Short-Term Traffic Speed Prediction for an Urban Corridor
Short-Term Traffic Speed Prediction for an Urban Corridor
复制标题
城市走廊的短期交通速度预测
DOI：
10.1111/mice.12221
复制
发表时间：
2017
影响因子：
9.6
通讯作者：
Yu Bin
中科院分区：
文献类型：
--
作者：
Yao Baozhen;Chen Chao;Cao Qingda;Jin Lu;Zhang Mingheng;Zhu Hanbing;Yu Bin
短期交通速度预测是智能交通系统（ITS）最关键的组成部分之一。准确、实时的交通速度预测可以为出行者的路线选择和交通引导/控制提供支持。本文提出了一种由时空参数组成的支持向量机模型（单步预测模型）。在单步预测模型的基础上，建立了短期交通速度预测模型。为了验证所提出的短期交通速度预测模型的准确性，以中国佛山市出租车的GPS数据为例进行了应用。结果表明，短期交通速度预测误差在3.31% ~ 15.35%之间。与人工神经网络、k近邻模型、基于历史数据的模型和基于移动平均数据的模型相比，具有时空参数的支持向量机模型表现出良好的性能。
Short‐term traffic speed prediction is one of the most critical components of an intelligent transportation system (ITS). The accurate and real‐time prediction of traffic speeds can support travellers’ route choices and traffic guidance/control. In this article, a support vector machine model (single‐step prediction model) composed of spatial and temporal parameters is proposed. Furthermore, a short‐term traffic speed prediction model is developed based on the single‐step prediction model. To test the accuracy of the proposed short‐term traffic speed prediction model, its application is illustrated using GPS data from taxis in Foshan city, China. The results indicate that the error of the short‐term traffic speed prediction varies from 3.31% to 15.35%. The support vector machine model with spatial‐temporal parameters exhibits good performance compared with an artificial neural network, a k‐nearest neighbor model, a historical data‐based model, and a moving average data‐based model.