San Jose City Government announced on February 6th that, through the use of an artificial intelligence traffic signal priority system, the average speed of buses in the city has increased by 20%, effectively reducing commute times and improving punctuality. This initiative has become one of the representative cases in the United States where AI is being utilized to optimize public transportation services.
The system, known as Transit Signal Prioritization (TSP), has now been expanded to cover all bus routes in the city. By analyzing real-time traffic data, the system dynamically adjusts traffic light timings at intersections. This allows buses to more easily encounter green lights when approaching intersections, reducing time spent waiting at red lights and giving passengers a sense of faster and smoother travel.
Mayor Matt Mahan stated that a simple way to assess the effectiveness of a city government is to look at the smoothness of basic public services, such as bus punctuality and road safety. By making buses run faster and more reliably, valuable time can be saved for daily bus commuters, demonstrating the government’s ability to deliver results in the most crucial areas.
The system, provided by the transportation technology company LYT, utilizes AI and machine learning technology to analyze traffic flow on the roads in real-time, assisting the signal systems in making optimized adjustments. Each bus is equipped with communication devices to engage in real-time “conversations” with traffic lights along the route, enhancing overall traffic flow and reducing traffic congestion and greenhouse gas emissions.
Currently, nearly 150 intersections in the city have implemented this technology, covering multiple main roads and high-traffic bus routes.
The key to quickly deploying this system citywide lies in cross-departmental collaboration and securing stable funding sources. The city government closely collaborated with the Santa Clara Valley Transportation Authority (VTA) to integrate bus operation data and signal management systems, successfully obtaining transportation grants from both the state and federal government. About 90% of the construction costs were covered by external funds, significantly alleviating the financial burden on the local government.
Looking back on the entire implementation process, this policy was not achieved overnight. In early 2026, the city government officially announced the AI signal priority system, which now covers the entire bus network in the city, marking the completion of the transition from a pilot project to a city-wide application. Prior to this, from 2024 to 2025, the related technology gradually expanded to multiple major routes and key intersections. As the system’s stability improved, it was gradually integrated into the overall traffic management framework.
The inception of this system can be traced back to 2023. That year, San Jose initiated pilot projects on two bus routes, testing the impact of AI real-time traffic data and signal prioritization on bus operational efficiency. The pilot results showed a reduction of approximately 50% in bus waiting time at red lights and a significant increase in punctuality, providing a clear empirical basis for subsequent large-scale expansion.
LYT CEO Tim Menard stated that the bus signal priority program brings about “daily improvements that can be felt”. It not only enhances passenger experience but also reinforces the role of buses as a fundamental urban service. Some transportation officials mentioned that the only “new problem” now is that some buses are arriving at stops earlier due to their smoother travel.
In addition to bus transportation, San Jose has also extended the application of AI to other municipal services in recent years, including detecting road potholes and obstacles, automatically reporting faulty traffic signals, and nighttime pedestrian warning systems.
Experts analyze that against the backdrop of continuous population growth and increasing transportation demands, San Jose’s choice to use AI to enhance the efficiency of existing buses and roads is considered a governance path with relatively controllable costs and evident results, providing practical experience that other cities can learn from.
LYT is a cloud technology company specialized in transportation and public services, leveraging artificial intelligence and machine learning technologies to assist cities in real-time vehicle-to-traffic signal connections, enhancing bus efficiency and road safety. Currently, their system has been deployed by various government departments across North America.