How Can AI Solve Urban Traffic Problems?

AI can help improve urban traffic management in various ways

Urban traffic is one of the most pressing problems many cities face worldwide. Traffic congestion causes frustration and stress for drivers and commuters, leading to increased air pollution, greenhouse gas emissions, fuel consumption, and road accidents. According to a report by INRIX, a global leader in transportation analytics, traffic congestion cost the US economy US$88 billion in 2019, and the average American driver lost 99 hours due to traffic.

However, there is hope that artificial intelligence (AI) can help solve urban traffic problems. AI is a branch of computer science that aims to create machines and systems that can perform tasks that normally require human intelligence, such as learning, reasoning, and decision-making. AI has many applications in various fields and domains, such as health care, education, entertainment, and more. One of the areas where AI can have a significant impact is urban traffic management.

AI can help improve urban traffic management in several ways, such as:

Traffic Signal Control

AI can help optimize traffic signals to improve traffic flow and reduce congestion. For example, real-time data from sensors and cameras can adjust traffic signals according to the current traffic conditions and demand. Furthermore, predictive models can help city planners anticipate traffic patterns and make proactive decisions to avoid congestion. Some cities using AI for traffic signal control are Phoenix and Pittsburgh.

Smart Parking

AI can help drivers find available parking spaces quickly and conveniently. For example, smart parking apps can use data from sensors and cameras to show drivers the location, availability, and price of parking spaces near their destination. Moreover, smart parking systems can also use AI to optimize the allocation and pricing of parking spaces to increase revenue and efficiency. Some examples of smart parking solutions are ParkWhiz, Parkopedia, and Parknav.

Law Enforcement

AI can help enforce traffic laws and regulations more effectively and fairly. For example, AI can use image recognition and natural language processing to detect and identify traffic violations, such as speeding, running red lights, or illegal parking. Moreover, AI can also use speech recognition and generation to communicate with drivers and issue warnings or tickets. Some examples of law enforcement using AI are E-Challan, a system that automates the issuance of traffic challans in India; Waycare, a platform that uses AI to provide real-time traffic insights and recommendations to law enforcement agencies; and Gatso USA, a company that provides AI-powered speed cameras and red-light cameras.

Transit Planning

AI can help improve public transportation systems and services by providing better routing, scheduling, and demand forecasting. For example, AI can use GPS, sensors, and smartphone data to monitor the location, speed, and occupancy of buses, trains, and other vehicles. Moreover, AI can also use data from weather, events, holidays, and user preferences to predict the demand for public transportation and adjust the supply accordingly. Some examples of transit planning using AI are Moovit, an app that provides real-time transit information and directions; Remix, a platform that helps city planners design and evaluate transit networks; and Via, a company that provides on-demand shared rides using AI.

Automated Vehicles

AI can help enable the development and deployment of automated vehicles (AVs), such as self-driving cars, buses, trucks, or drones. AVs can use AI to perceive their environment, navigate their routes, avoid obstacles, and interact with other vehicles and road users. AVs can reduce traffic congestion by improving road capacity, safety, efficiency, and convenience. Some examples of automated vehicles using AI are Waymo, a company that develops self-driving cars; Navya, a company that produces autonomous shuttles; and Wing, which delivers goods by drones.

These are some ways that AI can help solve urban traffic problems. However, some challenges and risks are associated with using AI for urban traffic management. For example:

Cybersecurity Issues

AI systems rely on data from various sources and devices that could be vulnerable to hacking or tampering. This could compromise the security and reliability of the systems and cause serious consequences for traffic safety and efficiency.

Economic Questions

AI systems could have significant economic impacts on various stakeholders involved in urban transportation. For example, AI could create new jobs, eliminate existing ones, increase or decrease transportation costs, or affect income or wealth distribution among different groups.

Ethical Considerations

AI systems could raise ethical questions about the values, principles, and responsibilities of using AI for urban traffic management. For example: Who owns the data collected by AI systems? How transparent do AI systems use the algorithms? How accountable are the developers or operators of AI systems? How fair are the outcomes or impacts of AI systems?

Therefore, developing and using AI for urban traffic management responsibly and with human values in mind is important. AI can be a powerful tool to help improve urban traffic management, but it should not replace human judgment, oversight, and participation. AI should be used to augment and complement human capabilities, not to undermine or replace them.