Applied Machine Learning for Be-In/Be-Out detection for automated public transportation ticketing based on smartphone sensors

In the past year, we collected travel data on iOS and Android in multiple locations from over 100 participants. By analyzing the preprocessed sensor data, we were able to train multiple classification algorithms and achieved an accuracy of over 99% on Android data and 95% on iOS data. The preliminary results are promising but need further investigation. Among other things, we have applied Shapley values to understand the local feature importance. Next, we will test how the different models perform on the phone. Additionally, we will explore transfer learning to further improve accuracy and model performance. From an academic perspective, Philippe is going to publish a short paper with the title "Unsupervised data mining on spatial-temporal passenger mobility and survey data during Covid-19" in a workshop at IEEE Big Data BDA. Due to personal reasons, Philippe decided to quit the Ph.D and leave fluxLoop and Høyskolen Kristiania.

The PhD project will attempt to solve the foundation for enabling automatic ticketing (Be-In, Be-Out/ BIBO) in the public transportation industry. This will lead to better user experience as the traveller is automatically debited for the actual trip and enables more advanced and reasonable pricing structures. Also, the obtained data will provide the industry players with valuable insights for further optimalisation. Past research shows that BLE is the most promising technology for automated BIBO detection. However, the smartphone provides more sensor data which could be useful to accurately identify if a person enters or leaves public transport such as data obtained from the smartphone’s accelerometer or magnetometer. Here, the project will make use of machine learning to correctly classify users in-vehicle presence. The candidate will together with the project owner fluxLoop and the public transport provider Ruter, attempt to develop a reliable measure to identify if a person enters or leaves the public transport. The large amounts of available data and the existing infrastructure allow to create a solution for automated ticketing to increase user experience, enable fair pricing models, while at the same time understand travel behaviour to optimize public transport and reduce fraud. The project will address the use of Bluetooth Low Energy (BLE) in combination with multiple sensors and applied machine learning, which will allow for automated in-vehicle detection. This can provide an easy-to-use and environmentally friendly solution for automated public transport ticketing. During the project different supervised machine learning algorithm will be tested to identify the transportation mode, based on smartphone sensors. Hereby, the focus is on adding more data, considering data as a sequence, as well as conducting error analysis to get insights why models fail the classification task. Future work will include even additional sensors for even better accuracy.