XU Xiujuan1,2, BAI Yulin1, XU Lu1, XU Zhenzhen1,2*, ZHAO Xiaowei1,2
(1 School of Software, Dalian University of Technology, Dalian 116620, Liaoning, China;2 Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province(Dalian University of Technology), Dalian 116620, Liaoning, China)
Abstract:
Aiming at severe weather conditions, traffic flow predition modd based on random forest was proposed.Based on taxi data and weather conditions in New York city in 2016, screening the original GPS data layer by layer, the data that meet the definition of severe weather conditions are screened out. Based on the random forest regression method, the traffic flow prediction model under severe weather is studied, and the performance of the model is improved by adjusting the super parameters of the model. At the same time, the performance of random forest model is compared with that of BP neural network model and decision tree model, and the experimental results of random forest prediction model are better.
KeyWords:
traffic flow prediction; random forest; severe weather; bootstrap aggregation