Online Traffic Congestion Prediction Based on Random Forest

DOI

10.2991/icmmcce-15.2015.518How to use a DOI?

Keywords

Intelligent Transportation System, Traffic Congestion, Online Prediction, Random Forest.

Abstract

In recent years, distinction and prediction of urban traffic congestion has become an important part of Intelligent Transportation System (ITS), hence attracting more and more attentions. Road congestion can be predicted by analyzing traffic flow data collected by various data acquisition equipment primarily. However, existing methods not only need to store large amount of historical information, but has not enough suitability for large-scaled and changing traffic flows. Therefore, an online prediction method based on Random Forest (RF) is put forward in this paper and the prediction on congestions is made by real-time data instead of digging the historical data. Simulation and experiment results show that the design presented in this paper improves accuracy of predictions and it has a certain use value.

Copyright

© 2015, 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  - Xiao Han
AU  - Yijie Shi
PY  - 2015/12
DA  - 2015/12
TI  - Online Traffic Congestion Prediction Based on Random Forest
BT  - Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmmcce-15.2015.518
DO  - 10.2991/icmmcce-15.2015.518
ID  - Han2015/12
ER  -