Relationship between Short Term Traffic Flow Chaos Fractal Properties and the Necessary Data for Prediction

DOI

10.2991/iccsee.2013.560How to use a DOI?

Keywords

component, recursive map, correlation dimension, traffic flow

Abstract

Analyze the traffic flow in multi-scale time window of a freeway by using the nonlinear analysis method such as Correlation dimension , recursive map and so on, we find that chaos and fractal still exist in wide observation scales. Traffic flow correlation dimension reduces when the length of time window increases, in the observation scale of minutes. However, traffic flow correlation dimension reduces when the length of time window reduces, in the observation scale of seconds, instead of fractal property disappearance as predicted before. We present that, from the view of prediction, the recording point which is 10 times of the correlation dimension is an essential length of the data to predict. The simple model we present, which includes speed difference between vehicles and observation scales of traffic flow, can explain some of the reasons of the traffic flow chaos.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Gang Yin
AU  - Yu Feng Chen
PY  - 2013/03
DA  - 2013/03
TI  - Relationship between Short Term Traffic Flow Chaos Fractal Properties and the Necessary Data for Prediction
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 2233
EP  - 2237
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.560
DO  - 10.2991/iccsee.2013.560
ID  - Yin2013/03
ER  -