基于BP神经网络出租车资源配置的研究

Research on Taxi Resource Allocation Based on BP Neural Network

针对"互联网+"时代出租车资源配置,综合分析北京市出租车载客数据,分别构建了时空供求匹配度、BP神经网络和虚拟变量回归等模型,运用EXCEL、EVIEWS、MATLAB等软件编程求解,研究得出北京市出租车资源在不同时空的供求匹配度、两家公司补贴方案均能缓解打车难,其中滴滴公司补贴方案效率更高的结论。

In the era of ＂ Internet ＋ ＂ taxi resource allocation,comprehensive analysis of Beijing taxi passenger data,build the space and time supply and demand matching degree,BP neural network and virtual variable regression model,using EVIEWS,MATLAB,EXCEL and other software programming solution,the Beijing city taxi resources in different time and space,the supply and demand matching,the two companies are able to ease taxi subsidy scheme,which is more effective and more efficient.