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State Estimation of Unequipped Vehicles Utilizing Microscopic Traffic Model and Principle of Particle Filter

Yonghua Zhou1, Xun Yang1, Chao Mi1
School of Electronic and Information Engineering, Beijing Jiaotong University. Email: yhzhou@bjtu.edu.cn.

Computer Modeling in Engineering & Sciences 2012, 89(6), 497-512. https://doi.org/10.3970/cmes.2012.089.497

Abstract

The movements of vehicles equipped with various positioning systems such as global and wireless positioning ones have provided beneficial channels to acquire abundant traffic flow information for total road network. However, not all vehicles are mounted with positioning systems and not all equipped positioning facilities are always active. This paper will address how to estimate the number and the states of unequipped vehicles through a series of observations on equipped ones. The proposed estimation process initiates employing the non-analytical microscopic traffic model for particle filter to estimate the number, positions and speeds of unequipped vehicles between an equipped one and another equipped one or a specified point in front. Various kinds of possible particles are utilized for the estimation process. Each kind of particle is composed of a definite number of vehicles (quarks) with possible position and speed distributions. The movements of vehicles in each particle are described through microscopic traffic simulation model. The importance of each particle is iteratively measured by the distinction between the observed states of equipped vehicle and their estimates. The position and speed information of unequipped vehicles can be roughly abstracted through the weighted sum of simulated vehicle movements in the same kind of particles. The subtotal of weights of the same kind of particles stands for the confidence level of corresponding kind of state estimates. Numerical tests have demonstrated the favorable performance of proposed estimation approach. It provides a solution to establish traffic flow database of total road network through limited mobile positioning sensors for the application of traffic planning, route guidance and signal control.

Keywords

state estimation, traffic flow, microscopic model, particle filter.

Cite This Article

Zhou, Y., Yang, X., Mi, C. (2012). State Estimation of Unequipped Vehicles Utilizing Microscopic Traffic Model and Principle of Particle Filter. CMES-Computer Modeling in Engineering & Sciences, 89(6), 497–512.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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