基于机器学习的智能出租车预测系统

Intelligent Taxi Forecasting System Based on Machine Learning

为了更合理地调度出租车资源,提出基于机器学习的智能出租车预测系统.首先,对波尔图出租车GPS数据集进行分割处理,并抽取其中的一部分作为研究对象;接着利用回声状态网络算法预测旅行目的地;最后利用随机森林算法在相同情况下预测出租车抵达时间.实验表明本系统能根据当前的波尔图出租车GPS数据集预测出实际出租车某段旅程的目的地和旅程所需要的时间,以达到减少出租车资源浪费的目的.

To bring more reasonable scheduling of taxi resources, this study proposes an intelligent taxi forecasting system based on machine learning. Firstly, the GPS data set of Porto taxi is preprocessed, and a part of the training sets are taken as the research object. Then the echo state network algorithm is used to predict the travel destination of the taxi under the premise of predicting the travel destination. Finally, the taxi arrival time is predicted by using random forest algorithm in the same circumstances. Experiments show that the system can predict the actual taxi destination of the part of the journey and the time required for the journey, thus achieved the purpose of reducing the waste of taxi resources based on the current Porto taxi GPS data set.