Artificial intelligence and smart transportation: The future of modern cities

Artificial intelligence and smart transportation enhance the driving experience, optimize logistics, and ensure better management of vehicles, roads, and infrastructure. Let's Learn how AI experts can help transportation companies apply this technology and solve potential challenges along the way.

1.Market trends and statistics on artificial intelligence and smart transportation

According to MarketsandMarkets' forecast, the smart transportation market size will increase from 122.4 billion USD in 2023 to 248.7 billion USD in 2028 with a compound growth rate of 15.2%. . The main factor driving the growth of this market is the increasing investment and interest of the government as well as the interest of users. Governments around the world have a strong vision to improve modern transportation systems and meet future mobility needs.

By 2035, self-driving cars could generate $300 to $400 billion in revenue according to MCKinsey's forecast, and $600 million is the annual economic value that artificial intelligence and smart transportation can created in the Chinese market by 2030.

Overall, market data shows that smart transportation is receiving a lot of attention from governments, vehicle companies, and users and that an industry will grow and develop strongly in the coming years. future.

2. Applications of artificial intelligence in smart transportation 

2.1. Advanced Driver Assistance Systems (ADAS)

Many automakers have long since begun implementing semi-autonomous features into their vehicles, such as advanced driver assistance systems (ADAS) to help with parking procedures, ensuring vehicle control. Control your vehicle in bad weather and avoid collisions. ADAS systems rely on AI-powered cameras and sensors to identify vehicles, obstacles, pedestrians or passengers' facial expressions through computer vision to provide warnings to the driver even actions to prevent human error.

2.2. Self-driving car

Self-driving cars represent the future of ADAS systems, as they rely on AI to fully automate the driving experience. Most of this technology is still in the prototyping and testing phase. However, companies like Tesla have pioneered self-driving and other self-driving vehicles in a number of situations with promising results. There are also self-driving taxi systems, truck convoy systems (coordinated movement of multiple trucks at close range), and automated navigation for container ships through lidar and video object recognition technology. .

2.3. Traffic management and road monitoring

The operation of a traffic management system is to deploy a widespread network of cameras and sensors to monitor traffic conditions, identify car accidents through computer vision, and make predictions about traffic conditions. traffic. This allows authorities to intervene promptly in the event of a traffic accident, speed up road repair and maintenance activities, and optimize traffic light switching based on density. vehicle.

2.4. Automatic license plate recognition

These solutions include HD cameras mounted on road poles, infrared sensors to ensure 24/7 surveillance, and image processing software to recognize license plates through OCR (optical character recognition). The ANPR system is useful in many management and security tasks, including travel time analysis to improve road planning, identification of vehicles violating traffic laws, and electronic payments for toll lanes Don't use cash.

2.5. Smart parking  

Artificial intelligence supports parking space search with the help of cameras and computer vision, which can be deployed both in indoor parking lots and outdoor urban areas. These solutions can be useful in many ways, including vehicle counting and space detection connected to parking space indicators, license plate matching to detect illegal parking spaces, time tracking to Easier invoicing and ticket payments. AI-powered cameras are also used to identify suspicious activities to ensure parking lot security.

2.6. Fleet management and route optimization

AI-based solutions can help transportation companies optimize supply chains by streamlining and coordinating fleets of vehicles, ships, and aircraft. Their operation is based on a combination of GPS, sensors, computer vision-enabled cameras and IoT devices deployed to collect data on weather, traffic, congestion or accidents. These tools are then combined with AI-based analytics systems to process that information, identify recurring traffic patterns through machine learning algorithms, and turn the data into recommendations. travel route or forecast potential traffic congestion.

3. Challenges of applying artificial intelligence and smart transportation

3.1. Data quality and availability  

AI models rely on large and diverse data sets to learn and make predictions. However, collecting and integrating data is often costly, time-consuming and complicated. Furthermore, data quality can vary depending on the source, format, accuracy, and completeness of the information. Transportation planners therefore need to ensure that they have access to reliable and relevant data sources, as well as methods to validate and standardize data for AI applications. .

3.2. Ethical and social implications

AI can have a positive or negative impact on many different aspects of transportation, such as fairness, privacy, security, accountability, and transparency. Likewise, AI can helps enhance the privacy and security of road users, but it can also pose risks of data breaches, cyber attacks or misuse of personal information. Therefore, transportation planners need to ensure that they adhere to ethical principles and standards, as well as engage with stakeholders and communities when using AI for traffic management.

3.3. Technical and operational challenges

AI is complex and rapidly changing, requiring high levels of expertise, infrastructure, and maintenance. For example, AI models need to be continuously updated and adjusted to reflect frequently changing conditions, as well as tested and validated to ensure their performance and reliability. Furthermore, AI models need to be integrated and coordinated with existing transportation systems and policies, as well as compatible and interoperable with other technologies and platforms. Therefore, transportation planners need to ensure that they have the necessary skills, resources and support to deploy and manage AI solutions effectively.

In general, artificial intelligence and smart transportation are increasingly developing and are the trends of modern cities in the new era. However, this is a long process that requires the consensus of the government, businesses and people. Follow 1C Vietnam now to update the latest information on artificial intelligence.