In this paper,we develop a stochastic LWR model based on the influences of the driver's individual property on his/her perceived density and speed deviation.The numerical results show that the driver's individ...In this paper,we develop a stochastic LWR model based on the influences of the driver's individual property on his/her perceived density and speed deviation.The numerical results show that the driver's individual property has great effects on traffic flow only when the initial density is moderate,i.e.,at this time,oscillating traffic flow will occur and the oscillating phenomena in the traffic system consisting of the conservative and aggressive drivers is more serious than that in the traffic system consisting of the conservative(aggressive) drivers.展开更多
We present a high-resolution relaxation scheme for a multi-class Lighthill-Whitham-Richards (MCLWR) traffic flow model. This scheme is based on high-order reconstruction for spatial discretization and an implicit-expl...We present a high-resolution relaxation scheme for a multi-class Lighthill-Whitham-Richards (MCLWR) traffic flow model. This scheme is based on high-order reconstruction for spatial discretization and an implicit-explicit Runge-Kutta method for time integration. The resulting method retains the simplicity of the relaxation schemes. There is no need to involve Riemann solvers and characteristic decomposition. Even the computation of the eigenvalues is not required. This makes the scheme particularly well suited for the MCLWR model in which the analytical expressions of the eigenvalues are difficult to obtain for more than four classes of road users. The numerical results illustrate the effectiveness of the presented method.展开更多
This paper deals with the effects of traffic bottlenecks using an extended Lighthill-Whitham-Richards （LWR） model. The solution structure is analytically indicated by the study of the Riemann problem characterized b...This paper deals with the effects of traffic bottlenecks using an extended Lighthill-Whitham-Richards （LWR） model. The solution structure is analytically indicated by the study of the Riemann problem characterized by a discontinuous flux. This leads to a typical solution describing a queue upstream of the bottleneck and its width and height, and informs the design of a δ-mapping algorithm. More significantly, it is found that the kinetic model is able to reproduce stop-and-go waves for a triangular fun-damental diagram. Some simulation examples, which are in agreement with the analytical solutions, are given to support these conclusions.展开更多
全氮是土壤肥力的重要指标,对作物产量具有决定性作用,采用土壤可见-近红外(Vis-NIR)光谱预测技术及时获取土壤全氮含量信息具有重要意义。采用来自5省的450个土壤样本来验证局部加权回归方法(LWR)结合Vis-NIR光谱技术预测大面积土壤全...全氮是土壤肥力的重要指标,对作物产量具有决定性作用,采用土壤可见-近红外(Vis-NIR)光谱预测技术及时获取土壤全氮含量信息具有重要意义。采用来自5省的450个土壤样本来验证局部加权回归方法(LWR)结合Vis-NIR光谱技术预测大面积土壤全氮含量的适用性。结果表明,LWR模型的预测效果优于偏最小二乘回归(PLSR)、人工神经网络(ANN)和支持向量机(SVM),选取主成分数为5,相似样本为40时,模型验证的决定系数(RP2)为0.83,均方根误差(RMSEP)为0.25 g kg-1,测定值标准偏差与标准预测误差的比值(RPD)达到2.41。LWR从建模集中选取与验证样本相似的土样作为局部建模样本,降低了差别大的样本对模型的干扰,从而提高了模型的预测能力。因此,LWR建模方法通过大范围、大样本土壤光谱数据进行大尺度区域的全氮等土壤属性预测时能够发挥更好的作用。展开更多
基金Supported by the National Natural Science Foundation of China under Grant Nos. 70971007 and 71271016
文摘In this paper,we develop a stochastic LWR model based on the influences of the driver's individual property on his/her perceived density and speed deviation.The numerical results show that the driver's individual property has great effects on traffic flow only when the initial density is moderate,i.e.,at this time,oscillating traffic flow will occur and the oscillating phenomena in the traffic system consisting of the conservative and aggressive drivers is more serious than that in the traffic system consisting of the conservative(aggressive) drivers.
