Probabilistic Dependency Networks for Prediction and Diagnostics
This work addresses prediction and diagnostics in transportation systems, but appears incremental as it applies an existing method (probabilistic graphical models) to a specific domain.
The paper tackles the problem of modeling and predicting attributes of events in transportation networks, such as bus arrivals or traffic volume, by proposing a probabilistic graphical model to learn dependencies between events, and demonstrates good prediction accuracy compared to state-of-the-art methods.
Research in transportation frequently involve modelling and predicting attributes of events that occur at regular intervals. The event could be arrival of a bus at a bus stop, the volume of a traffic at a particular point, the demand at a particular bus stop etc. In this work, we propose a specific implementation of probabilistic graphical models to learn the probabilistic dependency between the events that occur in a network. A dependency graph is built from the past observed instances of the event and we use the graph to understand the causal effects of some events on others in the system. The dependency graph is also used to predict the attributes of future events and is shown to have a good prediction accuracy compared to the state of the art.