Road traffic flow forecasting provides critical information for the operational management of road mobility challenges, and models are used to generate the forecast. This paper uses a random process to present a novel...Road traffic flow forecasting provides critical information for the operational management of road mobility challenges, and models are used to generate the forecast. This paper uses a random process to present a novel traffic modelling framework for aggregate traffic on urban roads. The main idea is that road traffic flow is random, even for the recurrent flow, such as rush hour traffic, which is predisposed to congestion. Therefore, the structure of the aggregate traffic flow model for urban roads should correlate well with the essential variables of the observed random dynamics of the traffic flow phenomena. The novelty of this paper is the developed framework, based on the Poisson process, the kinematics of urban road traffic flow, and the intermediate modelling approach, which were combined to formulate the model. Empirical data from an urban road in Ghana was used to explore the model’s fidelity. The results show that the distribution from the model correlates well with that of the empirical traffic, providing a strong validation of the new framework and instilling confidence in its potential for significantly improved forecasts and, hence, a more hopeful outlook for real-world traffic management.展开更多
Numerical modelling of coastal morphology is a complex and sometimes unrewarding exercise and often not yielding tangible results. Typically, the underlying drivers of morphology are not properly accounted for in nume...Numerical modelling of coastal morphology is a complex and sometimes unrewarding exercise and often not yielding tangible results. Typically, the underlying drivers of morphology are not properly accounted for in numerical models. Such inaccuracies combined with a paucity of validation data create a difficulty for coastal planners/engineers who are required to interpret such morphological models to develop coastal management strategies. This study develops an approach to long term morphological modelling of a barrier beach system that includes the findings of over 10 years of coastal monitoring on a dynamic coastal system. The novel approach to predicting the long term evolution of the area combines a mix of short term hydrodynamic monitoring and long term morphological modelling to predict future changes in a breached barrier system. A coupled wave, wind, hydrodynamic and sediment transport numerical model was used to predict the coastal evolution in the dynamic barrier beach system of Inner Dingle Bay, Co. Kerry, Ireland. The modelling approach utilizes the schematisation of inputs to reflect observed trends. The approach is subject to two stages of validation both quantitative and qualitative. The study highlights the importance of considering all the parameters responsible for driving coastal evolution and the necessity to have long term monitoring results for trend based validation.展开更多
In the study of complex networks （systems）, the scaling phenomenon of flow fluctuations refers to a certain powerlaw between the mean flux （activity） （Fi） of the i-th node and its variance σi as σi α （Fi）α...In the study of complex networks （systems）, the scaling phenomenon of flow fluctuations refers to a certain powerlaw between the mean flux （activity） （Fi） of the i-th node and its variance σi as σi α （Fi）α Such scaling laws are found to be prevalent both in natural and man-made network systems, but the understanding of their origins still remains limited. This paper proposes a non-stationary Poisson process model to give an analytical explanation of the non-universal scaling phenomenon： the exponent α varies between 1/2 and 1 depending on the size of sampling time window and the relative strength of the external/internal driven forces of the systems. The crossover behaviour and the relation of fluctuation scaling with pseudo long range dependence are also accounted for by the model. Numerical experiments show that the proposed model can recover the multi-scaiing phenomenon.展开更多
