safety risk management model was applied to operational planning, a process that involved developing a process framework, providing definitions, and clarifying relations to traffic management systems (TMS). Based on this work, seven use cases were specified where macroscopic rail simulation (i.e. PROTON) could enrich the planning process.

1. Management of real-time traffic
2. Temporary traffic restrictions
3. Short-term requests
4. Infrastructure maintenance
5. Rolling stock maintenance
6. Resource plans for rolling stock and staff
7. Communication of weather-related information

Although PROTON could already support most of the requirements for each use case, it lacked capacity information. To address this shortcoming, the macroscopic simulation was supplemented with microscopic infrastructure information. However, this coupling turned out to be unsuitable for most applications, the result of it requiring simple dispatching rules and a lack of communication between simulations.

As an alternative, the PROTON infrastructure was augmented with microscopic data. Here, the representation of the rail network remains mostly the same, but the properties of the nodes and edges are more fine-grained – ultimately striking a good balance between fast running times and a realistic representation of the real world.

Of the resources whose availability rail operations depend on, PROTON initially focused on infrastructure, mapping the dynamic resources (e.g. rolling stock, staff) and any resulting delays. It did this randomly using probability distributions that excluded causal effects.

With a better understanding of how resource dependencies could be included in the simulation, PROTON was adapted accordingly, with a flexible data interface defined, relevant functionalities implemented into the simulation core, and a dispatching logic developed.

The adapted version was then evaluated via a one-week case study where operations in the German rail network were simulated. During the study, users could use the model to compare the effects of different resource plans on such KPIs as expected network punctuality. It was also used to answer such questions as:

• How do infrastructure disturbances affect nationwide railway traffic?
• How effective are specific replacement schedules?
• Can the task of planning a replacement schedule be automated?

Key findings

• While the simulation was initially expected to address 40% of overall delay time, by collecting further information and evaluating the feasibility of applying the simulation approach to other use cases, PROTON was able to address more than 60% of the overall delay time.
• The simulation model is particularly well-positioned to reduce delays caused by secondary sources or planning management.

Conclusions

 The adapted simulation tool, which achieved a TRL 3 (experimental proof of concept), can support rail stakeholders in making quick decisions using new digital supported solutions. Specifically, by taking all important information into account, identifying operational bottlenecks, and harmonising long- and short-term planning activities, it ensures greater robustness of planned timetables, leads to greater system compatibility, and lays an important foundation for the Single European Railway Area.

PROTON for Automated train operations Logistics on demand Service operations timed to the second Stations and smart city mobility

Next steps

 Further improvements to the simulation model could be made by including dispatching decisions (if resources are not available at the planned departure time). PROTON’s scope could also be extended by writing interfaces to other data formats and tools. Furthermore, by specifying an integration layer, the model’s infrastructure information could be translated into an internal data format.

 

Integrated mobility management

 Another key aspect of increasing rail reliability is Integrated Mobility Management (I2M).

I2M is a key solution to rail traffic management. By integrating different modes of transport, I2M can drastically simplify route planning and make rail travel safer and more efficient.

Within the context of the Europe’s Rail Joint Undertaking, I2M has the potential to increase:

• Operational reliability in freight
• Operational performance for passenger services
• Efficiency of rail freight operations
• Efficiency of passenger-focused operations

With the aim of leveraging I2M’s full potential within rail, the JU developed a portfolio of prototypes, each of which focused on using the TMS to support high efficiency freight operations.

The prototypes

Dangerous goods management application

• Focus: On-board diagnostic system to monitor a wagon carrying dangerous goods.
• Objective: Although vehicles carrying goods by road are equipped with diagnostic and localisation systems, in rail, freight wagons carrying such goods run without any on-board electronics or power control systems. This prototype aimed to close this gap.
• Results: All tests were successfully passed.
• TRL: 3 (experimental proof of concept) or 4 (technology validated in lab).

Big potential for managing dangerous goods Several interesting developments for managing dangerous goods are envisaged. For example: Installing a GNSS receiver inside the MCU will guarantee the real-time localisation of freight wagon fleets, significantly improving logistic management. In addition to temperature sensors, the entire sensor network could be integrated with accelerometers installed on the bogies, sending alarms related to anomalous accelerations. This would make the monitoring system more robust and more accurate. Because their power consumption is relatively low, the MCU and onboard sensors could be powered by renewable energy generated from solar panels, wind turbines, or piezoelectric sources.

ATO-application for freight trains

• Focus: Automated loading and unloading of bulk goods for mining.
• Objective: To meet the key requirements for a train operation solution for mines and its integration with existing loading/unloading automation systems.
• Results: The solution is in operation in Indonesia with plans to expand to other projects.
• TRL: 3 (experimental proof of concept) or 4 (technology validated in lab) within the scope of the Shift2Rail project.

 

Functions to support better management of freight operations

• Focus: Calculating and forecasting estimated time of arrival (ETA) and train path slot planning and management.
• Results: Demonstrator can connect ETA to integration layer.
• TRL: 3 (experimental proof of concept) or 4 (technology validated in lab).

 

Conflict detection and resolution

• Focus: Increasing transport capacity and reliability.
• Objective: Calculate an optimal short-term timetable and provide a process for using said timetables as a decision support tool for the traffic operator.
• Results: A small subset of the infrastructure was analysed (single track line operations with limited number of crossings and overtaking opportunities).
• TRL: 3 (experimental proof of concept) or 4 (technology validated in lab).

