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Short-term Traffic Flow Prediction with ACD and Particle Filter

机译：ACD和粒子滤波的短期交通流量预测

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摘要

Urban road traffic is a non-linear, non-stationary, and uncertain process; itsrnuncertainty increases rapidly when making short-term (five minute-long) traffic flowrnprediction. The state-of-the-art in traffic flow prediction is reviewed here, includingrnTime-Series, Artificial Neural Network, Ada-Boost, and Support Vector Machinernbased algorithms. A novel prediction method is proposed for short-term (fivernminute-long) traffic flow prediction. The time interval between vehicles is treated asrna stochastic variable and described with the Marked Point Process in the ARCHrn(autoregressive conditional heteroskedasticity) framework. Different point processesrnmay generate a corresponding ACD (autoregressive conditional duration) model forrnthe prediction of time intervals between vehicles in the traffic’s flow. A particle filterrnis applied with measured vehicle speed data for traffic flow speed and densityrnprediction. We apply this algorithm to the Traffic Guidance System of thernCross-River Tunnel, Lujiazui, Pudong, Shanghai. Nine congestion levels arernproposed and the prediction error for short time (five minute-long) is within threernlevels.
机译：城市道路交通是一个非线性，不稳定和不确定的过程；当进行短期（五分钟长）流量预测时，其不确定性迅速增加。这里回顾了交通流量预测的最新技术，包括时间序列，人工神经网络，Ada-Boost和基于支持向量机的算法。提出了一种新颖的预测方法，用于短期（五分钟长）交通流量预测。车辆之间的时间间隔被视为随机变量，并在ARCHrn（自回归条件异方差）框架中通过标记点过程进行描述。不同的点过程可能会生成相应的ACD（自回归条件持续时间）模型，用于预测交通流中车辆之间的时间间隔。粒子过滤器与测得的车速数据一起应用，以进行交通流速度和密度预测。我们将该算法应用到上海浦东陆家嘴的过江隧道交通诱导系统中。提出了九个拥塞级别，并且短时间（五分钟长）的预测误差在三个级别之内。

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  • 来源
  • 会议地点 Harbin(CN);Harbin(CN)
  • 作者

    G. Y. Zhang; Q. Zhao; Z.W. Luo; H.Wei;

  • 作者单位

    Shanghai Super Star Industrial Development Co., Ltd (Fu Dan UniversityrnPost-Doctoral Station), 11th Floor, Shanghai Science Technology Tower, No.285,rnJian Guo Road West, Shanghai, P.R.C, PH (86-21)-64452140 Shanghai Finance University, Shanghai, 201209;

    email: zhanggy@shfc.edu.cn;

    Shanghai Super Star Industrial Development Co., Ltd (Fu Dan UniversityrnPost-Doctoral Station), 11th Floor, Shanghai Science Technology Tower, No.285,rnJian Guo Road West, Shanghai, P.R.C, PH (86-21)-64452140 email: zhaoqiang1@gmail.com,Shanghai Super Star Industrial Development Co., Ltd (Fu Dan UniversityrnPost-Doctoral Station), 11th Floor, Shanghai Science Technology Tower, No.285,rnJian Guo Road West, Shanghai, P.R.C, PH (86-21)-64452140;

    Fudan University;

  • 会议组织
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 交通工程与交通管理;
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