Traffic Flow Multi-model with Machine Learning Method based on Floating Car Data

Abstract

The traffic flow measurement is one of the most important components in the traffic management systems. The existing traditional measurement methods are highly time-consuming and costly to continuously gather the required data, such as loop detectors and video cameras. However the travel duration provided by the emerging Floating Car Data (FCD) on Google Maps offers a novel way to estimate traffic flows. Therefore, this work presents a novel multi-model for urban traffic flows by applying a Gaussian Process Regressor (GPR) tuned using machine learning method based on FCD. The FCD on roads, requested through the Google Maps API, only provides information as congestion and travel duration. Traffic flows is estimated with GPR, including different models built by aggregating together data from days sharing similar configuration. The aggregation is performed manually or using unsupervised classification. At last, a series of experiments are conducted to compare the estimated traffic flow and the real one from actual sensors data. The obtained results show that, the proposed modeling can always reproduce and capture the tendency of real traffic flow. The aggregation permits effectively to increase the performance and to conclude on the capability of the approach to replace traditional loop detectors for the measurement of traffic flows.