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How AI & Data Can Help Traffic Lights Adjust To Evacuation Traffic Patterns

By Tim Menard

Florida Gov. Ron DeSantis recently said Florida emergency officials made final preparations ahead of Hurricane Idalia, as cities and counties ordered more than 1.5 million people to leave their homes in preparation for the storm, which rolled onshore in late August as a strong category-3 hurricane.

From hurricanes in Florida and all throughout the southeast, to wildfires in the western part of the country, evacuations are a part of life each year and are becoming more irritating for traffic congestion in a time of great panic. 

Is it inevitable that every evacuation will result in frustrating, patience-eroding, bumper-to-bumper traffic? The answer is both no and yes. No, because traffic engineers and planners have long known how to create evacuation plans that maximize the system’s capacity to transport people to safety. Yes, because it is unlikely that the solutions for effective evacuations would be put into action—that authorities would enforce what they require, and that the general public would follow those instructions—just as we haven’t been able to completely eliminate daily congestion on the nation’s freeway arteries in major metropolitan areas.

Because we know this, it is time to implement and leverage sophisticated AI and data-driven technologies that can assist local and state officials when the need arises to adjust to rapidly changing traffic patterns.

Sure, interstates are a source of great frustration, testing the patience of millions of evacuees. But just getting out of the city can be challenging simply because outdated traffic light technology isn’t designed to reprogram itself in the blink of an eye when inter-city traffic patterns rapidly shift.

Today, there exists the technology to help these traffic lights and intersections do just that.

AI NextGen solutions are providing evacuation traffic relief

New, AI-powered traffic lights are using sophisticated algorithms to analyze real-time data and traffic patterns, unlike traditional traffic light technology that operates on predetermined timers. Today’s AI systems and data studies traffic movements and learns from them to effectively optimize traffic flow, even when traffic patterns shift suddenly.

The AI systems are designed to make an accurate assessment of the traffic situation by combining data from multiple sources, including traffic sensors and cameras. As a result, it orchestrates a number of traffic lights, enabling vehicles, transit buses, and emergency response vehicles to pass through intersections more fluidly even during times of high traffic.

Even better is the fact that the implementation of these systems can be budget friendly since it does not require new traffic intersections or vehicle hardware.

Advanced cloud-based open architecture transit signal priority systems now combine asset management and automation to produce a system capable of providing services to an entire region. Unlike hardware-based systems, these solutions use pre-existing equipment and leverage cloud technology to facilitate operations. This removes the need for vehicle detection hardware at the intersection because vehicle location is known through the CAD/AVL system. It also enables both priority calls from greater distances away from signals and priority calls coordinated among a group of signals. Furthermore, the system provides real-time insights on which buses are currently receiving priority along with daily reports of performance metrics.

How the cloud-based technologies work with city systems

Cloud-based web portals are then leveraged to show the real-time location and activity of emergency vehicles, and area buses, including current assigned route, speed, bearing, next stop, on-time performance, and traffic priority status. In addition to individual bus data, the solution integrates other real-time data for display, including traffic signal phase state for signals within each transit region. There is also an additional portal that reports the daily TSP performance for each bus approach of every pilot intersection.

These advanced cloud-based TSP systems take the global picture of a route into account and use machine learning to predict the optimal time to grant the green light to transit vehicles at just the right time. It minimizes the interference with crisscrossing routes and simultaneously maximizes the probability of a continuous drive. This takes place even as traffic patterns shift in real time.

To enable safe and secure connections with traffic signals, each city receives a single device, a computer, that resides at the “edge” and serves as the critical link between city traffic signals and the AI platform. It is designed to securely manage the information exchange between traffic lights and the system and is the only additional hardware necessary.

With this technology now at our fingertips, cities and municipalities have the technology they need to properly accelerate the buildout of intelligent transit networks to benefit everyone in the region. As more of these solutions are utilized across the country, we can have the trust to move people through cities and communities on time and safely, even when new traffic patterns emerge or face additional stress from events such as dangerous weather-related evacuations.

Tim Menard is the CEO and founder of LYT (urban traffic solutions) and he would like to set up an interview to discuss this scenario and to explain how AI and predictive data are now teaming together to help traffic signals make real-time adjustments to accommodate additional traffic so that congestion is minimized in local regions.