Predicting Comfort Zone around Commuter for Acceptance of Personal Mobility Devices: Pedestrian
Nanyang Technological University (NTU), Singapore • Conducting experiments currently with the pedestrians of age 21-65, videos are recorded and data are extracted using Kinovea software • Collected data will be processed using MATLAB and the same data will be used as an input file for the prediction models • Prediction is done to find whether the zone is comfortable or not using Machine Learning Algorithms ( Random Forrest, Logistic Regressions and Support Vector Machines) in MATLAB and prediction accuracy will be compared among the 3 algorithms