融合VGG与FCN的智能出租车订单预测模型  

Intelligent Taxi Order Forecasting Model Fusing VGG with FCN

作　　者：李浩[1,2] 霍雯 裴春营 袁瑶瑶 康雁 LI Hao;HUO Wen;PEI Chunying;YUAN Yaoyao;KANG Yan(School of Software,Yunnan University,Kunming 650091,China;Key Laboratory of Software Engineering of Yunnan Province,Kunming 650091,China;College of Geomatics,Xi’an University of Science and Technology,Xi’an 710054,China)

机构地区：[1]云南大学软件学院,昆明650091 [2]云南省软件工程重点实验室,昆明650091 [3]西安科技大学测绘科学与技术学院,西安710054

出　　处：《计算机工程》2020年第12期276-282,共7页Computer Engineering

基　　金：国家自然科学基金(61762092)。

摘　　要：为提高出租车市场管理和运营效率以及实现出租车效益最大化,在地图栅格化的基础上,提出一种融合VGG网络与全卷积网络(FCN)的出租车多区域订单预测模型。将出租车轨迹数据转换为订单图像,去除VGG网络全连接层仅保留主要结构以减少模型参数,利用该网络中深度卷积提取不同空间区域出租车行驶特征,使用FCN中反卷积层上采样重构下一个时间段出租车订单图像,从而获得不同区域和时间段的出租车订单预测数据,并以订单图像形式呈现在地图上。实验结果表明,与BP、RBF等预测模型相比,该模型预测结果平均准确率更高且均方根误差更低,可快速预测出租车多区域订单分布情况。In order to improve the efficiency of taxi market management and operation,and maximize the taxi benefit,this paper proposes a multi-region taxi order forecasting model based on VGG network and Fully Convolutional Networks(FCN)using map rasterization.The taxi trajectory data is converted into order images,and the full connection layer of VGG network is removed while only the main structure is retained to reduce the number of model parameters.The deep convolution in the network is used to extract taxi driving characteristics in different spatial regions,and the taxi order images of the next time period is reconstructed by sampling on the deconvolution layer.So the forecasting data of the taxi orders of different regions and periods is obtained and presented as order images on the map.Experimental results show that compared with BP,RBF and other forecasting models,the prediction results of the proposed model has a higher average accuracy and a lower Root Mean Square Error(RMSE).It can quickly predict the distribution of taxi orders in different regions.

关 键 词：出租车订单预测 VGG网络 全卷积网络 反卷积层 融合模型 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]