How agentic AI and dynamic networks are solving the transit reliability gap

The greatest challenge facing transit agencies within today’s urban mobility ecosystem is no longer a lack of data, but the reliability gap—the space between a published schedule and the chaotic reality of city streets. For decades, the public transit experience has been defined by the ghost vehicle phenomenon, where a bus appears on a digital map only to vanish into the friction of gridlock.  

However, a fundamental shift is occurring where we are moving away from static infrastructure and toward agentic networks where buses and emergency vehicles act as intelligent entities, negotiating with traffic lights in real time to preserve the flow of the city. 

By integrating agentic artificial intelligence (AI) with more dynamic transit signal priority (TSP) and predictive preemption, we are transforming the urban corridor into a schedule-aware ecosystem that prioritizes human time and public safety. 

The evolution of schedule-aware infrastructure 

Traditional TSP was often a binary, blunt instrument. When a bus approached an intersection, it triggered a signal, and the light stayed green. While helpful, this lack of nuance often created as many problems as it solved. It ignored the context of the network, frequently leading to early arrivals that forced buses to wait at stops or worse, creating unnecessary congestion for cross-traffic that actually hindered the transit system at large. 

Under today’s more dynamic model, the traffic brain makes real-time decisions based on the bus’s actual performance. If a vehicle is running ahead of schedule, the system maintains a red light to prevent the bus from arriving at its next stop too early—a frequent pain point for riders who miss their connection by seconds. Conversely, if a bus is lagging, the AI calculates the precise, minimum green extension required to get that vehicle back on track.  

This surgical precision ensures that we maintain the flow of equity. It provides the transit-dependent rider with a reliable commute while minimizing the impact on the broader traffic network, ensuring that priority is earned by need rather than granted by default. 

Predictive preemption and the zero-braking corridor 

The same logic that restores sanity to bus schedules is also modernizing the way first responders move through the city. Traditional emergency preemption relied on local optical sensors, meaning a fire truck or ambulance only gained priority once it was physically at the intersection. This creates a dangerous stop-and-go rhythm, where responders are forced to navigate through clogged intersections where cars and transit vehicles have nowhere to move. Predictive preemption, powered by cloud-to-signal technology, eliminates this friction. By utilizing the GPS trajectory of the emergency vehicle, the system can flush traffic downstream miles in advance. 

This creates what engineers call a zero-braking response corridor. Instead of fighting through traffic, the first responder encounters a series of green lights and empty intersections that were cleared minutes before their arrival. For every minute saved in emergency response, the probability of a positive outcome in a cardiac event or structure fire increases exponentially. By turning the vehicle into an agent that communicates its intent to the cloud, the city’s infrastructure anticipates the emergency rather than reacting to it. 

Digital sirens and the virtual clearance zone 

Even with a green light, the last mile of emergency response remains the most dangerous due to the soundproof cabin effect of modern vehicles. As consumer cars become more insulated and drivers more distracted, traditional auditory sirens are losing their efficacy.  

This has led to a rise in T-bone collisions at intersections, which remains a leading cause of first responder fatalities. The solution lies in the deployment of digital sirens—a vehicle-to-vehicle  and vehicle-to-everything  communication layer that bridges the gap between the siren and the driver's consciousness. 

Closing the Gap: A new era of urban intelligence 

The integration of dynamic TSP, predictive preemption and digital sirens represents a move away from the timetable and toward intent. In an agentic transit network, we no longer hope that a bus is on time; we ensure it is by allowing the vehicle to advocate for itself within the traffic grid. We no longer hope that an ambulance can squeeze through a traffic jam; we ensure the jam never forms by clearing the path through predictive logic. This is the hallmark of a smarter transit system: one that values the minutes of a commuter and the seconds of a first responder with equal precision 

For transit leaders, the path forward is clear. The ghost vehicle is not a failure of the driver or the vehicle, but a failure of the network to prioritize the mission of mass transit. By investing in schedule-aware infrastructure and cloud-to-signal technology, agencies can close the reliability gap for good. We are entering an era where the city's pulse is managed by a traffic brain that understands equity, safety and efficiency as a single, unified goal. The result is a city that moves with purpose, where the timetable is not a wish list, but a guarantee.