Traffic prediction approaches face challenges when presented with sparse or missing data. This can be caused by numerous factors such as: i) sensors not being operational; ii) communication issues; iii) cost prohibiting full monitoring of a road network. This present work adds to existing body of knowledge by proposing a particle based framework for dealing with these challenges. An expression of the likelihood function is derived for the case when the missing value is calculated based on Kriging interpolation. With the Kriging interpolation, the missing values of the measurements are predicted, which are subsequently used in the computation of likelihood terms in the particle filter algorithm. The results show 23% to 36.34% improvement in RMSE values for the synthetic data used.

Vehicle logo recognition is an important part of vehicle identification in intelligent transportation systems. State-of-the-art vehicle logo recognition approaches typically consider training models on large datasets. However, there might only be a small training dataset to start with and more images can be obtained during the real-time applications. This paper proposes an online image recognition framework which provides solutions for both small and large datasets. Using this recognition framework, models are built efficiently using a weight updating scheme. Another novelty of this work is that the Cauchy prior logistic regression with conjugate gradient descent is proposed to deal with the multinomial classification tasks. The Cauchy prior results in a quicker convergence speed for the weight updating process which could decrease the computational cost for both online and offline methods. By testing with a publicly available dataset, the Cauchy prior logistic regression deceases the classification time by 59%. An accuracy of up to 98.80% is achieved when the proposed framework is applied.

Traffic control and vehicle route planning require accurate estimates of the traffic state in order to be successfully implemented. This estimation problem can be solved by using particle filters in conjunction with macroscopic traffic models such as the stochastic compositional model. The accuracy of the estimates can be decreased for road segments where there are no measurements available. However, the inclusion of measurements for all segment boundaries carries a computational cost associated with the evaluation of the likelihood function required by the particle filter. To solve this problem, this paper proposes using the column based matrix decomposition method to select the most significant locations in the road network. This results in the particle filter being applied over a reduced measurement space, allowing a trade-off between computational efficiency and estimation accuracy to be achieved. A performance evaluation based on a simulated stretch of road is provided to validate the proposed method. It shows that by selecting half the original number of measurements, the computational time is reduced by approximately 9% without significantly decreasing the estimation accuracy. A more significant improvement in terms of savings in computational complexity can be expected when considering larger urban road networks.

Sparse wideband array design for sensor location optimization is highly nonlinear and it is traditionally solved by genetic algorithms (GAs) or other similar optimization methods. This is an extremely time-consuming process and an optimum solution is not always guaranteed. In this work, this problem is studied from the viewpoint of compressive sensing (CS). Although there have been CS-based methods proposed for the design of sparse narrowband arrays, its extension to the wideband case is not straightforward, as there are multiple coefficients associated with each sensor and they have to be simultaneously minimized in order to discard the corresponding sensor locations. At first, sensor location optimization for both general wideband beamforming and frequency invariant beamforming is considered. Then, sparsity in the tapped delay-line (TDL) coefficients associated with each sensor is considered in order to reduce the implementation complexity of each TDL. Finally, design of robust wideband arrays against norm-bounded steering vector errors is addressed. Design examples are provided to verify the effectiveness of the proposed methods, with comparisons drawn with a GA-based design method.