The use of data-driven strategies, such as machine learning, has absolutely modified the level of innovation in various sectors in this era. Machine learning has brought massive, positive, and rapid changes across industries. Be it tech, automation, healthcare, manufacturing, or many others. And the use of machine learning across industries is only increasing. As per research, the machine learning market size was $1.4 billion in 2020 and is expected to reach $8.81 billion by 2025. Almost an eight-fold increase in just five years!
And the penetration of machine learning in the mobility industry is no exception.
Machine learning has been one of the reasons for rapid improvements in the mobility industry in recent years. It has led to the emergence of new mobility application scenarios that were unthinkable a few years ago.
But, these complex use cases need rigorous MLOps planning to ensure that the development, testing, and deployment are done efficiently. It also ensures that these models perform as intended and make the necessary impact in the mobility industry.
Role of Machine Learning in Mobility
At a fundamental level, machine learning can do three things to improve mobility structures. These things are as follow:
Sense
Obtaining and interpreting data that comes from the network. This includes data such as traffic flows and weather. Machine learning apps gather data for a period to find similar trends and patterns.
Think
Creating insights to predict future conditions, like disturbances and congestion. Based on the observed trends, the machine learning program can predict future instances to a high degree of accuracy.
Act
Assisting humans in rapid decision-making, like optimizing routes and adjusting speeds. Complete automation in the mobility industry is a distant future. But, humans and machines can work together to solve issues faced in the mobility industry.
Current Trends in the Mobility Industry
Machine learning provides invaluable service when dealing with huge amounts of data. It can construct applications that offer real-time changes. It also supplements human interaction without having to be continuously updated. This allows applications to optimize and change routes immediately when traffic builds up. It also provides an allowance for human input at the same time.
The journey of enterprise intelligence for mobility is tied with the journey towards smart cities. Both, smart cities and improved mobility are becoming necessary. It is due to an increased population and changing consumer behaviors. As our lives become more digitized, mobility organizations use records to automate upgrades and innovate through machine learning.
The value of the smart cities' marketplace was $739 billion in 2020. It is expected to reach more than $2 trillion by 2026. With mobility being a big part of smart cities' development, it's a core focus area for machine learning.
Use Cases of Machine Learning in Mobility
Some key areas wherein machine learning uses the collected data are public safety, traffic management, and public transit. In Phoenix, Arizona, NoTraffic, an AI-based traffic management system, has received the green light. It's a self-sustaining system. It uses traffic records to update the traffic light settings automatically. It's a massive shift from the conventional traffic time management system.
Another example of such a shift is displayed by the Hop Fastpass payment system. It is created by Tri-Met. This system deviates from the vintage paper payment system. It allows people to shop for tickets through a singular “digital wallet.” The ride-share providers like Lyft and Uber are part of the Mobility-as-a-Service ecosystem as well. They have partnered with city transportation systems so that residents can plan and pay for their trips with ease.
In the future, we can even see all transportation managed with the aid of machine learning-based transportation hubs. This will lead to more organized and efficient transit.
Expected Future of Machine Learning in the Growth of Mobility Sector
Some of the expected changes we can see with machine learning in the mobility sector include:
Seeing Structural Defects
Reality modeling blends machine learning and deep learning in recognition of images. This enables in evaluating and comparing the preceding photographs of the automobile components with the current ones. We can find out if there are any defects easily. Currently, this task is performed by engineers and mechanics. But soon, it will get replaced by a virtual system. It aims to maintain the flow of vehicles, save time, and enhance client satisfaction.
Visualizing Benefits
Machine learning will assist in preparing the 3D models of roads. These models will be used to analyze how the current traffic flow affects the safety of people. The data collected will be evaluated to make future assumptions. This will assist in decision-making related to whether the street needs to be widened or reconstructed. This technology plans to use drone carriers. The drones will collect aerial and precise details of the area.
Then, the plan is taken into the advanced stages for implementation. This will ease the process of regulating the traffic by considering the buildings and pedestrians. It is helpful to first visualize a 3D image to analyze the benefits it might get when turning it into reality.
Autonomous Vehicles
Autonomous vehicles are already a reality. Thanks to machine learning, those are set to provide extra mobility to thousands of vision-impaired and disabled people. They will also enable deliveries in remote regions. It will also improve road safety and reduce traffic-related incidents. Autonomous vehicles will see a complete overhaul due to machine learning.
Others
Neural networks and computer vision aided by machine learning and some other futuristic technological advancements that are being conceptualized by many giants in the transportation and infrastructure industry. It is anticipated that neural networks could research the objects from the pictures captured and recognize them in the future. The technology also aims to enhance profitability and productivity levels.
Conclusion
A brand new age of machine learning rises over the world. The destiny of mobility is being reshaped on a worldwide scale. Players in the mobility zone are leaving no stone unturned. They are offering immaculate experiences by leveraging leading-edge, modern technology. Mobility is already starting to revive itself from the pandemic's effect. It is aiming to accomplish an upward boom trajectory in the post-pandemic era, thanks to tech-like machine learning.
