Xuan Sy Trinh’s scientific contributions

There has been extensive research in traffic state estimation that accounts for the stochastic nature of traffic flow models. However, these studies often exhibit limitations such as an exclusive focus on motorway traffic and a reliance on a single data source. This paper departs from these methods by introducing a stochastic estimation framework that is specifically designed for urban arterial traffic. The framework has the capability to incorporate multiple data sources, which serves to improve its accuracy and robustness. The framework is composed of three components: (i) a stochastic traffic flow model, (ii) a filtering algorithm, and (iii) an algorithm for incorporating multi-source measurements. In terms of the traffic model, we introduce a new stochastic Switching Mode Model that can be applied to arterial roads that have both signalized and unsignalized intersections. This model does not consider uncertainty in the current mode of operation, which substantially reduces the computational complexity because there is only one mode at each time step. Furthermore, we propose three different filtering algorithms for multi-source traffic estimation, including the incremental stochastic Kalman Filter (SKF), the incremental stochastic Unscented Kalman Filter (SUKF), and the hybrid approach. Since the SKF can only deal with linear functions, non-linear measurement equations need to be linearized using first-order Taylor expansions. The SUKF is based on the Unscented Transform (UT), which enables it to work with a wider range of functions regardless of linearity, non-linearity, or non-differentiability. The hybrid algorithm is a combination of the SKF and the SUKF, in which linear equations are treated similarly to the SKF, and non-linear equations are handled with the UT in the same way as in the SUKF. The performances of these algorithms were similar when applied to the synthetic data of an urban arterial in Christchurch CBD, New Zealand. The hybrid algorithm, however, worked slightly better and was more stable than the other two.