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.
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NOVEMBER 2023 csengineermag.com
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