恶劣天气情况下基于随机森林算法的交通流量预测

Traffic flow prediction based on random forest in severe weather conditions

针对恶劣天气情况,提出基于随机森林交通流量预测模型,基于2016年纽约市出租车数据以及天气情况,对原始GPS数据进行层层筛选,筛选出符合恶劣天气条件定义的数据,以随机森林回归方法为基础研究恶劣天气下交通流量的预测模型,并通过调整模型的超参数改善了模型的性能;同时将随机森林模型与BP神经网络模型和决策树模型做了性能对比,随机森林预测模型最终取得的实验结果较好。

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.