Artificial Intelligence in Indian Railways
Indian railways are the fourth largest network in the World with a track length of over 70,000 km and a network of more than 20,000 passenger trains. Managing all aspects – operation, maintenance, scheduling, repair or monitoring has always been a tough task for the Indian railways. The task is difficult due to the problem of integration of various systems such as signalling, telecom, operation, rolling stock, electrical, information technology, traffic, infrastructure, etc. and the involvement of human factors.
One way of trying to meet the demand is to enhance railway infrastructure. The infrastructure has been the key feature of current projects such as the Konkan Railway double-track expansion and the introduction of bullet train along the Mumbai-Ahmedabad High-Speed Corridor. But the question is why to overlook the increase in throughput to meet the increasing demand by the incorporation of Artificial Intelligence, machine learning, and self-control systems.
These systems are designed to improve the reliability of existing infrastructure and make up for the one-time heavy investment. The heavy investment can eliminate the need for human interference and provide the required level of safety and speed up-gradation. It should be possible to do more with the hardware if we can make the ‘software’ more efficient. Having more efficient software includes greater information sharing, lower latency, and smart algorithms. This is why many OEMs and startups are now investigating the feasibility of services based on Artificial Intelligence worldwide.
Feasibility of Artificial Intelligence in Indian Railways
- Operation – Artificial intelligence for any system requires a huge amount of linkable data. In Indian railways, the network already runs on the SCADA system, so a vast amount of operational data is available for modelling and training purposes.
- Infrastructure – Digitised versions of railway infrastructure is also readily available. It is also available for rake information and crew rosters.
- Tracks and Rolling stock – The data for predictive maintenance of the tracks or trains is in development phase in the country. However, with not much variation data from other countries can be used for training of these modules and sections.
- Signal and Telecom – The history of events may be obtained from the data loggers of the interlocking system. This can help to schedule the movement of trains and also to manage machine-driven operations.
Although we have good availability of the data, the key challenge would be in developing a framework where researchers can start with several individual problems, and then integrate them effectively. Hierarchical or more generic architectures are effective in handling such intentions. These could be used from an algorithmic standpoint. The need is to make the various systems talk to each other effectively. This is where expertise in software/hardware architectures and system integration is required.
Applications of Artificial Intelligence in Indian Railways
There is a long-range service spectrum that AI can provide depending upon the level of efficiency and need. The feasibility of some of the primary requirements of Railways which Artificial Intelligence can provide is discussed below:
- Train Scheduling
All signalling rules assume that tracks at stations are occupied by at most one train at a time. This can be ensured by algorithms, simulation models, graphs, heuristics and control systems with the required degree of Artificial Intelligence in Indian Railways.
The information which can be obtained from AI shall include:
- Time duration from the ﬁrst event to the last event,
- The total or average running time of trains,
- The priority-weighted running time of trains,
- Robustness of the timetable to deviations, and combinations thereof.
This will make sure that
- Either a track section between two stations is occupied by at most one train at a time (in absolute block signalling), or
- Every piece of track between two signals is occupied by at most one train at a time (in automatic block signalling).
Currently, railway networks do not use automated algorithms for this function. They instead rely on the training and experience of controllers (dispatchers) to take decisions. They cannot process a large amount of contextual information, Neither they can meet the demand for small turnaround time for decision making.
But the above-mentioned approach will generate high-level timetables and schedules of train movement. They will specify tracks to be occupied, the time required for switching tracks, and signalling requirements. And the parameters of immediate conflict can be evaluated in real-time. Hence, instant scheduling decisions can be taken, generating microscopic schedules.
With AI, one can develop iterative optimization approaches or graph-based models to compute low-level timetables with real-time decision support using heuristics. Artificial Intelligence also offers a way of training algorithms to react to disturbances quickly and yet with near-optimal solutions.
- Controlling the speed profiles of trains
An AI-based conﬂict resolution scheme shall not only achieve hard signalling (signal aspects) but more optimum approaches such as train speed management. Both the energy consumption of the train and total delay depend on the speed proﬁle used between stations. Using Reinforcement Learning (RL) or dynamic programming, energy-efficient speed proﬁles for single trains can be computed at the initial level. However, for broader applications in the country, the future possibility is the development of AI techniques that can:
- Interact with human train operators without increasing their load,
- Be implemented by humans in the loop, and
- Detect obstacles on the tracks.
- Delay Prediction and Reduction
One major role of Artificial Intelligence in Indian Railways can be the prediction of train delays. This is an important consideration for the highly limited nature of railway networks. Currently, there is no mechanism in IR to take corrective actions for the delay in train timings. Such delays are caused due to train priorities, downstream conﬂicts with other trains, freight loads, and irregular stopping times. A human cannot process all of these factors simultaneously, or come up with an optimal solution for the network as a whole.
