Artificial intelligence (AI) is beginning to change the railway industry in a big way. From streamlining operations to improving safety, AI is set to have a major impact on how we travel by train.
As we enter the next stage of AI applications, it’s more important than ever to make accurate decisions. That’s why we’ve rounded up the top stories and new articles to help you stay informed.
For the most part, automation has been used to reduce the cost of operations, thus increasing profitability and making trains more reliable. In recent years it has also been used in other areas such as developing better passenger information systems.
AI is expected to boost automation even further by providing more accurate predictive maintenance and improving safety through better decisions. On-board technology that can make autonomous train platooning a reality is already helping automate some tasks, for example switching signals and diesel generation.
What is AI in Railways
AI is the simulation of human intelligence processes by machines, especially computer systems. It is related to the similar task of using computers to understand human language, as in natural language processing, but in artificial intelligence, a machine is able to ‘reinvent’ itself and improve its performance on a specific task over time; this learning ability and adaptability are also known as strong AI.
The history of artificial intelligence goes back several decades but has accelerated dramatically in recent years with the proliferation of low-cost sensors and cloud computing to store and process the data they generate.
Brightline has had to rely on public education efforts to keep people off the tracks in the past; by working with Remark’s AI technologies, the future of high-speed rail is set to be considerably safer.
In order to reduce maintenance costs and improve operational efficiency, a machine learning model was developed to predict the health of rail network assets. The model was trained on a dataset of rail transport incidents and failures.
The results showed that the model was able to accurately predict the health of rail network assets, and this information can be used to improve the maintenance of the rail network.
How can AI be Used to Manage Railroad Crossings More Efficiently
Normally, when vehicles and trains head into a railroad crossing, they are controlled by gates that open or close at the appropriate time. The length of time they stay open is determined by traffic signals. The problem with this is that it can lead to congestion in some areas due to the way in which traffic signals are timed.
With the advent of artificial intelligence, however, it is possible to ensure that these crossings are managed more efficiently. Onboard computers can be used to take data from sensors on each vehicle and continuously compare it with real-time conditions and predictions about future traffic flows – such as the location of following vehicles – allowing them to make more informed decisions about when and how long a gate should be open.
In Europe, Switzerland has partnered with Nokia to increase train crossing safety with real-time surveillance and AI algorithms.
Decision-Making & Operations
AI will have a significant impact on railway operations including management structures, personnel roles, scheduling, and even revenue forecasting. AI systems can sift through vast amounts of data and make better use of the information available to them.
As they gather new information, they learn even more and their situational awareness becomes increasingly accurate over time. Using AI, railway companies will be able to optimize operations and increase reliability, maximizing revenue and reducing costs.
For example, AI can help railway companies improve their scheduling by using historical data on travel trends to predict likely passenger numbers for an entire year – well beyond what previous systems could do.
This may help companies avoid the need to invest in a costly rolling stock just as demand is beginning to fall or give them time to plan ahead for seasonal spikes in traffic that would otherwise require extra trains to meet demand at peak periods.
AI can also improve fleet efficiency by using historical data to predict when parts will need replacing and better manage maintenance schedules.
Safety & Risk Management
In an industry where safety is top of mind, AI will be used to improve decision-making and reduce risk. This may be through the use of AI-enabled sensors that monitor crucial functions and send alerts in case of failure or other issues.
AI systems can also help optimize scheduling, improving deployment of staff, equipment, and rolling stock to address areas where safety is a concern.
Using AI systems with real-time operating information and predictive data about travel patterns, train companies can monitor risk and make better decisions about whether to proceed.
Even the decision to put a train into service can be made more efficiently by AI, reducing the need for manual approval on safety grounds or letting trains depart before human operators are ready.
Customer Service & Retail
AI will have a significant impact on how passengers are served and how the rail industry engages with them. Customer care will become more personal and AI systems will be able to adapt the level of personal service available to passengers to their needs.
This may involve placing an extra train on standby if demand is high, for example, or making it possible for people to book tickets in advance by tapping their phone against a display rather than speaking to a human.
Similarly, AI could also be used to improve retail offerings by offering more accurate pricing information from data mining in real-time and serving up relevant offers based on passenger preferences.
Benefits of AI In the Railway Industry
Railway operations are becoming safer, smarter, and more reliable as a result of intelligence from AI-driven systems and applications, dramatically improving the passenger travel experience and freight transportation services.
It is now possible to use AI to monitor traffic flows and control crossings so that delays can be reduced. The technology has also been applied to many other areas in the railway industry by leading rail companies. Here are some examples of how AI has been used to improve passenger services:
AI is used in the case of personal mobility, such as ensuring that trains are on time, so passengers can get where they need to go quickly and safely. This is done through real-time communication between driver and train which allows them to know if the doors will open on time, if there is space inside a carriage or if a station has a platform that is currently unreserved. Thanks to this communication, the train can adjust its speed, move slightly left or right and change the route that it takes on the journey.
2. Tickets & reservations
Thanks to AI, train companies can gather data from passengers and use it to improve their services. By monitoring the way in which people travel, AI systems can make better predictions about when demand will be high or which routes will be most popular. They can also provide better information about ticket availability and help travelers find the best deal for them.
The information gathered by AI systems is even being used to help train companies plan for capacity needs well into the future, allowing them to make more accurate estimates of travel patterns and adjust plans accordingly.
