计算机工程 ›› 2019, Vol. 45 ›› Issue (1): 29-34.doi: 10.19678/j.issn.1000-3428.0049296

• 体系结构与软件技术 • 上一篇    下一篇

基于TFPCM与随机模型的交通滞留量预测

李莎,孙丽珺   

  1. 青岛科技大学 信息科学技术学院,山东 青岛 266061
  • 收稿日期:2017-11-14 出版日期:2019-01-15 发布日期:2019-01-15
  • 作者简介:李莎(1993—),女,硕士研究生,主研方向为智能交通;孙丽珺,副教授、博士
  • 基金项目:

    国家自然科学基金(61273180);山东省自然科学基金(ZR2016FQ10)

Prediction of Traffic Retention Based on TFPCM and Stochastic Model

LI Sha,SUN Lijun   

  1. College of Information Science and Technology,Qingdao University of Science and Technology,Qingdao,Shandong 266061,China
  • Received:2017-11-14 Online:2019-01-15 Published:2019-01-15

摘要:

交通滞留量预测是实现智能交通灯自动配时的前提,准确的交通滞留量预测可以为交通信号的动态调配提供支持,从而缓解城市交通拥堵问题。为此,提出一种交通滞留量预测系统。利用基于时间序列分割与极限学习机结合的交通流量预测算法,设计道路系统的模拟方案,将得到的预测流量进行仿真,构建扩展的二级马尔科夫随机模型,计算交通滞留量的预测值。实验结果表明,与BP神经网络相比,该系统能够准确预测交通滞留量,可为城市交通疏导和控制提供理论依据

关键词: 交通滞留量, 时间序列分割, 极限学习机, 交通流量预测算法, 随机模型

Abstract:

The prediction of traffic retention is the premise of realizing the automatic timing of intelligent traffic lights.Accurate traffic detention prediction can provide support for the dynamic allocation of traffic signals,thus alleviating urban traffic congestion.A traffic detention prediction system is proposed.The Traffic Flow Prediction (TFP) algorithm based on time series segmentation and Extreme Learning Machine (ELM) is used to predict traffic flow,the simulation scheme of road system is designed,and the predicted traffic is simulated to construct extended secondary Markov randomization model,calculating the predicted value of traffic retention.Experimental results show that compared with BP neural network,the system can accurately predict traffic detention,and can provide theoretical basis for urban traffic guidance and control.

Key words: traffic retention, time series segmentation, Extreme Learning Machine(ELM), Traffic Flow Prediction (TFP) algorithm, stochastic model

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