Mobile apps are an increasingly important part of public transport, and can be seen as part of the journey experience. Personalisation of the app is then one aspect of the experience that, for example, can give travellers a possibility to save favourite journeys for easy access. Such a list of journeys can be extensive and inaccurate if it doesn’t consider the traveller’s context. Making an app context aware and present upcoming journeys transforms the app experience in a personal direction, especially for commuters. By using historical personal contextual data, a travel app can present probable journeys or accurately predict and present an upcoming journey with departure times. The predictions can take place when the app is started or be used to remind a commuter when it is time to leave in order to catch a regularly travelled bus or train.

To address this research opportunity we have created an individually trained Machine Learning (ML) agent that we added to a publicly available commuter app. The added part of the app uses weekday, time, user activity and location to predict a user’s upcoming journey. Predictions are made when the app starts and departure times for the most probable transport are presented to the traveller. In our case a commuter only makes a few journey searches in the app every day which implies that, based on our contextual parameters, it will take at least some weeks to create journey patterns that can give acceptable accuracy for the predictions. In the work we present here, we focus on how to handle this cold start problem e.g. the situation when no or inaccurate historical data is available for the Machine Learning agent to train from. These situations will occur both initially when no data exists and due to concept drift originating from changes in travel patterns. In these situations, no predictions or only inaccurate predictions of upcoming journeys can be made.    

We present experiences and evaluate results gathered when designing the interactions needed for the MT session as well as design decisions for the ML pipeline and the ML agent. The user’s interaction with the ML agent during the teaching session is a crucial factor for the success. During the teaching session, information on what the agent already has learnt has to be presented to the user as well as possibilities to unlearn obsolete commute patterns and to teach new. We present a baseline that shows an idealised situation and the amount of training data that the user needs to add in a MT session to reach acceptable accuracy in predictions. Our main contribution is user evaluated design proposals for the MT session.

Using individually trained ML agents opens up opportunities to protect personal data and this approach can be used to create mobile applications that is independent of local transport providers and thus act on open data on a global scale.