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Physical SecurityTransportation/Logistics/Supply Chain/Distribution/ Warehousing

Security Strategy

The Future of Public Transit: Leveraging AI Analytics for Enhanced Operations and Passenger Experience

As AI technology advances, transit agencies embrace innovations to meet the growing demands of modern urban mobility.

By Anthony Incorvati
Man waiting for train

Lorado/ E+ via Getty Images

July 21, 2025

Public transit is on the verge of a technological revolution. The same AI-powered video surveillance devices that have been instrumental in keeping passengers safe and tools to support Operations and Maintenance (O&M) are now offering a new frontier of benefits: optimizing operations, improving performance, and enhancing the passenger experience to higher levels. This shift represents a further return on investment for transit systems, as technology that has historically been used primarily for security, live view of operations, and forensic tools now contributes to efficiency and service improvements.

As ridership returns to pre-2020 levels, data acquisition and AI-driven analytics are becoming increasingly crucial in winning back public confidence. Other industries, particularly in the private sector, have long embraced AI-driven optimization — so why has the transportation sector been slower to adopt these advancements? And what makes today’s environment different?

The Pace of AI Adoption in Public Transit

Public transit systems have historically prioritized safety and reliability above all else. Unlike many other industries, where innovation and agility drive rapid change, transit agencies operate in safety-critical environments where technology failures could jeopardize passenger and employee well-being. Any new implementation must be rigorously tested and proven effective before adoption, creating an inherently cautious approach to innovation.

Furthermore, public transit involves complex, safety-critical and interdependent systems with numerous unpredictable variables, including weather conditions, traffic patterns, and fluctuations in ridership. These challenges have traditionally made transit agencies in many cases, risk-averse when considering new technologies, no matter how promising or advanced. With so many factors to consider, and with failure potentially resulting in everything from service outages to loss of life, all innovations must be thoroughly vetted before implementation.

However, the landscape is changing, and AI is on the cusp of being adopted more widely across the transportation sector. The question is no longer whether AI should be integrated into transit systems, but rather how and when it will be leveraged to its full potential.

Why Now? The Case for AI in Public Transit

The rapid advancement of AI-based and specialized analytics, coupled with their seamless integration into existing video sensor devices, has made their adoption more accessible than ever before. Unlike previous waves of emerging technology, AI-powered analytics have now been widely tested and successfully implemented in numerous industries. In the transportation sector, airports have emerged as leaders in AI and computer vision adoption, and many have already seen significant ROI from AI-driven improvements.

Post-pandemic ridership recovery has also driven the need for transit agencies to explore new ways to improve service reliability, predictability, quality and the passenger experience. One of the biggest deterrents to public transit use is uncertainty — commuters value predictability and efficiency. AI is also improving transit apps by enabling accurate, real-time updates based on scheduling delays, crowd data, and other factors, helping to mitigate rider concerns by ensuring they have access to reliable, up-to-the-minute data.

Public transit systems have historically prioritized safety and reliability above all else. Unlike many other industries, where innovation and agility drive rapid change, transit agencies operate in safety-critical environments where technology failures could jeopardize passenger and employee well-being.

Perhaps most compelling is that AI technology is now embedded in the very video surveillance devices transit agencies already rely on. This makes implementation far simpler than before, allowing agencies to transition smoothly into a data-driven operational model without requiring an overhaul of their existing infrastructure. With AI-enhanced security devices in place, transit agencies can begin shifting their focus beyond safety to explore new efficiencies and improvements.

Unlocking the Power of AI

While safety and security were initial driving forces behind surveillance investments, AI’s capabilities extend far beyond security and crime prevention. These same AI-powered computer vision sensors (a new term for IP Security Cameras) are now improving operational performance and enhancing the passenger experience. AI-enabled cameras and sensors can monitor passenger flow at key transit points, identifying congestion in real-time and adjusting scheduling or dispatching additional resources accordingly. Additionally, AI can predict when maintenance is needed on vehicles and infrastructure, reducing the likelihood of service disruptions.

AI analytics also enable real-time communication between transit agencies and passengers. By delivering insights on parking availability, crowding, transit delays, and anomalies in the operations and maintenance infrastructure, these systems improve reliability, reduce uncertainty for riders and help create a level of predictability for the transit system authorities. Mobile apps, digital signage, and station announcements can relay this critical information, ensuring a more seamless transit experience — to support both the traveling public and those who are responsible for providing these safety-critical systems to the traveler.

Beyond communication, AI is also optimizing scheduling and resource allocation. By analyzing demand patterns, transit agencies can adjust schedules to improve efficiency and minimize operational costs. Additionally, AI enhances route planning by identifying bottlenecks and suggesting alternative paths, ensuring services are deployed where they are most needed.

AI is also supporting overall operational efficiency by helping agencies make smarter decisions about fleet management, resource distribution, and traffic conditions. Integrating AI into transit planning enables the highest safety standards while maximizing efficiency and responsiveness.

AI in Action: A Bus System Case Study

Buses, one of the most widely used forms of public transit, provide a clear example of how AI can transform operations. Public transit buses are already equipped with multiple security devices, including onboard cameras, dashcams, and traffic surveillance systems. These cameras collect vast amounts of data, but their utility has historically been limited to safety monitoring. With AI integration, however, this same data can be leveraged for operational improvements.

By analyzing crowding levels, AI can provide real-time updates to commuters about seat availability, allowing riders to plan accordingly. Traffic conditions can also be assessed, with AI processing data to suggest route adjustments in real-time, helping to minimize delays and improve on-time performance. Additionally, cameras at bus stops can monitor waiting passengers and relay data to transit operators, ensuring that additional buses are deployed when necessary to prevent overcrowding. By analyzing and disseminating this information to both passengers and transit operators, AI enhances the overall efficiency of bus systems, leading to smoother and more predictable rides while maintaining the safety that passengers rely on.

The Future of AI in Public Transit

The integration of AI into public transit systems represents a major step forward in creating smarter, more responsive transportation networks. While safety will always be the top priority, AI’s ability to enhance operations, optimize resource allocation, and improve passenger experiences makes it an invaluable tool for the future. With AI-driven insights, transit agencies can address long-standing challenges such as congestion, unpredictable delays, and inefficient scheduling. By leveraging the data already being collected through existing security devices, agencies can unlock new efficiencies while maintaining the highest standards of safety.

For passengers, AI-enabled transit systems mean greater reliability, reduced wait times, and real-time information at their fingertips — key factors in making public transit a more attractive option to skeptical riders. As AI technology continues to advance, transit agencies that embrace these innovations will be well-positioned to meet the growing demands of modern urban mobility. The opportunity is here, and the future of public transit is smarter, safer, and more efficient than ever before.

KEYWORDS: analytics artificial intelligence (AI) passenger security public transportation security

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Anthonyincorvati

Anthony Incorvati is the Segment Development Manager, Transportation at Axis Communications. Image courtesy of Incorvati

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