Relationship between Short Term Traffic Flow Chaos Fractal Properties and the Necessary Data for Prediction
- https://doi.org/10.2991/iccsee.2013.560How to use a DOI?
- component, recursive map, correlation dimension, traffic flow
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.
- © 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/).
Cite this article
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 - https://doi.org/10.2991/iccsee.2013.560 ID - Yin2013/03 ER -