 

Driver advisory functionality

• Focus: TMS and resource management system (RMS) integration.
• Objective: TMS and RMS are typically controlled by different entities. This prototype aims to integrate these systems as a means of improving accuracy, speeding up the re-planning of both, and, ultimately, enhancing their operational efficiency.
• Results: All test cases were successfully passed.
• TRL: 3 (experimental proof of concept) or 4 (technology validated in lab).

 

Design concepts for annual and ad-hoc timetable planning

• Focus: Timetable optimiser that can be used in both annual and ad-hoc timetable planning.
• Objective: To provide planners with a tool capable of finding solutions to common planning problems and to guarantee that the resulting timetables are conflict-free and optimal in relation to some evaluation criterion (e.g. total runtime of included trains, total deviation from intended departure/arrival times).
• Results: Tests have shown positive feedback in terms of feasibility.
• TRL: 3 (experimental proof of concept) or 4 (technology validated in lab).

 

Automatic router adaptations related to providing automatic routing for manoeuvres at stations/depots

• Focus: Traffic-node coordination and dynamic crossing analysis modules.
• Objective: To create a coordination link and share information between the mainline and nodes via a single system.
• TRL: 3 (experimental proof of concept).

 

Freight functionalities and resource mapping for management of dangerous goods integrated into freight operations

• Focus: Optimal timeslot and itinerary for dangerous goods.
• Objective: Manage resources (route, timeslot) to minimise risk to civil infrastructure and people in those areas crossed by trains carrying dangerous goods.
• Results: All tests have been successfully passed.
• TRL: 3 (experimental proof of concept) or 4 (technology validated in lab).

 

How data from driver advisory systems can be aggregated and used to optimise freight operations

• Focus: Data exchange between TMS and freight operations and optimising freight operations using data from container management systems.
• Objective: Demonstrate data exchange between TMS and freight operations, with a particular focus on providing new data to optimise these operations through the container management system, thus allowing terminal managers and customers to adapt their plans based on ETA’s live status of containers.
• Results: All tests have been successfully passed.

 

Key findings

By integrating highly advanced status information across the dispatching process, these prototypes help advance I2M’s ability to increase both capacity and reliability. In fact, it is estimated that this integration will reduce delays by as much as 10%.

The integration of new functional applications and modes will also result in at least a 10% cumulative cost savings by:

• Decreasing operational cost of rail traffic, including energy, vehicle wear, delays, and productivity.
• Improving overall productivity through better use of locomotives and reduced wait times at freight/station terminals.
• Minimising operational cost of staff at control centres.
• Reducing life-cycle costs of railway infrastructure assets.

These savings will be further complemented by a minimum 10% reduction in the costs related to investing in hardware and functional service applications, along with adapting non-compatible subsystems from different suppliers.

Advanced business services

In addition to the aforementioned prototypes, the JU also developed an advanced business services (ABS) concept to increase the efficiency of passenger-focused operations. With the ABS serving as the integration through data sets, business logic, and systems, a higher value can be achieved than what the individual services can provide alone.

Numerous ABS concepts were developed, either as an initial proof-of-concept (TRL 3 – experimental proof-of-concept – or 4 – technology validated in lab) or as a full proof-of-concept (TRL 5 – technology validated in relevant environment – or 6 – technology demonstrated in relevant environment). Each concept was designed to either improve traffic management or to improve asset management and maintenance strategies.

The 10 ABS concepts include:

1. Traffic management and stock crew integration: Integrate a production TMS and a production stock and crew management system using shared train service forecasts.
2. Multi-modal operation control centre: An automated incident identification, classification, and prioritisation process to assist control centre staff with the strenuous task of manually browsing through incoming transport-related data to identify those incidents where action must be taken.
3. Automated weather response in TMS: Automatic detection of those weather conditions that require operating restrictions and to generate such restrictions for the affected location. Said restrictions are integrated into a production TMS to allow operators to activate and plan a response.
4. Intelligent incident management for rail communication centres: To provide rail communication centres with a tool for understanding, summarising, and disseminating correct information to railway stakeholders and passengers.
5. Automated passenger information: To improve the automated, real-time disruption notifications sent to passengers.
6. Network optimisation and cascading failure: Explore different modelling techniques for identifying asset criticality.
7. Asset prioritisation according to criticality and mitigation of service interruptions: To prioritise the assets (i.e. track circuits) to be maintained based on their condition and criticality (with criticality being evaluated based on the level of consequence that the track circuit’s failure would have on train service).
8. Delay propagation: Improve train estimated arrival times across a network, particularly when the network is stressed, using a predictive algorithm that compares the current situation with historical data about ETAs in similar situations.
9. Possession planning: Implement a reinforcement learning system to help address the dual challenge of optimising the efficient use of available resources and those tasks typically performed by a human.
10. Bus pinch-point analysis: Improve the capabilities of local transport managers to optimise road networks by enhancing traffic flow to and from train stations. This is done via a visualisation tool that identifies the key junctions that act as ‘pinch-points’ and delay bus services to train stations.

Conclusions

• All the prototypes and functionalities exemplify the benefits that can be gained when data is made available and used to support an integration platform.
• The developed applications support improving the reliability of the system, which facilitates capacity increase and cost reductions by optimising the traffic on the network.
• Each of the delivered prototypes and concepts, which provide additional features of the TMS, are ripe for further development, creating a unique opportunity for infrastructure managers, railway operators, suppliers and final users.
• Europe’s Rail is further developing these concepts in the next digitalisation element of the Flagship Area 1 on network management planning and control and mobility management in a multimodal environment.