Multimodel Ensemble for Freeway Traffic State Estimations

Abstract

Freeway traffic state estimation is a vital component of traffic management and information systems. Macroscopic-model-based traffic state estimation methods are widely used in this field and have gained significant achievements. However, tests show that the inherent randomness of traffic flow and uncertainties in the initial conditions of models, model parameters, and model structures all influence traffic state estimations. To improve the estimation accuracy, this paper presents an ensemble learning framework to appropriately combine estimation results from multiple macroscopic traffic flow models. This framework first assumes that any models existing are imperfect and have their own strengths/weaknesses. It then estimates the online traffic states in a rolling horizon scheme. This framework automatically ensembles the information from each individual estimation model based on their performance during the selected regression horizon. In particular, we discuss three weighting algorithms, namely, least square regression, ridge regression, and lasso, which represent different presumptions of model capabilities. A field test based on real freeway measurements indicates that lasso ensemble best handles various uncertainties and improves estimation accuracy significantly. It should be also pointed out that the proposed framework is a flexible tool to assemble nonmodel-based traffic estimation algorithms. This framework can be also extended for many other applications, including traffic flow prediction and travel-time prediction.