Anyone who\u2019s attended a large sporting event understands the bittersweet feeling of a come-from-behind win. With thousands of fans engaged to the final whistle, the joy of victory quickly becomes the agony of traffic as throngs of fans emerge from the stadium on a select few paths: to the train station, to the interstate, or to nearby nightlife.<\/p>\n
One county in the southeastern United States is using artificial intelligence, the IoT, and location technology to improve that experience. In cooperation with the stadium owner in a large metropolitan area, the county is testing a new system that enlists an intelligent algorithm to monitor street cameras and adjust traffic lights to regulate pedestrian and vehicle flows.<\/p>\n
The concept sounds simple, but at both ends of the spectrum it\u2019s deceptively complex. Using human monitors to oversee such large crowds can quickly become inefficient and overwhelming, and trusting the task to technology demands sophisticated machine learning algorithms.<\/p>\n
The county and its partners are using trend detection, an advanced form of machine learning, as part of the solution. Unlike the pattern recognition techniques that have earned machine learning its notoriety\u2014including detecting cats and road signs in static images\u2014this technique involves not just snapshots but also images that change over time. In the stadium command center, the machine learning algorithm analyzes real-time video of departing fans and spots areas where crowds are beginning to form. Using location intelligence to understand where traffic lights\u2014connected to the IoT\u2014 can be adjusted to ease that congestion, the program makes the necessary adjustments in specific locations while keeping the whole system safe from slowdowns.<\/p>\n
The project is an innovative private-public partnership between the county and the stadium owners, both of which have a vested interest in moving pedestrian and vehicle traffic smoothly through the city. Each group brings a critical piece of the puzzle to the machine learning solution: the county\u2019s IoT-based camera network combined with the sport team\u2019s command center and connected infrastructure.<\/p>\n
At the heart of this smart-traffic solution is a modern GIS. GIS technology was among the first to incorporate artificial intelligence for predictive capabilities, delivering heat maps to help companies detect areas of improving or declining sales. That predictive prowess is now being combined with new forms of artificial intelligence to enhance companies\u2019 predictive power \u2014 and keep crowds moving.<\/p>\n
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Anyone who\u2019s attended a large sporting event understands the bittersweet feeling of a come-from-behind win. With thousands of fans engaged to the final whistle, the joy of victory quickly becomes the agony of traffic as throngs of fans emerge from the stadium on a select…<\/p>\n","protected":false},"author":10,"featured_media":16533,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"inline_featured_image":false,"footnotes":""},"categories":[89],"tags":[],"class_list":["post-16538","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog-en"],"yoast_head":"\n