googleResearch Institute recently published a study utilizingAIThe research results of the model for traffic diversion, which was applied in the T-Mobile Park and Lumen Field areas in Seattle through the open source simulation software SUMO (Simulation of Urban Mobility). The results of the study showed that the AI model reduced congestion time by an average of 7 minutes and successfully improved traffic efficiency at 30%.
It is reported that Google researchers used SUMO software to build a base model of the T-Mobile Park and Lumen Field areas of Seattle, and used the information provided by Google Maps on 'congestion', 'traffic light locations', and 'average road travel speeds' to create a complete heat map. This data provides the AI model with a wealth of information that enables it to better understand and predict traffic conditions.
In August and November 2023, Google researchers, in collaboration with the Seattle Department of Transportation, applied this traffic calming AI model to a number of large-scale events. Together with "Dynamic Message Signs", the model successfully reduced congestion times and increased traffic efficiency.
The results of this research suggest thatAI technologyIt has great potential for application in the field of traffic diversion. By combining AI models and real-time data, we are expected to solve the problem of urban traffic congestion in the future, improve the efficiency of transportation, and provide urban residents with a more convenient and efficient travel experience.
In the future, we expect to see more AI technology applied in the field of transportation, helping to improve urban traffic conditions and provide more convenience for our travel.
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