A Mesoscopic Traffic Data Assimilation Framework for Vehicle Density Estimation on Urban Traffic Networks Based on Particle Filters

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.

Particle filter–based data assimilation in dynamic data-driven simulation: sensitivity analysis of three critical experimental conditions

“…Similar findings are reported in a few other studies. 24,28,35 In this paper, we aim to further study the experimental conditions of PF applied to DES. The focus is on three common and critical conditions: the time interval, the number of particles, and the measurement errors.…”

“…For example, people's location estimation in smart buildings, 23,24 household energy consumption behavior, 25,26 vehicle trajectory reconstruction, 27 and traffic density estimation. 28 Those simulation models often provide detailed information about system states. But they are not typically data driven in the sense that the models are generally developed and calibrated (by human modelers) using historical data before simulation.…”

“…For example, people’s location estimation in smart buildings, 23,24 household energy consumption behavior, 25,26 vehicle trajectory reconstruction, 27 and traffic density estimation. 28…”