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Avoiding bumps in the road when designing AI-powered traffic-management systems
4 Min Read
Since its introduction in the 1950s, artificial intelligence (AI) software has transcended its theoretical existence in research labs to become omnipresent in our lives. This early AI research software has not only fueled an explosion in efficiency, but it has also opened the door to entirely new opportunities in business, education and government. Most of us use AI software daily in e-commerce, banking, health care and insurance.
In this article, we focus on an industrial sector where AI is showing great promise: transportation system management. Artificial intelligence can provide traffic system managers with real-time, predictive insights about traffic flow that can lessen congestion and improve safety. However, as explained here, a rugged hardware network must be developed and a software system in place to connect devices and transmit data.
AI in transportation management
Source: IEEE — Smart Town Traffic Management System Using LoRa and Machine Learning Mechanism
Artificial intelligence and machine learning (ML) techniques have gained traction in transportation system management as a key part of Smart City and Intelligent Transportation Systems (ITS) projects and initiatives. Some of the most significant and recent advancements in AI-based transportation networks happened with the integration of sophisticated ML algorithms into ITS technologies. ML algorithms “learn” from studying traffic patterns, pedestrian behaviors and other experiences so they are constantly improving their models and by extension, improving the safety of roadways.
Traffic management is highly complex. The only possibility of dealing with its tsunami of data is to abandon traditional traffic management approaches and turn control over to AI. Artificial intelligence will analyze, summarize and finally relay back to an administrator actionable insight that can reduce congestion, and possibly save lives. Sequenced follow-up actions can range from dispatching emergency services to adjusting traffic signal timing system controls. In most cases, responses are automated without human interaction.
AI’s use in ITS has been heralded as a new era of mobility, one characterized by improved driver safety and comfort, reduced traffic congestion, lowered carbon emissions, and greater speed and efficiency in supply chain management tasks. That’s not to say AI is without challenges. Infrastructure expense, managing competing priorities among city leaders and coordinating the multiple parties and technologies involved in projects have slowed adoption, as have public concerns over privacy and security related to sensing. Despite these challenges, the future of AI in transportation holds immense potential for startups and private companies to pitch their solutions to authorities.
Practical uses of AI in transportation
Artificial intelligence integrated with ITS creates a context-aware solution that merges real-time data from connected road infrastructure with predictive analytics to effectively coordinate traffic across key city arteries. Today, we are on the cusp of a proliferation of traffic applications, technologies and services where AI integration takes center stage, namely:
Autonomous vehicles: AI is revolutionizing the development of autonomous vehicles. Advanced AI algorithms and machine learning methods, such as deep learning, enable vehicles to perceive their surroundings, make real-time decisions and navigate safely. As AI and machine learning technology continues to advance, we can expect increased adoption of self-driving cars, trucks and even drones, which have the potential to improve road safety, reduce traffic congestion, and enhance overall transportation efficiency. Despite concerns around the technology and its ability to safeguard passengers from harm, KPMG has predicted the adoption of self-driving vehicle technology could reduce the frequency of accidents by approximately 90 percent.
Smart traffic management: AI optimizes traffic flow by analyzing real-time data from various sources, including sensors, cameras and connected vehicles. By using AI algorithms, smart transportation systems can dynamically adjust traffic signals, manage and predict traffic patterns, and help drivers find parking spots to improve the overall performance and efficiency of road networks. Besides helping traffic planners, insights from AI assist commuters with key details on traffic predictions, accidents or road blockages and provide suggestions on the shortest routes to their destinations.
Road enforcement: When a car violates a speed limit or other law, a traffic enforcement camera system, which consists of a camera and a vehicle-monitoring device, detects and identifies the offending vehicle and immediately tickets the driver based on the license plate number. This is referred to as mobile license plate recognition. Tickets for moving violations are mailed. Cameras also identify red light violations, illegal railroad crossings, HOV occupancy offenders and cars traveling in lanes reserved for buses.
To read the complete article, visit American City & County.
