AI for Railways
Artificial intelligence (AI) can be adopted in the railways to address various challenges including safety operational efficiency customer experience and more specifically driver training or training of the loco pilots. AI is the fundamental enabler that will provide safety, scalability and flexibility in a dynamic external environment, demographics, urbanization, and multiplicity of public and freight mobility means. This article focuses on the potential of artificial intelligence in improving the efficacy of training provided to loco pilots in the Indian Railways.
Implementing Artificial Intelligence for Railway simulations:
Loco-pilot training for driving skills has progressed from “On – the – Job training” to “theoretical classroom – based training” for operations to Motion based Simulation training based on computational science based on scientific models. Simulation models can be explained and reconstructed by domain subject matter experts and can be used in prediction in the explicit goal-oriented objectives. AI models do not require domain expertise / understanding and the models are developed with sufficient data by recognising patterns beyond the capabilities of individuals. The three stages of development of Loco-pilot training can be envisaged as:
Stage 1:
Use of Simulation data to develop a Machine Learning Machine Learning (ML) application that will identify the computational loss functions and will improve the model on specific test applications. This model is continuously improved and updated based on new data received. In this stage, most of the learning happens by virtue of digital structured data collected in simulation trials. Machine learning algorithms react to the simulation data collected and optimises the model and build & networks without needing or with minimal human intervention. These Physics informed Neutral networks (PINN) will be highly accurate and validated through simulation data.
Stage 2:
Synthesis of PINN and Big Data: Field data from incidences, data from Memotel / SPM / TCMS / RTTIS / KAVACH / OMRS, Passenger data on stations, data from IoT devices, personal data of Loco pilots from their journeys, behaviour and psychological data coupled with regional biases and many more.Visual data from CCTV, Vehicle based track cameras, CVVRS require development of specialized neural networks commonly termed as convolutional neural networks (CNN). They use mathematical operations called convolution in place of general matrix multiplication in multiple layers of visual data. CNN process pixel data and is used in image recognition and processing. Similarly audio data analysis is needed for transforming and interpreting audio signals recorded by various digital on-board systems meant for troubleshooting and predictive maintenance. Decoding human communication between the LP and ALP, with passing stations, communication with TLC etc. would all be logged and analysed.
This data is what we commonly understand as Big Data. This is unstructured complex data and human intervention is not possible. Neural transformation models are generated from this unstructured data mimicking the learning process of the human brain. This may be termed as Deep learning or the capability to decode unstructured data and set up neural transformation models. Deep learning algorithms use hierarchical multi-level neural networks in which the abstraction levels slowly increase through non-linear input data transformations.
Stage 3:
Use the ML and Deep learning models developed on a dynamically changing environmental data, Digital Twins of the train and the external environment to enable computationally accelerated learning simulations, personalized training, build exercises for potential vulnerable safety scenarios never seen or experienced before. These models will mature and over time improve the precision of predictability and will help in optimizing delivery of comprehensive and personalized training in all required domains / skills with assured effectiveness and reliability.
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