A Comparison of Machine Learning Models for Speed Estimation
2006 (English)In: IFAC Proceedings Volumes, Delft, The Netherlands, 2006, 55-60 p.Conference paper (Refereed)
Speed-density relationships are a classic way of modeling stationary traffic relationships. Besides offering valuable insight in traffic stream flows, such relationships are widely used in simulation-based Dynamic Traffic Assignment (DTA) systems. In this paper, alternative approaches for modeling traffic dynamics, appropriate for traffic simulation, are proposed. Their basic premise is the wide availability of sensor data. The approaches are based on machine learning methods such as locally weighted regression and support vector regression. Neural networks are also considered, as they are a well-established approach, successful in many applications. While such models may not provide as much insight into traffic flow theory, they allow for easy incorporation of additional information tospeed estimation, and hence, may be more appropriate for use in DTA models, especially simulation based. In particular, in this paper, it is demonstrated (using data from a network in Irvine, CA) that the use of such machine learning methods can improve the accuracy of speed estimation.
Place, publisher, year, edition, pages
Delft, The Netherlands, 2006. 55-60 p.
Machine learning, Neural networks, Non-parametric regression, Road traffic
Transport Systems and Logistics
IdentifiersURN: urn:nbn:se:kth:diva-88167ISBN: 978-390266113-5OAI: oai:DiVA.org:kth-88167DiVA: diva2:502156
(cd-rom) 11th International Federation of Automatic Control (IFAC) Symposium on Control in Transportation Systems
TSC import 1996 2012-02-14. QC 201202282012-02-142012-02-142012-02-28Bibliographically approved