In this paper we investigate the dynamics of an asymmetric exclusion process on a one-dimensional lattice with long- range hopping and random update via Monte Carlo simulations theoretically. Particles in the model wi...In this paper we investigate the dynamics of an asymmetric exclusion process on a one-dimensional lattice with long- range hopping and random update via Monte Carlo simulations theoretically. Particles in the model will firstly try to hop over successive unoccupied sites with a probability q, which is different from previous exclusion process models. The probability q may represent the random access of particles. Numerical simulations for stationary particle currents, density profiles, and phase diagrams are obtained. There are three possible stationary phases： the low density （LD） phase, high density （HD） phase, and maximal current （MC） in the system, respectively. Interestingly, bulk density in the LD phase tends to zero, while the MC phase is governed by α,β, and q. The HD phase is nearly the same as the normal TASEP, determined by exit rate β. Theoretical analysis is in good agreement with simulation results. The proposed model may provide a better understanding of random interaction dynamics in complex systems.展开更多
实时动态差分(real time kinematic,RTK)定位技术因其成本低、实时性强等优点,已成为实时位移监测领域的重要技术手段.然而,随着跨海大桥、海上平台等远距离基础设施对高精度实时位移监测需求不断增长,常规RTK技术在长距离作业中,因测...实时动态差分(real time kinematic,RTK)定位技术因其成本低、实时性强等优点,已成为实时位移监测领域的重要技术手段.然而,随着跨海大桥、海上平台等远距离基础设施对高精度实时位移监测需求不断增长,常规RTK技术在长距离作业中,因测站间距离增加导致大气误差(对流层和电离层延迟)的空间相关性降低,差分后残余大气误差难以充分消除,严重影响模糊度的收敛从而影响定位精度.针对这一问题,提出一种大气误差附加约束的长距离RTK定位方法:1)将经先验模型改正并进行差分后残余的对流层和电离层延迟参数化并纳入估计模型,针对残余误差分别建立先验约束:对流层残差基于台站间高差和测站距离构建先验方差,更全面地刻画长距离条件下对流层残差的不确定性;电离层残差结合纬度相关性构建先验方差,实现对定位参数解算过程的稳健约束;2)考虑大气误差的时变特性,采用随机游走过程对对流层和电离层参数进行动态估计,电离层活动变化大,随机游走噪声建模考虑基线长度和卫星高度角变化,使动态估计更符合实际情况.基于国际GNSS服务组织(International GNSS Service,IGS)测站和海上平台实测数据开展试验,结果表明:相较于常规RTK方法,所提方法在不同的观测环境下均有效缩短了收敛时间和模糊度首次固定时间,显著提升了模糊度固定率,同时在水平和垂向定位精度上取得明显改善.展开更多
Let {Xt,t ≥ 1} be a moving average process defined by Xt = ∑^∞ k=0 αkξt-k, where {αk,k ≥ 0} is a sequence of real numbers and {ξt,-∞ 〈 t 〈 ∞} is a doubly infinite sequence of strictly stationary dependen...Let {Xt,t ≥ 1} be a moving average process defined by Xt = ∑^∞ k=0 αkξt-k, where {αk,k ≥ 0} is a sequence of real numbers and {ξt,-∞ 〈 t 〈 ∞} is a doubly infinite sequence of strictly stationary dependent random variables. Under the conditions of {αk, k ≥ 0} which entail that {Xt, t ≥ 1} is either a long memory process or a linear process, the strong approximation of {Xt, t ≥ 1} to a Gaussian process is studied. Finally, the results are applied to obtain the strong approximation of a long memory process to a fractional Brownian motion and the laws of the iterated logarithm for moving average processes.展开更多
文摘Road traffic flow forecasting provides critical information for the operational management of road mobility challenges, and models are used to generate the forecast. This paper uses a random process to present a novel traffic modelling framework for aggregate traffic on urban roads. The main idea is that road traffic flow is random, even for the recurrent flow, such as rush hour traffic, which is predisposed to congestion. Therefore, the structure of the aggregate traffic flow model for urban roads should correlate well with the essential variables of the observed random dynamics of the traffic flow phenomena. The novelty of this paper is the developed framework, based on the Poisson process, the kinematics of urban road traffic flow, and the intermediate modelling approach, which were combined to formulate the model. Empirical data from an urban road in Ghana was used to explore the model’s fidelity. The results show that the distribution from the model correlates well with that of the empirical traffic, providing a strong validation of the new framework and instilling confidence in its potential for significantly improved forecasts and, hence, a more hopeful outlook for real-world traffic management.
文摘Numerical modelling of coastal morphology is a complex and sometimes unrewarding exercise and often not yielding tangible results. Typically, the underlying drivers of morphology are not properly accounted for in numerical models. Such inaccuracies combined with a paucity of validation data create a difficulty for coastal planners/engineers who are required to interpret such morphological models to develop coastal management strategies. This study develops an approach to long term morphological modelling of a barrier beach system that includes the findings of over 10 years of coastal monitoring on a dynamic coastal system. The novel approach to predicting the long term evolution of the area combines a mix of short term hydrodynamic monitoring and long term morphological modelling to predict future changes in a breached barrier system. A coupled wave, wind, hydrodynamic and sediment transport numerical model was used to predict the coastal evolution in the dynamic barrier beach system of Inner Dingle Bay, Co. Kerry, Ireland. The modelling approach utilizes the schematisation of inputs to reflect observed trends. The approach is subject to two stages of validation both quantitative and qualitative. The study highlights the importance of considering all the parameters responsible for driving coastal evolution and the necessity to have long term monitoring results for trend based validation.
