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How Machine Learning Can Prevent Train Delays

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How Machine Learning Can Prevent Train Delays


The use of machine learning can help anticipate train delays, detect early signs of failure and plan routes based on changing needs.

Machine learning can improve rail travel both in the long and the short-term by minimizing train delays and ensuring high service quality.

Train delays can be really frustrating and disruptive, especially if you frequently commute by train for work. In addition to the trains being late, you might end up feeling so annoyed, you’re almost sure the railway operator has something personal against you. But the fact is, delayed trains affect millions of people all over the world, and there is very little operators can do to minimize such delays. This is because rail delays are caused by numerous factors that are interrelated, making it hard to assess the effects and devise solutions. However, using artificial intelligence applications, powered by machine learning, can help rail operators improve services by enabling intelligent decision-making and prompt action for minimizing train delays.

How Machine Learning Can Prevent Train Delays

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Train delays are caused by multiple factors, including infrastructural and mechanical failures, unfavorable weather, delays caused by passengers, and suboptimal scheduling. Using machine learning, rail operators can optimize and improve rail networks and minimize train delays.

1. Accurately Predicting Train Delays

 The already frustrating issue of delayed trains is further aggravated by the inaccuracy in the rescheduled time of arrival. With multiple trains sharing the same routes, one late train can delay all the trains scheduled after it. Depending on the number and frequency of trains passing through a route, delayed trains can have extensive ramifications, causing inconvenience to thousands or even millions of passengers. Machine learning is being used to accurately estimate how late a train will arrive at a station. To accurately predict the ETA, large volumes of historical data, along with the real-time positional and performance data, is constantly analyzed for every train. This allows operators to control and conduct rail traffic in a way that minimizes the effect of one delayed train on the schedules of others. This will ensure the overall system adheres to the schedule even if some trains, due to unforeseen causes, get delayed.

2. Minimizing Risks & Ensuring Timely Maintenance

A failure or defect in either the locomotive or the track in a railway network can lead to a complete halt of operations on a given route. This causes long delays with the estimated time for restoration contingent on various factors. The use of machine learning in the automotive industry has already proven its effectiveness in performing predictive maintenance, which can be applied to locomotives to eliminate delays due to breakdowns. Machine learning can potentially eliminate mechanical breakdowns by constantly monitoring data points that can indicate any impending breakdowns. A proactive approach towards the maintenance of trains and tracks minimizes the need for urgent repairs.

3. Better Management of Train Schedules

The massive amount of real-time data collected and analyzed through machine learning can not only improve the current railway operations but can also assist in making long-term improvements like laying new lines and planning new routes. Insights gained through machine learning enable railway operators and authorities to plan and optimize the use of the available trains and the related infrastructure. Suboptimal routes can be re-planned, or new routes can be designed, to ensure. This can be achieved using reinforcement learning, a type of machine learning that can determine the most optimal solution to problems by evaluating the results of previous actions. Optimizing routes and schedules can help in minimizing train delays due to congestion of passengers and uneven rail traffic.

The use of data analytics to make trains run on time is not a radically new or futuristic concept but is something that is already happening. In addition to minimizing train delays, analytics are also enabling rail operators to provide better customer experiences like real-time tracking of trains and gathering feedback through mobile applications. With the help of machine learning and big data, train journeys will become much more convenient.  



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TECHNOLOGY

Why Using AI in Education Could Be a Game Changer

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Why Using AI in Education Could Be a Game Changer


An assistant to teachers and a guide to students, AI in education can transform the entire education industry.

It allows teachers and students alike to extract helpful information whenever required and has several other benefits.

Why Using AI in Education Could Be a Game Changer Keyword phrase: AI in education An assistant to teachers and a guide to students, AI in education can transform the entire education industry. It allows teachers and students alike to extract helpful information whenever required and has several other benefits. Generally, the ratio of students to teachers in a classroom is relatively high, and sometimes, managing and giving the same amount of attention to all the students for a single teacher can be difficult. AI in education can act as the teacher’s assistant and help them in various areas. Technology is advancing in all sectors and proving to be a great boon. One such industry is education. The general market for AI in education is expected to reach $25.7 billion by the year 2030. A child’s mind is curious, and they have questions that humans may sometimes fail to answer correctly. To feed this curiosity, AI in education could be the solution whenever they need it.

Use of AI in Education

AI in education has the power to optimize both teaching and learning. It will enable both teachers and students to evolve and sharpen their skills. Here are some ways AI can prove beneficial in the education sector.

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1. Automation of Tasks

There are certain tasks, such as planning, grading and scheduling of lessons, that take up a significant amount of the teacher’s time. This increases their workload and decreases their time and ability to teach in a classroom. By automating such manual work, teachers can free up their time and focus on their core competencies, like teaching and aiding students with special needs.

2. Personalization of Courses

By understanding the student’s learning preferences, speed of learning, and extent of knowledge, AI can churn out personalized programs to help cater to the students and enhance and simplify their learning experience. For a teacher to manually do the same task could be time-consuming, but AI can come to their aid here and simplify the personalization of courses.

3. Solutions to Queries

AI-powered chatbots have access to the entire school’s knowledge base. The chatbots can answer any repetitive or general questions without the intervention of a faculty member. This helps to speed up the doubt-clearing process for a student so that they can continue with their studies without any interruptions for themselves or the teacher.

 

Integrating AI in education allows teachers to provide students with personalized learning, which helps to enhance students’ knowledge. This helps the child to learn at their own pace. Students also have the opportunity to resolve any queries quickly and not rely on the faculty to answer their questions. The deployment of AI in education allows students to learn better and faster and helps teachers to reduce their workload by automating manual tasks. From presentations to virtual reality, technology is poised to bring about many advances in the education sector.



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