Abstract
Traffic conditions can be more accurately estimated using data assimilation techniques since these methods incorporate an imperfect traffic simulation model with the (partial) noisy measurement data. In this paper, we propose a data assimilation framework for vehicle density estimation on urban traffic networks. To compromise between computational efficiency and estimation accuracy, a mesoscopic traffic simulation model (we choose the platoon based model) is employed in this framework. Vehicle passages from loop detectors are considered as the measurement data which contain errors, such as missed and false detections. Due to the nonlinear and non-Gaussian nature of the problem, particle filters are adopted to carry out the state estimation, since this method does not have any restrictions on the model dynamics and error assumptions. Simulation experiments are carried out to test the proposed data assimilation framework, and the results show that the proposed framework can provide good vehicle density estimation on relatively large urban traffic networks under moderate sensor quality. The sensitivity analysis proves that the proposed framework is robust to errors both in the model and in the measurements.
Highlights
Traffic state information, such as the density, speed on road segments and the queue size in front of an intersection, is the basis of various road traffic management and control strategies
As shown in the figure, the RMSE errors of the estimation results with data assimilation are smaller than that of the estimation results without data assimilation at all time steps, which indicates that the data assimilation framework has improved the estimation results of the whole traffic network with the help of the sensor data
We presented a data assimilation framework for vehicle density estimation on urban traffic networks
Summary
Traffic state information, such as the density, speed on road segments and the queue size in front of an intersection, is the basis of various road traffic management and control strategies. Since every traffic flow model is a simplification of a real traffic system which is complex and uncertain in nature, errors from the process of modeling are inevitable. They include both the inaccurate modeling, the errors in parametric data as well as the uncertainty in traffic systems [7,8,9,10]. In order to reduce these errors and improve the accuracy of traffic simulation results, data assimilation techniques are employed
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