A compact and scalable representation of network traffic dynamics using shapes and its applications

A compact and scalable representation of network traffic dynamics using shapes and its applications

Krishnakumari, P.K. (TU Delft Transport and Planning)
Cats, O. (TU Delft Transport and Planning)
van Lint, J.W.C. (TU Delft Transport and Planning)

2020

The biggest challenge of analysing network traffic dynamics of large-scale networks is its complexity and pattern interpretability. In this work, we present a new computationally efficient method, inspired by human vision, to reduce the dimensions of a large-scale network and describe the traffic conditions with a compact, scalable and interpretable custom feature vector. This is done by extracting pockets of congestion that encompass connected 3D subnetworks as 3D shapes. We then parameterize these 3D shapes as 2D projections and construct parsimonious feature vectors from these projections. There are various applications of these feature vectors such as revealing the day-to-day regularity of the congestion patterns and building a classification model that allows us to predict travel time from any origin to any destination in the network. We demonstrate that our method achieves a 44% accuracy improvement when compared against the consensus method for travel prediction of an urban network of Amsterdam. Our method also outperforms historical average methods, especially for days with severe congestion. Furthermore, we demonstrate the scalability of the approach by applying the method on the entire Dutch highway network and show that the feature vector was able to encapsulate the network dynamics with a 93% prediction accuracy. There are many paths to further refine and improve the method. The compact form of the feature vector allows us to efficiently enrich it with more information such as context, weather and event without increasing the computational complexity.

Nation wide
Network traffic dynamics
Pocket of congestion
Shapes

http://resolver.tudelft.nl/uuid:f2a2dae4-1d2e-4717-96b0-ee63813ee35c

https://doi.org/10.1016/j.trc.2020.102850

0968-090X

Transportation Research. Part C: Emerging Technologies, 121, 1-19

Institutional Repository

journal article

© 2020 P.K. Krishnakumari, O. Cats, J.W.C. van Lint