Machine Learning Based Modelling For Scaling Urban Road Floating Car Data To Enable Enhanced Traffic Forecasting

Rapid urbanization and the growth of cities worldwide have led to increasing traffic congestion, deteriorating air quality, and heightened safety concerns for both drivers and pedestrians. At the same time, advances in technology have provided traffic management organizations with more sophisticated tools to plan and manage urban transport networks. However, deploying sensors across every road in a metropolitan area remains currently cost prohibitive. To address this challenge, this paper proposes a two-stage approach to modelling traffic on roads without sensors. In the first phase, detailed in this study, Floating Car Data is used to estimate traffic flows on sensor-less roads. By employing Machine Learning techniques, the Floating Car Data is scaled to approximate actual traffic volumes and flows along these routes. The second phase builds upon these estimates to forecast future traffic conditions.