Industry Insights with Tim Menard

How AI Can Help Traffic Management During Evacuations

AI and Data Can Help Traffic Lights Adjust to Evacuation Traffic Patterns
How AI Can Help Traffic Management During Evacuations
Image: Shutterstock

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

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From hurricanes in Florida and throughout the southeast, to wildfires in the western part of the country, evacuations are necessary to minimize the impact on civilian life. However, in such critical times, traffic congestion often deters a smooth evacuation process.

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.

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.

While interstates add to the woes of millions of evacuees, getting out of the city itself can be challenging because outdated traffic light technology isn't designed to reprogram itself in the blink of an eye when intercity traffic patterns shift rapidly.

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

Next-Gen AI Solutions for 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 time schedules. Today's AI systems and data study traffic movements and analyze them to effectively optimize traffic flow, even when traffic patterns shift suddenly.

These 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 intersection or vehicle hardware.

Advanced cloud-based open architecture transit signal priority systems now available combine asset management and automation to produce a system capable of providing services to an entire region. Unlike hardware-based systems, these solutions use preexisting equipment and leverage cloud technology to facilitate operations. This eliminates 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 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 transit signal priorities, TSP, performance for every 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, every 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 reliable resources to transport 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.



About the Author

Tim Menard

Tim Menard

CEO and Founder

Menard has more than 12 years of experience in the technology domain in automotive sector. Before founding LYT, he was a firmware engineer at Tesla, designing hardware/software simulation systems that autonomously tested the functionality of vehicle electronics used in Tesla’s Model S and Model X. Prior to Tesla, Menard worked on connected vehicle technology at Toyota.




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