Traffic Flow Scaling Using Sensors and Floating Car Data: Bologna Case Study

This paper proposes a methodology for scaling Floating Car Data (FCD) to represent urban traffic flows using a combination of data from fixed sensors and Floating Car Data (FCD), and the methodology is successfully applied to the road network of the city of Bologna. In particular, it focuses on 11 urban roads in the ring road around the city center that are continuously observed by sensors. Machine learning methods, derived from decision tree-based models, are used with the goal of predicting urban traffic flows given the variables related to FCD traffic flows. A comparison of various tree-based machine learning methods is made among CatBoost, LightGBM, XGBoost, AdaBoost Regressor, and Random Forest Regressor. In particular, the approach used to test these machine learning architectures is a simulation analysis regarding the spatial generalization capabilities of those methods via a leave-one-route-out cross-validation approach. Results show that boosting methods, especially those from XGBoost or LightGBM, perform best, achieving the lowest percentage of errors for the specific set of homogeneous routes evaluated More importantly, the results confirm the feasibility of using the proposed approach for the traffic flow scaling in the segments that are not traditionally monitored by physical sensors and hence contribute to the increase of the spatial area that can be managed by traffic management organizations without enlarging the sensor network which can lead to overall lower traffic monitoring and forecasting costs.