Online Traffic Congestion Prediction Based on Random Forest
Xiao Han, Yijie Shi
Available Online December 2015.
- https://doi.org/10.2991/icmmcce-15.2015.518How to use a DOI?
- Intelligent Transportation System, Traffic Congestion, Online Prediction, Random Forest.
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - https://doi.org/10.2991/icmmcce-15.2015.518 ID - Han2015/12 ER -