ifac wc 2020 tensor final.pdf (480.97 kB)
Download file

A machine learning approach to traffic flow prediction using CP tensor decomposition

Download (480.97 kB)
conference contribution
posted on 28.02.2022, 09:32 authored by Thomas SteffenThomas Steffen, Gerwald Lichtenberg
This paper deals with the prediction of highway traffic flow based on historic data. The methodology is based on canonical polyadic (CP) tensor decompositions of traffic flow data. This step captures the regular elements of the traffic signal based on daily and weekly rhythms and typical geographical distributions of the traffic, while significantly reducing the amount of data required to describe these. The key factors are then extrapolated into the future, and the traffic data is reconstructed from the decomposition. Applied to traffic flow data from the M62 in the North of England in October 2019, this approach provides a surprisingly accurate prediction based on a very compact model, which is a distinct advantage compared to conventional machine learning approaches. Using 4 factors, the prediction captures 90% of the signal energy, which beats existing rolling average prediction techniques.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Source

21st IFAC World Congress 2020

Version

AM (Accepted Manuscript)

Rights holder

© The Authors

Acceptance date

20/04/2020

Publication date

2020-07-13

Notes

Presented as late breaking results contribution.

Publisher version

Language

en

Location

Berlin, Germany (Virtual)

Event dates

11th July 2020 - 17th July 2020

Depositor

Dr Thomas Steffen. Deposit date: 25 February 2022