Accurate delay predictions due to the incorporation of Artificial Intelligence in Indian Railways would help dispatchers (controllers) in downstream portions of the network. It would also improve the passenger experience by providing early updates regarding their journeys. A system to predict delay time would learn from past train delay data, predict how long each delay will be, and use a cloud-based service to deliver updates.
An AI-driven approach is a ‘sense-analyze-respond’ system to predict and correct delays. The ‘sense’ part of the program gathers data about the status of trains in the network. The ‘analyze’ part calculates the implications of each possible option. And the ‘respond’ part allots the computed track resources to each train based on physical capabilities and safety standards.
The foolproof working of the signalling system is important for safe train operations. Railways completely depend on the health of its signalling assets along with real-time information. Most of the delays happen due to the failure of signals. So far, Indian systems follow a manual maintenance system and find-and-fix methods. But the adoption of Artificial Intelligence in Indian Railways can help predict failures by remote condition monitoring of the system well in advance.
This can be possible by embedding smart sensors on critical rail components and take necessary preventive actions. Inputs shall be collected on fixed intervals and sent to a central location (such as operations control center or OCC). As a result, any problems in the signalling system would be detected on a real-time basis.
Trains equipped with smart sensors and GPS transmit component-wise health status and location to the AIC (Asset Intelligence Center). The AIC which maintains the digital database of all railway assets also collects inputs from the safe distance warning (SDW) system embedded in the track-side cameras about the train/wagon defects and electronic damage notifications (also transmitted to the driver).
Once such information is gathered and integrated, data analytics in the form of RAM / LCC (Reliability, Availability, Maintainability and Lifecycle Cost) which calculates the cost overhead in maintaining the particular train component at a given time and conditions and also generates a digital Rulebook (say, Rulebook 4.0) which shall provide an easy access to the maintenance policies in the form of a structured data model for use by concerned operators and workshop staff in the future.
The AI-recommended decisions based on dynamic algorithms and policies as per the digital Rulebook are then encapsulated into a consolidated maintenance schedule inside the Digital Fleet Control Module and is compared against the workshop capabilities to generate demand for material and labor. This demand is eventually fetched to the workshop Digital Interface in the form of digital order.
Fleet control module of a railway system illustrating processes involved in digital decision making is stated as under:
- Smart Locomotives – Equipped with sensors and GPS modules to send component based condition and location information continuously.
- Electronic Damage Notification – Enables the train driver the digital recording and transmission of defect information of the locomotive.
- Smart Wagons – Sending location data and specific status information such as shocks and loading status.
- Safe Distance Warning (SDW) – Identifies wagon defects by camera systems placed e.g; at cameras across the rail tracks supported with image recognition technology.
- Asset Intelligence Center – Integrates and provides all vehicle information consistently and standardized structure for live analytics.
- RAM-LCC Reports – Deliver required information to identify cost potential continuously. An integrated tool provides LCC diagrams and supports fleet control and technical vehicle development.
- Condition Based Maintenance – Optimizes and extends rules and knowledge to maintain all vehicles individually and condition based.
- Rulebook 4.0 – Provides all maintenance policies by a modular structured data model. Gives digital access to task specific maintenance policies – also on tablets in the workshop.
- Digital Fleet Control – Clustering of condition-based maintenance demand for vehicles and live matching with workshop capacities to control the workshop supply.
- Workshop Management System – Digital order management of the workshop. Complete digital data collection and processing. Further services such as automated material ordering to be processed digitally.
- Optimization and Automatization of Processes – Provision of dashboards and apps for end users as well as by direct integration of information in operational sales and production systems.
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Indian Railways can have remarkable improvement in asset management and other technology based services using IoT for Rolling Stock like Coaches, Wagons and Locomotives. The optimal use of assets can be facilitated once their exact location is known in real time. Track maintenance can become better and manpower can be effectively utilized. The great pressure that railways is facing due to the whopping wage bill and its severe criticism by experts can be eased once the handheld devices can enable management to optimally deploy staff for maintenance works. The assets will have sensors depicting their health and with use of intelligent monitoring systems, they will reach the right location at the right time. IR today is dependent heavily on supply chain partners. Lot of time and effort is wasted in pursuing the supplies, gaining access to information of vendor. All this can be automated using IoT & AI. The role of purchase department can be limited just to give the purchase order, the balance work can be handled by intelligent systems when the network has information on consignments, stock position etc. IoT is the future, and it has already arrived.
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