AI is being used to automatically manage parking outside stations. This is done by using sensors to detect when a car has arrived and how many spaces are available. Those sensors can then communicate with the train station’s parking lot, allowing it to open or close at the appropriate time.
This allows people who drive to their destination to find a place to park more quickly and easily. It also helps reduce congestion in areas around stations, reducing the potential for accidents and helping reduce pollution as well.
4. Supply Chain Management
Thanks to AI, it is possible for train companies to run their supply chain more efficiently. By using data from sensors on trains and passenger information cards, it is possible for them to predict demand and equipment needs as they approach stations.
This allows them to make more informed decisions about how many trains to send through each station and when particular parts will need replacing. It also helps devices in the supply chain such as those that use electricity for maintenance keep running.
5. Fleet Management
AI can also be used to help manage the fleet of trains owned by train companies. By using data from sensors on each train and information about their usage, it is possible to make better decisions about when they should be serviced and how they should be stored at the end of a journey. This helps reduce costs and improves safety by ensuring that each vehicle is in good condition before it is sent out on the track again.
Artificial Intelligence has been used to great effect in many areas of the industry over the past few years and it is helping to transform transportation today, as well as preparing for similar changes in the future.
5 Challenges of AI in the Railway Industry
AI is projected to become increasingly incorporated in rail networks around the world, bringing train transportation into the twenty-first century.
Artificial Intelligence has a wide range of applications for the rail industry which will help it to become a safer and more reliable place. However, there are a number of challenges that must be overcome before this can happen. These challenges include:
1. Lack of Data
This is one of the biggest challenges that AI systems face, since it is impossible for them to learn without having access to large amounts of relevant data. For AI to work in the railway industry, digital train and track logs would need to be collected and saved over an extended period or locomotives could carry sensors that record information about how they are being used, like those found on smartphones.
2. Lack of Knowledge
This is another challenge that AI faces, as any AI system requires a knowledge base that it can draw upon. Currently, this is dealt with by the inputting of definitions and statements from users or engineers. As the number of different applications for AI grows, however, there will be more need for this kind of information and more people to deal with it.
3. Lack of Standards
There are also no consistent standards for AI to work on in the railway industry. While software systems have been developed by Automation Group to help designers take advantage of AI technology, other groups have developed their own proprietary software platforms that do not necessarily work in tandem with each other.
4. The Need for Expert Input
AI systems require human input to develop and grow. This can include programming computers with instructions, as well as providing data, algorithms, and solutions to problems. A lack of training programs may result in a shortage of specialist experts in the future.
Collecting training data can be a time-consuming process, but it’s essential to train AI. Once you have a good amount of data, you can begin to analyze it to see what patterns emerge.
This will help you to analyze site traffic better and make more informed decisions about how to train your AI. Keep in mind that data collection is an ongoing process, so you’ll need to keep adding new data as you go.
5. The Need for Greater Processing Power
As artificial intelligence gathers pace, so does its demand for processing power. This is a key challenge that railway companies must overcome, as it will only increase over time as users seek more seamless AI services from the technology and more specialized applications that can learn from their own data sets in order to improve their performance.
The Role of AI in the Future of the Railway Industry
The COVID-19 epidemic has altered priorities and boosted demand for railway transportation in numerous ways. Artificial intelligence is already playing an important role in the railway industry today.
In the future, it will allow trains to be managed more efficiently and make it possible for people to travel on them in a safer and more streamlined way. We look at some of the ways that AI is being used today by train and rail companies.
There are four main ways that AI plays a significant role within the railway industry:
1. Usage of Mechanical Computers in Train Engines
The engineering sector is another major application scenario of artificial intelligence. The use of computer-aided engineering and simulation (CAESM) tools can reduce time to market and costs, while at the same time increasing safety throughout the entire lifecycle.
2. Automated Driving
Automated driving is the first use of artificial intelligence in the railway industry. Most modern trains are built with a computer system that can adjust and manage their engine’s speed, as well as activate and deactivate various systems on board. Engineers can also use this system to train drivers on how to operate the train safely.
3. Spotting Violations by Unmanned Vehicles
Using AI, it is possible to build cameras that will detect when another vehicle is violating traffic rules such as moving across the track at an unsafe speed or crossing near a junction while a signal is blinking red. This information can be sent to the train’s control centre, allowing it to react accordingly.
4. Machine Learning for Train Inspections
Machine learning can also be used for research and development. This process involves the creation of computer systems based on a set of instructions or algorithms that allow them to learn from data and solve problems.
Engineers are creating neural networks that embed knowledge from experts and then apply it to a new context. They are used to teach AI how to identify defects and make the best decision moving forward, in order to increase compliance with standards during inspections.
The Impact of AI on the Railway Industry
AI can also help with proactive traffic safety management by utilising systems like computer vision, which can potentially avoid crashes by evaluating data for dangerous hotspots.
As the technology of Artificial Intelligence gathers pace, it is clear that AI will have a major role to play in the future of the railway industry. Making trains more environmentally friendly and more efficient and by easing congestion on our roads and railways, it will help improve the quality of life for billions across the globe by reducing air pollution and greenhouse gas emissions.
Some argue that artificial intelligence may lead to mass unemployment because many jobs will be taken over by computer systems. However, in many cases, this technology simply takes over the mundane parts of tasks such as monitoring equipment or issuing advisory messages to drivers.
AI will also help to ensure a reliable service for passengers. It will allow trains to operate more efficiently and suggest the most appropriate route, meaning that passengers have a better experience at stations. We could also see driverless trains become possible in the future.