The railway is one of the most used transportation services used by Indians. It connects the head to toe and right to left in India and serves as a major lifeline for transportation.
On average approximately 1.3 crores of passengers travel in trains every day. Timeliness, service, safety, and revenue serve as the four pillars of Railways.
ML/AI in railways can effectively help in strengthening these pillars and can serve as concrete solutions to the problems faced. I would be writing a series of blogs on each pillar so that we can go to the depths and find out possible solutions for problems pertaining to it.
Today let’s take the first one.
Timeliness
“Punctuality is not just limited to arriving at a place at right time, it is also about taking actions at right time.”
― Amit Kalantri
Problem Statement
The sole aim of this pillar is to ensure that each train departs on time and arrives on time. To increase your perception of types of trains below are some which I can identify.
- Intercity normal passenger.
- Special super fast like Rajdhani, Shatabdi, etc.
- Goods trains.
- Local city trains.
Multiple trains need to run on the same track with pinpoint accuracy to ensure everything goes well. In the real world, this is not possible. Delays are introduced in the system which causes trains to deviate from their ideal timings. What causes these problems?
- Mechanical Faults
- Technical Snags
- Human Interventions
- Animal Interventions
- Weather
- Disasters
- Many more…
How can ML/AI in railways help to solve these issues?
Mechanical Faults
In a railway subsystem, there are ON-TRAIN and OFF-TRAIN mechanicals. The lifeline of the spares must be estimated to ensure proper servicing and change. Factors such as load, traveling time, and whether can be taken into account. In an advanced version of ML/AI, the working noise of a mechanical can be heard and converted to provide its current condition.
Technical Snags
The failure of signals is one of the major reasons for train accidents and delays. ML/AI in railways can be introduced to ensure all signaling systems are working correctly and if any issue is found a real-time decision can be taken on it.
Advanced versions of ML/AI could inform the route trains directly about the issue and provide a corrective measure.
Weather
In India, the weather plays a very crucial role in the management of trains. During winter and rainy seasons many parts of the country witness extreme weather conditions which causes trains to get delayed.
An early estimation of whether can ensure that corrective decisions can taken before the problems hit.
An implementation of AI in railways could help in determining possible delays due to the current weather conditions and passengers could be well informed about the probability of the delay to avoid any hassle.
The speed and movement of the train can be efficiently managed so that it reaches the destination at the stipulated time.
Conclusion
Time is said to be costlier than money. To avoid losses in time, ML/AI in railways could greatly increase the efficiency of the railway system.
References:
Smart technology to detect faults in railway coaches, enhance safety
Railways to use artificial intelligence to prevent signal failures
Image of the Indian Railways Map taken from Trainpnrstatus
Next Step
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