UTN-Model-Based Traffic Flow Prediction for Parallel-Transportation Management Systems

Kong; Q.-J.; Xu; Y.; Lin; S.; Wen; D.

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UTN-Model-Based Traffic Flow Prediction for Parallel-Transportation Management Systems

机译：基于UTN模型的并行运输管理系统交通流量预测

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Aiming to comply with the requirement of parallel-transportation management systems (PtMS), this paper presents a short-term traffic flow prediction method for signal-controlled urban traffic networks (UTNs) based on the macroscopic UTN model. In contrast with other time-series-based or spatio-temporal correlation methods, the proposed method focuses more on using the substantial mechanism of traffic transmission in road networks and the topology model of the entire UTN. Furthermore, this approach employs a speed-density model based on the fundamental diagram (FD) to obtain more accurate travel times in links. In the comparison experiment, the microscopic traffic simulation software CORSIM is adopted to simulate the real urban traffic. The experiment results fully verify the outstanding performances of the proposed prediction method.

机译：为了满足并行交通管理系统（PtMS）的要求，本文提出了一种基于宏观UTN模型的信号控制城市交通网络（UTN）的短期交通流量预测方法。与其他基于时间序列或时空相关的方法相比，所提出的方法更多地侧重于利用路网中交通传输的实质机制和整个UTN的拓扑模型。此外，该方法采用基于基本图（FD）的速度密度模型来获得更准确的路段旅行时间。在对比实验中，采用微观交通模拟软件CORSIM来模拟真实的城市交通。实验结果充分验证了该预测方法的出色性能。

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来源
《IEEE Transactions on Intelligent Transportation Systems》 |2013年第3期|1541-1547|共7页
作者
Kong; Q.-J.; Xu; Y.; Lin; S.; Wen; D.;
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作者单位

State Key Laboratory for Management and Control of Complex Systems, Institute of Automation , Chinese Academy of Sciences, Beijing, China|c|;

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正文语种 eng
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关键词
CORSIM; fundamental diagram (FD); parallel-transportation management systems (PtMS); short-term traffic flow prediction; urban traffic network (UTN) model;

机译：CORSIM;基本图（FD）;并行交通管理系统（PtMS）;短期交通流量预测;城市交通网络（UTN）模型;

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机译：基于神经网络的城市交通管理系统道路交通参数预测
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机译：利用MI算法并考虑噪声和数据丢失情况的交通流量预测：在明尼苏达州交通流量预测中的应用
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机译：智能交通系统中基于短期交通流量预测的路径规划模型研究
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机译：基于神经网络的城市交通管理系统道路交通参数预测
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机译：基于个体飞行事件预测不确定性的航路扇区概率交通需求预测新方法。第九届美国/欧洲空中交通管理研究与发展研讨会（aTm2011），德国柏林，2011年6月14日至17日

UTN-Model-Based Traffic Flow Prediction for Parallel-Transportation Management Systems

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