On-line estimation of the state of traffic based on data sampled by electronic detectors is important for intelligent traffic management and control. Because a nonlinear feature exists in the traffic state, and becaus...On-line estimation of the state of traffic based on data sampled by electronic detectors is important for intelligent traffic management and control. Because a nonlinear feature exists in the traffic state, and because particle filters have good characteristics when it comes to solving the nonlinear problem, a genetic resampling particle filter is proposed to estimate the state of freeway traffic. In this paper, a freeway section of the northern third ring road in the city of Beijing in China is considered as the experimental object. By analysing the traffic-state characteristics of the freeway, the traffic is modeled based on the second-order validated macroscopic traffic flow model. In order to solve the particle degeneration issue in the performance of the particle filter, a genetic mechanism is introduced into the resampling process. The realization of a genetic particle filter for freeway traffic-state estimation is discussed in detail, and the filter estimation performance is validated and evaluated by the achieved experimental data.展开更多
卷积神经网络(CNN)是目前交通状态估计的深度学习算法中提取交通特征的关键模块,其对稀疏数据和多模式交通状态的计算不稳定,制约深度学习的状态估计精度。为进一步提升CNN的交通特征分析精度,本文建立了一种编码-解码的交通自适应卷积...卷积神经网络(CNN)是目前交通状态估计的深度学习算法中提取交通特征的关键模块,其对稀疏数据和多模式交通状态的计算不稳定,制约深度学习的状态估计精度。为进一步提升CNN的交通特征分析精度,本文建立了一种编码-解码的交通自适应卷积网络。首先,本文所提网络构建一种交通特征编码CNN,采用下采样操作聚合邻域交通信息,以从稀疏数据中提取有效的交通特征;其次,构建交通状态自适应重构CNN,利用提取的特征准确重构不同模式的交通状态。为表征多样化交通状态时空结构,该CNN引入先验知识引导卷积核形变。最后,实验采用长春市出租车GPS数据对算法在稀疏数据和不同情景下的交通状态估计性能进行验证。实验结果表明,与LSTM、GAN等先进算法相比,本文提出的改进的CNN算法估计精度提高了6.05 km/h RMSE,同时在不同交通情景下的估计精度仅差3.34%RMSE,能为深度学习估计交通状态提供有力的交通特征分析支撑。展开更多
基金Project supported by the National High Technology Research and Development Program of China (Grant No. 2011AA110303)
文摘On-line estimation of the state of traffic based on data sampled by electronic detectors is important for intelligent traffic management and control. Because a nonlinear feature exists in the traffic state, and because particle filters have good characteristics when it comes to solving the nonlinear problem, a genetic resampling particle filter is proposed to estimate the state of freeway traffic. In this paper, a freeway section of the northern third ring road in the city of Beijing in China is considered as the experimental object. By analysing the traffic-state characteristics of the freeway, the traffic is modeled based on the second-order validated macroscopic traffic flow model. In order to solve the particle degeneration issue in the performance of the particle filter, a genetic mechanism is introduced into the resampling process. The realization of a genetic particle filter for freeway traffic-state estimation is discussed in detail, and the filter estimation performance is validated and evaluated by the achieved experimental data.
文摘卷积神经网络(CNN)是目前交通状态估计的深度学习算法中提取交通特征的关键模块,其对稀疏数据和多模式交通状态的计算不稳定,制约深度学习的状态估计精度。为进一步提升CNN的交通特征分析精度,本文建立了一种编码-解码的交通自适应卷积网络。首先,本文所提网络构建一种交通特征编码CNN,采用下采样操作聚合邻域交通信息,以从稀疏数据中提取有效的交通特征;其次,构建交通状态自适应重构CNN,利用提取的特征准确重构不同模式的交通状态。为表征多样化交通状态时空结构,该CNN引入先验知识引导卷积核形变。最后,实验采用长春市出租车GPS数据对算法在稀疏数据和不同情景下的交通状态估计性能进行验证。实验结果表明,与LSTM、GAN等先进算法相比,本文提出的改进的CNN算法估计精度提高了6.05 km/h RMSE,同时在不同交通情景下的估计精度仅差3.34%RMSE,能为深度学习估计交通状态提供有力的交通特征分析支撑。
