Interactive Machine Learning for Commuters: Achieving Personalised Travel Planners through Machine Teaching
Smartphone apps are an increasingly important part of public transport and can be seen as part of the travel experience. Personalisation of the app is one aspect of the experience that, for example, can give travellers a possibility to save favourite journeys for easy access. However, such a list of journeys can be extensive and inaccurate if it does not consider the traveller’s context. Making an app context aware can transform 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 investigate this we created an technological probe (an Android app) that implements the machine learning paradigm Machine Teaching. In machine teaching the end user defines what the machine should learn. We used the contextual parameters weekday, time, activity and location as input so we can 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 commuter. In the work we present here, we mainly focus on how to teach the machine learning agent in an easy manner. Our aim is to give the commuter a possibility to initiate a machine teaching session at any time, add teaching data and evaluate the results of the prediction