Transportation Mode Detection by Using Smartphones and Smartwatches with Machine Learning

Abstract

Transportation Mode Detection (TMD) is important in planning new transportation projects as well as improving existing ones. Therefore, this study aims to develop predictive modes of transportation through the use of smartphone data and smartwatches and the use of machine learning techniques. To achieve the objective of this study, a review of the studies related to the use of algorithms to predict transportation modes was prepared. Besides, on the practical side, the Physical Activity for Smart Travel (PASTA) platform has been developed. Two groups of participants were recruited in Michigan and Texas to obtain the required data for the study. Daily activities have been classified into activities related to transportation (trips) and non-related to transportation (work, home, shopping, etc.), then focusing exclusively on transportation activities to determine their modes. In this study, four machine learning methods were used for prediction (Random Forest, Extreme Gradient Boosting, Artificial Neural Network, and Support Vector Machine) and using the data of physical activities as a new feature not used in prior studies. The accuracy of the methods of transportation mode prediction was compared through the training and testing phases. The results of the predictions were compared with the activities verified by the participants. The results showed that the method of Random Forest performed better than other methods. This study provides the appropriate tools for decision-makers to help them understand people’s travel behaviors.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 18 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.