首页|University of Texas Arlington Researchers Publish New Study Findings on Machine Learning (Enhancing Urban Parking Efficiency Through Machine Learning Model Inte gration)
University of Texas Arlington Researchers Publish New Study Findings on Machine Learning (Enhancing Urban Parking Efficiency Through Machine Learning Model Inte gration)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news originating from Arlington, Texas, by N ewsRx correspondents, research stated, “An increase in vehicular traffic and a s carcity of parking spaces are creating significant challenges for urban parking management.” The news reporters obtained a quote from the research from University of Texas A rlington: “This study aims to tackle these issues that escalate congestion and p ollution and decrease urban productivity, by utilizing machine learning models t o accurately predict parking space availability and categorize occupancy levels. It employs a dataset from a college campus garage collected from January 2022 t o June 2023 and analyzes the performance of random forest, decision tree, linear regression, and support vector models by comparing them, using multiple evaluat ion metrics. The results revealed that the random forest model was the most reli able, as it demonstrated strong performance in both the regression and classific ation analyses and was adept at estimating the exact number of available parking spaces. A concurrent classification analysis that categorized parking occupancy into different levels proved valuable for enhancing the quality of communicatio n and decision-making. An analysis of the importance of various features clearly highlighted the influence of the day of the week on parking demand and patterns ; the impact of seasonality on the volume of parking usage; and the time of day, which plays a crucial role in determining parking behavior.”
University of Texas ArlingtonArlingtonTexasUnited StatesNorth and Central AmericaCyborgsEmerging Technologie sMachine Learning
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