文摘为解决车辆在信号交叉口频繁停走导致高能耗的问题,同时考虑到自动驾驶车(autonomous vehicle,AV)与人工驾驶车(human-driven vehicle,HDV)混合交通的趋势,采用通过自动驾驶车引领人工驾驶车组成混合车队的方式实现信号交叉口处的生态驾驶。在混合车队建模方面,不仅考虑跟驰行为和能量消耗模型,还通过真实车辆轨迹数据进行理论分析,并采用Lighthill,Whitham and Richards(LWR)模型研究排队消散行为,为后续生态驾驶策略设计提供理论基础;研究了前方不同排队情况下目标速度的计算方法,提出了两阶段速度策略,并针对混合车队在同一绿灯时长内不能通过交叉口的情况设计了车队拆分策略。研究结果表明:在前方无排队的情况下,混合车队总能量消耗较自由驾驶模型减少了33.96%,比传统生态驾驶模型多节约了3.33%;在前方有排队且可消散的情况下,未考虑拆分策略的混合车队总能量消耗较自由驾驶模型减少了23.26%,应用拆分策略后总能量消耗的节约比例提高了4.41%;在存在二次排队的情况下,提出的模型相较自由驾驶模型总能量消耗下降了14.55%。研究结果有助于交通管理者根据不同交通状态为混合车队设计更加精准、灵活的动态策略,以降低单位车辆能耗,为实现双碳目标奠定基础。
基金Project supported by the Aoxiang Project and the Scientific and Technological Innovation Foundation of Northwestern Polytechnical University, China (No 2007KJ01011)
文摘We present a high-resolution relaxation scheme for a multi-class Lighthill-Whitham-Richards (MCLWR) traffic flow model. This scheme is based on high-order reconstruction for spatial discretization and an implicit-explicit Runge-Kutta method for time integration. The resulting method retains the simplicity of the relaxation schemes. There is no need to involve Riemann solvers and characteristic decomposition. Even the computation of the eigenvalues is not required. This makes the scheme particularly well suited for the MCLWR model in which the analytical expressions of the eigenvalues are difficult to obtain for more than four classes of road users. The numerical results illustrate the effectiveness of the presented method.
基金supported by the National Natural Science Foundation of China (Nos. 70629001 and10771134)the National Basic Research Program of China (973 Program) (No. 2006CB705500)+1 种基金the Research Grants Council of the Hong Kong Special Administrative Region of China(No. HKU7183/08E)the Research Committee of The University of Hong Kong (No. 10207394)
文摘This paper deals with the effects of traffic bottlenecks using an extended Lighthill-Whitham-Richards （LWR） model. The solution structure is analytically indicated by the study of the Riemann problem characterized by a discontinuous flux. This leads to a typical solution describing a queue upstream of the bottleneck and its width and height, and informs the design of a δ-mapping algorithm. More significantly, it is found that the kinetic model is able to reproduce stop-and-go waves for a triangular fun-damental diagram. Some simulation examples, which are in agreement with the analytical solutions, are given to support these conclusions.
文摘全氮是土壤肥力的重要指标,对作物产量具有决定性作用,采用土壤可见-近红外(Vis-NIR)光谱预测技术及时获取土壤全氮含量信息具有重要意义。采用来自5省的450个土壤样本来验证局部加权回归方法(LWR)结合Vis-NIR光谱技术预测大面积土壤全氮含量的适用性。结果表明,LWR模型的预测效果优于偏最小二乘回归(PLSR)、人工神经网络(ANN)和支持向量机(SVM),选取主成分数为5,相似样本为40时,模型验证的决定系数(RP2)为0.83,均方根误差(RMSEP)为0.25 g kg-1,测定值标准偏差与标准预测误差的比值(RPD)达到2.41。LWR从建模集中选取与验证样本相似的土样作为局部建模样本,降低了差别大的样本对模型的干扰,从而提高了模型的预测能力。因此,LWR建模方法通过大范围、大样本土壤光谱数据进行大尺度区域的全氮等土壤属性预测时能够发挥更好的作用。