基金Project supported in part by National Basic Research Program of China (973 Project) (Grant No 2006CB705506)Hi-Tech Research and Development Program of China (863 Project) (Grant No 2007AA11Z222)National Natural Science Foundation of China (Grant Nos 60721003 and 60774034)
文摘In the study of complex networks （systems）, the scaling phenomenon of flow fluctuations refers to a certain powerlaw between the mean flux （activity） （Fi） of the i-th node and its variance σi as σi α （Fi）α Such scaling laws are found to be prevalent both in natural and man-made network systems, but the understanding of their origins still remains limited. This paper proposes a non-stationary Poisson process model to give an analytical explanation of the non-universal scaling phenomenon： the exponent α varies between 1/2 and 1 depending on the size of sampling time window and the relative strength of the external/internal driven forces of the systems. The crossover behaviour and the relation of fluctuation scaling with pseudo long range dependence are also accounted for by the model. Numerical experiments show that the proposed model can recover the multi-scaiing phenomenon.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.41274109 and 11104022)the Fund for Sichuan Youth Science and Technology Innovation Research Team(Grant No.2011JTD0013)the Creative Team Program of Chengdu University of Technology
文摘In this paper we investigate the dynamics of an asymmetric exclusion process on a one-dimensional lattice with long- range hopping and random update via Monte Carlo simulations theoretically. Particles in the model will firstly try to hop over successive unoccupied sites with a probability q, which is different from previous exclusion process models. The probability q may represent the random access of particles. Numerical simulations for stationary particle currents, density profiles, and phase diagrams are obtained. There are three possible stationary phases： the low density （LD） phase, high density （HD） phase, and maximal current （MC） in the system, respectively. Interestingly, bulk density in the LD phase tends to zero, while the MC phase is governed by α,β, and q. The HD phase is nearly the same as the normal TASEP, determined by exit rate β. Theoretical analysis is in good agreement with simulation results. The proposed model may provide a better understanding of random interaction dynamics in complex systems.
文摘实时动态差分(real time kinematic,RTK)定位技术因其成本低、实时性强等优点,已成为实时位移监测领域的重要技术手段.然而,随着跨海大桥、海上平台等远距离基础设施对高精度实时位移监测需求不断增长,常规RTK技术在长距离作业中,因测站间距离增加导致大气误差(对流层和电离层延迟)的空间相关性降低,差分后残余大气误差难以充分消除,严重影响模糊度的收敛从而影响定位精度.针对这一问题,提出一种大气误差附加约束的长距离RTK定位方法:1)将经先验模型改正并进行差分后残余的对流层和电离层延迟参数化并纳入估计模型,针对残余误差分别建立先验约束:对流层残差基于台站间高差和测站距离构建先验方差,更全面地刻画长距离条件下对流层残差的不确定性;电离层残差结合纬度相关性构建先验方差,实现对定位参数解算过程的稳健约束;2)考虑大气误差的时变特性,采用随机游走过程对对流层和电离层参数进行动态估计,电离层活动变化大,随机游走噪声建模考虑基线长度和卫星高度角变化,使动态估计更符合实际情况.基于国际GNSS服务组织(International GNSS Service,IGS)测站和海上平台实测数据开展试验,结果表明:相较于常规RTK方法,所提方法在不同的观测环境下均有效缩短了收敛时间和模糊度首次固定时间,显著提升了模糊度固定率,同时在水平和垂向定位精度上取得明显改善.
文摘Let {Xt,t ≥ 1} be a moving average process defined by Xt = ∑^∞ k=0 αkξt-k, where {αk,k ≥ 0} is a sequence of real numbers and {ξt,-∞ 〈 t 〈 ∞} is a doubly infinite sequence of strictly stationary dependent random variables. Under the conditions of {αk, k ≥ 0} which entail that {Xt, t ≥ 1} is either a long memory process or a linear process, the strong approximation of {Xt, t ≥ 1} to a Gaussian process is studied. Finally, the results are applied to obtain the strong approximation of a long memory process to a fractional Brownian motion and the laws of the iterated logarithm for moving average processes.
