首页|Research on Machine Learning Published by a Researcher at Iowa State University (A Sequence-Based Hybrid Ensemble Approach for Estimating Trail Pavement Roughne ss Using Smartphone and Bicycle Data)
Research on Machine Learning Published by a Researcher at Iowa State University (A Sequence-Based Hybrid Ensemble Approach for Estimating Trail Pavement Roughne ss Using Smartphone and Bicycle Data)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on artificial intelligenc e is the subject of a new report. According to news originating from Ames, Iowa, by NewsRx correspondents, research stated, "Trail pavement roughness significan tly impacts user experience and safety." Funders for this research include Des Moines Area Metropolitan Planning Organiza tion. Our news correspondents obtained a quote from the research from Iowa State Unive rsity: "Measuring roughness over large areas using traditional equipment is chal lenging and expensive. The utilization of smartphones and bicycles offers a more feasible approach to measuring trail roughness, but the current methods to capt ure data using these have accuracy limitations. While machine learning has the p otential to improve accuracy, there have been few applications of real-time roug hness evaluation. This study proposes a hybrid ensemble machine learning model t hat combines sequence-based modeling with support vector regression (SVR) to est imate trail roughness using smartphone sensor data mounted on bicycles. The hybr id model outperformed traditional methods like double integration and whole-body vibration in roughness estimation. For the 0.031 mi (50 m) segments, it reduced RMSE by 54-74% for asphalt concrete (AC) trails and 50-59% for Portland cement concrete (PCC) trails."
Iowa State UniversityAmesIowaUnite d StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine Lea rning
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