Predict Scooter's Stopping Event Using Smartphone as the Sensing Device.
Journal
2014 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, and IEEE Cyber, Physical and Social Computing, iThings/GreenCom/CPSCom 2014, Taipei, Taiwan, September 1-3, 2014
Pages
17-23
Date Issued
2014
Author(s)
Abstract
Researches show that most of deadly crashes involve one or more unsafe driving behaviors typically associated with careless driving. Many researchers try to develop intelligent transportation system (ITS) or machine learning model to detect these potential risks, to make alert, and to prevent driver from traffic accident. For example, intentionally or carelessly inappropriate stopping or not stopping a vehicle may cause traffic violation or vehicle accident. However, to the best of our knowledge so far, there exist no research of ITS dedicated to collecting scooter's driving profile and improving driving safety of scooter rider, given the fact of that riding scooter is one of the most important transportation means in Taiwan - every 1.56 persons in Taiwan own a scooter. In this work, taking advantages of machine learning technique, we propose a model to predict whether scooter is going to stop or not, by collecting data of various sensors using smart phone, a popular and relative cheap device, set on the handler of scooter. Experiments shows that by carefully concerning the characteristics and tendencies differ from drivers to drivers, from locations to locations, our model can detect stop event of scooter with at most 90% accuracy, such that it can provide significant information to prevent traffic violation, ex: red-light running, or car accident. ? 2014 IEEE.
SDGs
Other Subjects
Artificial intelligence; Automobile drivers; Automobile safety devices; Classification (of information); Crashworthiness; Forecasting; Intelligent systems; Learning systems; Smartphones; Transportation; Vehicles; Driving behavior; Intelligent transportation systems; Machine learning models; Machine learning techniques; Red-light running; Sensing devices; Traffic violation; Vehicle accidents; Accidents
Type
conference paper