Application of Machine Learning in Smart Mobility

Transportation systems might be heavily affected by factors such as accidents and weather. Specifically, inclement weather conditions may have a drastic impact on travel time and traffic flow. This study has two objectives: first, to investigate a correlation between weather parameters and traffic flow and, second, to improve traffic flow prediction by proposing a novel holistic architecture.

It incorporates deep belief networks for traffic and weather prediction and decision-level data fusion scheme to enhance prediction accuracy using weather conditions.

The experimental results, using traffic and weather data originated from the San Francisco Bay Area of California, corroborate the effectiveness of the proposed approach compared with the state of the art.

N.B. The details of the work can be seen in this paper: Arief Koesdwiady, Ridha Soua, and Fakhry Karray. “Improving Traffic Flow Prediction with Weather Information in Connected Cars: a Deep Learning Approach”. IEEE Transactions on Vehicular Technology 65.12 (2016): 9508-9517. [link].