SWMLP: Shared Weight Multilayer Perceptron for Car Trajectory Speed
  Prediction using Road Topographical Features
Sarah Almeida Carneiro Giovanni Chierchia Jean Charléty Aurélie Chataignon Laurent Najman
Although traffic is one of the massively collected data, it is often only
available for specific regions. One concern is that, although there are studies
that give good results for these data, the data from these regions may not be
sufficiently representative to describe all the traffic patterns in the rest of
the world. In quest of addressing this concern, we propose a speed prediction
method that is independent of large historical speed data. To predict a
vehicle's speed, we use the trajectory road topographical features to fit a
Shared Weight Multilayer Perceptron learning model. Our results show
significant improvement, both qualitative and quantitative, over standard
regression analysis. Moreover, the proposed framework sheds new light on the
way to design new approaches for traffic analysis.