On Identifying Spatial Traffic Patterns using Advanced Pattern Matching Techniques

TY - CONF

T1 - On Identifying Spatial Traffic Patterns using Advanced Pattern Matching Techniques

AU - Krishnan, Rajesh

AU - Hodge, Victoria Jane

AU - Austin, Jim

AU - Polak, John

AU - Lee, Tzu-chang

PY - 2010/1/10

Y1 - 2010/1/10

N2 - The k-nearest neighbor algorithm (k-NN) has been used in the literature for traffic state estimation and prediction over the last decade or so. A number of such multivariate methods use input data from more than one traffic sensor. While a significant amount of discussion can be found in the literature aiming towards optimising the parameters of the k-NN for better accuracy of such models, limited research is available on configuring the k-NN to differentiate between different spatial patterns in the multivariate models. This paper presents an approach to distinguish spatial patterns from one another reliably in traffic variables observed using a number of point-based sensors in a neighbourhood of road links. The application of the proposed approach is demonstrated using AURA, a fast binary pattern matching tool based on neural networks. Two different spatial patterns of traffic congestion plus non-congested situations are simulated using a PARAMICS micro-simulation model. The AURA software is used to identify similar time periods of congestion using data from a congested time period as input using conventional and proposed distance metrics. It is shown that the proposed distance metrics can identify different spatial congestion patterns better than conventional methods. This method will be useful for traffic estimation and prediction methods that use the k-nearest neighbor algorithm or its variants

AB - The k-nearest neighbor algorithm (k-NN) has been used in the literature for traffic state estimation and prediction over the last decade or so. A number of such multivariate methods use input data from more than one traffic sensor. While a significant amount of discussion can be found in the literature aiming towards optimising the parameters of the k-NN for better accuracy of such models, limited research is available on configuring the k-NN to differentiate between different spatial patterns in the multivariate models. This paper presents an approach to distinguish spatial patterns from one another reliably in traffic variables observed using a number of point-based sensors in a neighbourhood of road links. The application of the proposed approach is demonstrated using AURA, a fast binary pattern matching tool based on neural networks. Two different spatial patterns of traffic congestion plus non-congested situations are simulated using a PARAMICS micro-simulation model. The AURA software is used to identify similar time periods of congestion using data from a congested time period as input using conventional and proposed distance metrics. It is shown that the proposed distance metrics can identify different spatial congestion patterns better than conventional methods. This method will be useful for traffic estimation and prediction methods that use the k-nearest neighbor algorithm or its variants

M3 - Paper

T2 - Proceedings of Transportation Research Board (TRB) 89th Annual Meeting

Y2 - 10 January 2010 through 14 January 2010

ER -