Multi-models machine learning methods for traffic flow estimation from Floating Car Data

• Published in: Transportation research. Part C, Emerging technologies Vol. 132; p. 103389
• Main Authors: Li, Jinjian, Boonaert, Jacques, Doniec, Arnaud, Lozenguez, Guillaume
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-11-2021; Elsevier
• Subjects: Artificial Intelligence; Big data; Computer Science; Estimation of traffic flows; Floating Car Data (FCD); Gaussian Process Regression (GPR); Machine learning; Multiagent Systems; Simulation and modeling of transportation systems; Floating Car Data (FCD); Big data; Simulation and modeling of transportation systems; Estimation of traffic flows; Gaussian Process Regression (GPR); Machine learning
• ISSN: 0968-090X; 1879-2359
• DOI: 10.1016/j.trc.2021.103389

Traffic flow measurement is very important for traffic management systems. However, the existing traditional measurement approaches are highly time-consuming and expensive to continuously gather the required data and to maintain the corresponding equipment, such as loop detectors and video cameras. On the other hand, many services on the web propose to estimate automobile travel time taking into account traffic conditions thanks to crowd sourced data (Floating Car Data). This work proposes to reconstruct, from estimated travel time, traffic flows using machine learning method. In particular, we evaluate the capacity of Gaussian Process Regressor (GPR) to address this issue. After obtaining estimated travel time on a given route, a clustering process shows that travel duration profiles in each day can be associated to different “types of day”. Then, different regressors are trained in order to estimate traffic flows from travel duration. In the “multi-model” variant, we trained a Regressor for each type of day. Conversely, in the “single model” variant, only one Regressor is trained (the type of day is not taken into account). This is an innovative work to estimate and reconstruct the traffic flow in transportation networks with machine learning method from aggregated Floating Car Data (FCD). A series of experiments are conducted to compare the estimated traffic flows, obtained by the proposed single model and multi-model, and the real ones from actual sensors. The obtained results show that both single model and multi-models can capture the tendency of real traffic flows. Furthermore, the performance can be improved by regulating parameters in GPR machine learning model, such as half width of sample window and sample size (a whole week or only weekdays), and multi-models can highly increase the performance compared with the single model. Therefore, the proposed GPR machine learning and FCD based new method can replace those traditional loop detectors for the measurement of traffic flow.
•Application of Machine Learning based model to rebuild the traffic flow relationship.•Construction and training of multi-models to reduce estimated traffic flow error.•Demonstration of Gaussian Process Regressor to achieve the lowest error.