The Internet of Things is used to Develop a Highly Smart Railway

Smart Railway
Smart Railway 

The world's Smart Railway networks are becoming increasingly congested; trains are traveling at quicker speeds and carrying more passengers or bigger axle loads than ever before. As a result, the railway industry needs new information technology (ITs) to keep up with its growth. Railway systems now rely on information technology almost as much as they do on physical assets, and this reliance is expanding as these systems confront increasing demands in terms of resilience, reliability, and capacity. 

The framework and accompanying technologies for a smart railway based on the Internet of Things (IoT) and big data will be discussed in this chapter. On the one hand, we show the design of a Smart Railway, which is separated into four layers, namely the perception and action layer, the transfer layer, the data engine layer, and the application layer, and examine the advanced and most concerning technologies in each layer. Technology-driven innovation is quickly becoming a must for business survival. In countries with a poor degree of digital services, the epidemic has expedited the digitalization of train travel. This is primarily to promote cleaner and safer travel experiences. Because of improving rail market conditions and shifting attitudes toward train travel, the pandemic could accelerate the migration of passengers from aircraft to trains.

We introduce the intelligent rail inspection system, which can be considered a case or application of the smart railway. The Smart Railway demonstrates how the Internet of Things and big data may be used to improve existing railway infrastructure. COVID-19 caused regular railway operations to be disrupted, leading railway traffic profits to plummet. The pandemic has compelled railway operators, particularly in metropolitan metros, to adhere to safety standards such as keeping social distance and wearing masks. With countries opening up and demand for both passenger and freight transportation beginning to revive, train operators must plan for long-term profitability and success in the post-pandemic world. 

The growing demand for rail services puts a strain on existing systems, necessitating the optimization of existing passenger and freight timetables in order to maximize throughput on existing rail infrastructure. Rail assets must be properly scheduled, monitored, and maintained for an efficient rail operation. Due to downtime, maintenance plans diminish asset productivity. This downtime is exacerbated by manual diagnostics that have a low success rate. Smart Railway authorities place a strong emphasis on condition-based and predictive maintenance solutions to improve efficiency and save time consumption. These technologies aid in prompt asset monitoring and efficient asset scheduling, reducing downtime. Condition-based and predictive maintenance are based on real-time data, which reduces the need for manual diagnostics. Rail asset data can be utilized to maximize the usage of rail assets. It also allows for asset maintenance on a timetable, as well as resource intensities and costs.

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