Machine Learning in Smart Transportation Systems for Mode Detection

The transportation system is the backbone of nation's economy and plays a vital role in the environmental changes in the region. In order to make the transportation system more efficient, the mobility patterns using the data collected from smartphone users can be exploited. For such context-aware systems, transportation mode detection can be of particular interest in different intelligence applications to increase the quality of transportation, traffic safety, and other services. In this paper, various machine learning (ML) techniques are used to classify five different modes of transportation. Specifically, we consider support vector machine (SVM), K-nearest neighbors (KNN), decision tree (DT), bagging, and random forest (RF) algorithms for classification. The performance of all the considered algorithms is compared on the basis of F1 score, accuracy, precision, and recall and tested on freely available TMD dataset.