Joint prediction of road-traffic and parking occupancy over a city with representation learning

As journey planning services begins to include real time traffic forecast features in order to compute more accurate routing along the journey, adaptive traffic control systems can also benefit from this prediction so as to minimize traffic congestion. But these two systems dedicated to end user and road traffic management authorities could also benefits from other information, and particularly from parking availability prediction since cruising for parking spot represents a significant part of urban traffic: when looking for a parking, drivers must guess where to go, and if they are wrong, may face long distances to find the next location, resulting in considerable time loss and a worsening of traffic congestion. We focus on the simultaneous prediction of traffic and parking availability. Our approach relay on machine learning techniques and more precisely on representation learning methods: each road and car-park is represented by a vector in a common large dimensional space which captures both structural and dynamical information about the observed phenomenon. Such a model is thus able to jointly capture the spatio-temporal correlations between parking and traffic resulting in a high performance prediction system. The results of our experiments on the Grand Lyon (France) urban area show the effectiveness of our approach compared to state of the art methods.