Artificial intelligence has changed the way the transportation industry works today as compared to the earlier years. Although people do not really see Artificial Intelligence in action, individuals and businesses are persistently surrounded by it.
Since its invention, AI has been used in various industries to create robots, home appliances, chatbots, Google Now, and Tesla cars. It has been consistently growing and changing over the years bringing more innovative insights and tools to the transportation industry.
AI uses neural networks that recognize patterns based on previous data for enhancing automation in vehicles. According to an industry report, the market for AI in the transportation industry was $1 billion in 2016. Further projections of the financial study say that it will grow at an annual rate of 17.87%.
Let’s take a closer look at how it is transforming the transportation industry in multiple ways:
AI transportation uses a complex combination of sensors, cameras, and radar to drive the car without any help from the person sitting inside. Various companies like BMW, Audi, and Tesla have been developing and testing such automation.
This high level of AI in transportation market size is not only a difficult task to achieve but is very complex too. As per the Google tests, several self-driven cars have navigated over 140,000 miles. Just like the human brain that multitasks while driving, complex neural networks have been developed that identify similar patterns.
Identifying traffic lights, people, curbs, trees, street signs, traffic jams, and everything on the roadways is a part of the data in the neural networks. With more navigation, the system will become more and more intelligent.
Moreover, using self-driven vehicles is essential for reducing logistics costs. Autonomous taxis are already tested in Tokyo, and according to industry forecasts, this technology will be helpful to increase public transportation in remote areas. The Tokyo experiment can be an excellent example of how AI is used in transportation already. Also, autonomous tracks can reduce the spending on transportation to 45%.
Living and working in any major city and struggling to make it to work or school on time is a tiring problem people face every day. AI-based traffic management is a step that can help to curb daily traffic problems and driver fatigue.
AI focuses on automated traffic signals, high-resolution cameras, real-time processing, automated plate recognition systems, to name a few. Recently, India’s capital decided to put to use such smart traffic management. Approximately 7500 infrared sensor cameras and 1000 LED boards are to be installed. AI tech will help to decrease congestion on the road by diverting traffic at peak hours using live feeds, sensors, and Google Maps. This can decrease manual interference to a minimum and provide a smarter and more efficient way of traveling.
Artificial intelligence in transportation can also be used to reduce traffic jams. The AI technology in the transportation industry collects the information about the route in real-time, sends the data to the cloud, and uses the analysis for traffic predictions. The system will notify the users of the best way to their destination. In this case use of AI in transportation will also improve route safety and diminish the waiting time.
Thanks to drone technology, beautiful aerial views can be obtained. However, drones can provide more information when combined with AI. For instance, AI drones can be used to scan construction sites, which might take more than a week if done manually. Also, there are proposals to use drones to manage and monitor traffic because they can give a much wider view of the whole area.
With the help of drones, the search for blood banks has decreased to 15 minutes in Rwanda. Drones with artificial intelligence can also be useful to avoid accidents on the road or to provide emergency help. This small toy-like apparatus is proving to be extra special when integrated with AI.
AI has not only helped in automation but has been proven to be life-saving. Artificial intelligence can identify patterns of extreme pain and other emergency conditions. This allows them to alarm local hospitals to send an ambulance or drive automatically to the nearest hospital.
According to a BBC News report, a patient suddenly suffered a pulmonary embolism, and his Tesla Model X AI car took him to a hospital in autopilot mode. Tesla’s use of AI has changed driving, but it also has the potential to save lives. The patient survived, all thanks to the swift decision made by the car’s AI system, which got the individual to the hospital just in time.
This is the future of AI in transportation. It can be used to alarm local ambulances rather than manually calling them, improving the service provided for emergencies.
Recently, autonomous trucks have become pioneering projects for AI companies. Cross-country trips are usually very tiring and boring but are more predictable, an ideal situation for AI to be used to drive these trucks. Since there are long, straight roads, the driver tends to fall asleep and crashes, which can be avoided by using AI.
A start-up company in California has developed an AI-based freight truck that made a 41 hour trip across the country to deliver butter. This was the world’s first cross-country trip by a commercial freight truck. The use of AI can save money that is spent on truck drivers and their needs.
More than half the public transportation is dependent on services like buses, trains, trams, subway, and taxi services. If AI directed public transportation is implemented, it would become much more efficient and easy for the commuters.
AI would collect and analyse the data provided by cameras and sensors. It would identify a faster route and avoid traffic lights to reach the destination faster. Data collection is in process at a huge level, for the integration of AI like:
- Purchases of tickets,
- Number of passengers,
- Number of stops,
- Rush hours, etc.
AI is a definite answer to streamline public transport and for smoother communication. It also will help to cut down carbon emissions into the environment.
Using artificial intelligence in the transportation industry is not a new thing, but implementing this technology for traffic operations in aviation can become game-changing. It opens new opportunities for airplane planning design and air-traffic management systems.
Many research projects use AI in the aircraft industry to improve the predictability of traffic at different stages of flight. For example, the BigData4ATM project uses data analysis of passengers’ behaviour and improves airport traffic in the future. Application of AI in transportation can also be used to ensure a high degree of air-grounded integration, which will make it possible to allay some tasks, and finally, will allow pilots to focus on the critical functions during the flight.
The railway industry used to be the most innovative transportation system, and it still can be with using AI for improving the management and operational process.
In 2018 a group of companies started working on driverless train prototypes for autonomous freight trains and passenger trains. Artificial intelligence transportation software allows the integration of sensors, cameras, and radars for creating the “train’s eyes” tool. The company plans to have a fully autonomous driverless train by 2025.
It is also essential for the railway infrastructure to predict possible collapses. Operational intelligence makes it possible to use the data from train sensors for accurate forecasting and recommendations for maintenance. Furthermore, AI helps the railway industry to evaluate long-term performance and find areas for improvement. Laing O’Rourke, for example, uses AI to reduce the time planning of logistics to 19 seconds. AI and transportation software solutions allow them to prepare for 23 days. In comparison, a human could schedule maintenance work only for three hours and one day ahead.
Shipping, Navigation and Ports
The last few years have been essential for the development of the shipping logistics industry, as many companies still use the vessel for delivering goods. Waterway transportation needs to analyse a lot of data for optimizing the shipping routes for ships of different sizes and with varying types of goods. Artificial intelligence in transport software allows companies to collect specific data that help in decision making, increasing the safety of shipment and energy efficiency of vessels.
Marine logistics and navigation depend on many changing factors, and it is usually challenging to decide on route optimization without a broad set of data. It also can be more efficient to have an on-land control for the ship. The MUNIN project, for example, examined the autonomous vessel that guided on-board but controlled from the land. This project improves the potential of using automation in the transportation industry for efficient ship navigation for quality marine delivery.
It is much easier to put an airplane or helicopter in autopilot mode. Recently there has been intensive research going on for auto-pilot helicopters to act as taxis. It will not only help to overcome traffic-related problems but also cut down on carbon emissions. Drone taxis will also reduce the need for building expensive city infrastructure. It became the most efficient decision for urban development. The future of AI in transportation will also influence the city-building industry.
They can cover longer distances in less time. AI-driven helicopters will be completely automatic; passengers just board it and reach their destination stress-free.
The benefits of AI in transportation are numerous and will increase and improve as it becomes more widely and efficiently used. Limited use of AI in transportation due to recent reports of accidents will diminish as progress continues. That is why constant attention and upgrading of AI is required.
At AppleTech, we know a thing or two about AI implementations for the transportation industry. We can help your business grow, too by enhancing your transport company to be empowered by the perfect AI powered solution.
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