Vehicle speed trajectory estimation using road traffic and infrastructure information

This paper describes a novel method to estimate vehicle speed trajectory at a road-link scale. Road network infrastructure and topology have a significant impact on the vehicle's driver behavior. We propose a dynamic speed estimation based on road macroscopic features, available anywhere through digital maps webservices. The method combines machine learning techniques and stochastic approaches to construct dynamic speed trajectories. This allows the estimation of several speed trajectories per road-link to take into account various possible driving behaviors. The model has been trained and validated on a database of 50 million kilometers of 1 Hz driving recordings coming from a crowdsensing project. Estimated speed trajectories are compared to these driving recordings and promising results are obtained in terms of reproducing real driving behavior. A map of traffic pollutant emission is presented as a first application.