On Identifying Spatial Traffic Patterns using Advanced Pattern Matching Techniques

Rajesh Krishnan, Victoria Jane Hodge, Jim Austin, John Polak, Tzu-chang Lee

Research output: Contribution to conferencePaperpeer-review


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
Original languageEnglish
Publication statusPublished - 10 Jan 2010
EventProceedings of Transportation Research Board (TRB) 89th Annual Meeting - Washington, D.C., United States
Duration: 10 Jan 201014 Jan 2010


ConferenceProceedings of Transportation Research Board (TRB) 89th Annual Meeting
Country/TerritoryUnited States
CityWashington, D.C.

Cite this