Traffic state estimation via a particle filter over a reduced measurement space

Abstract

Abstract-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.

Traffic Estimation for Large Urban Road Network with High Missing Data Ratio

“…This paper presented a traffic estimation for a large road network with different missing data ratios. The computational overhead of the large network was addressed by using a method called reduced measurement space proposed by [26] to select the most influential and information rich segments in the road network. These are subsequently used in the particle filter measurement update step.…”

“…For a large road network with many segments, using all the measurements in the particle filter measurement update step becomes computationally intensive. Column based matrix decomposition approach similar (as earlier stated in Section 1) to the work of [26] is employed to select the most probable segments that would give acceptable accuracy.…”

“…The task of training such large segments would be resource intensive, hence the reduced measurement space, proposed in Ref. [26], was used to select the most influential segments in the road network. The drawback of Ref.…”

“…The drawback of Ref. [26] is that the most influential segments are selected based on only available measurements without considering any missing values. This would mean that if some interconnected segments have missing data, they would not be used leading to information loss.…”

“…This would mean that if some interconnected segments have missing data, they would not be used leading to information loss. To address this, we propose a new approach, that combines the benefits of both approaches ([25,26]) in a single framework. First, the most probable segments are selected using the column based matrix decomposition (CBMD).…”