Towards an Application for Real-Time Travel Mode Detection in Urban Centers

Context-aware applications in intelligent transportation systems have a growing need for travel mode detection systems. However, few applications allow real-time travel mode detection through the use of smartphones. In this paper, we propose a real-time travel mode detection application based on GPS traces using a data mining technique through which these traces are preprocessed, grouped in motion segments and classified by supervised machine learning algorithms. An application prototype was implemented on the Android platform, used by smartphones, for movement data collection and user travel mode detection using the WEKA API in Java. Finally, to evaluate the performance of the application in a real environment, field tests were carried out with dozens of volunteers in the metropolitan area of Rio de Janeiro. Therefore, 1338 travel mode inferences were obtained by four machine learning techniques and the results were evaluated and compared by the indicators of the confusion matrix. Thus, through the performance evaluation carried out, it was possible to verify that the proposed application is useful for real-time travel mode detection in urban centers.