标题：Short-term Traffic Flow Forecasting Using Transfer Ratio and Road Similarity
作者：Guo, De ;Chen, Meng ;Yu, Xiaohui ;Liu, Yang
作者机构：[Guo, De ;Yu, Xiaohui ;Liu, Yang ] School of Computer Science and Technology, Shandong University, Jinan, Shandong; 250101, China;[Liu, Yang ] Departm 更多
会议名称：2018 International Joint Conference on Neural Networks, IJCNN 2018
会议日期：8 July 2018 through 13 July 2018
来源：Proceedings of the International Joint Conference on Neural Networks
关键词：BP Neural Network; CBOW; Time Series; Traffic Flow Forecasting; Transfer Ratio
摘要：It is essential to make accurate traffic flow forecast or the Intelligent Transportation Systems (ITS). To improve the forecasting accuracy, most existing methods mainly focus on the time-series data. In this paper, we propose a novel model combining transfer ratio and road similarity (CoTRRS) to forecast the short-term traffic flow, which makes full use of the spatial information in the urban road networks. We first utilize the Continuous Bag-of-Words (CBOW) model to extract the road similarity and then combine it with transfer ratio to make accurate forecast using a back propagation neural network. To verify our model, we have performed extensive experiments on a real dataset, and the empirical study reveals that CoTRRS outperforms baselines. © 2018 IEEE.