Research on Combined Dynamic Traffic Assignment and Signal Control

This paper presents a generalized bi-level programming model of combined dynamic traffic assignment and traffic signal control,and especially analyzes a procedure for determining the equilibrium queuing delays on saturated links for dynamic network signal control satisfying the FIFO (first-in-first-out)rule.The chaotic optimal algorithm proposed in this paper can not only present the optimal signal settings,but also calculate,at each interval,the link inflow rates and outflow rates for the dynamic user optimal problem,and provide real-time information for the travelers.Finally,a numerical example is given to illustrate the application of the proposed model and solution algorithm, and comparison shows that this model has better system performance.     

Model predictive control for hybrid vehicle ecological driving using traffic signal and road slope information

This paper presents development of a control system for ecological driving of a hybrid vehicle. Prediction using traffic signal and road slope information is considered to improve the fuel economy. It is assumed that the automobile receives traffic signal information from intelligent transportation systems(ITS). Model predictive control is used to calculate optimal vehicle control inputs using traffic signal and road slope information. The performance of the proposed method was analyzed through computer simulation results. Both the fuel economy and the driving profile are optimized using the proposed approach. It was observed that fuel economy was improved compared with driving of a typical human driving model.     

Traffic chaos and its p rediction based on a nonlinear car-f ollowing model

This paper discusses the dynamic behavior and its predictions for a simulated traffic flow based on the nonlinear response of a vehicle to the leading car’s movement in a single lane. Traffic chaos is a promising field ,and chaos theory has been applied to identify and predict its chaotic movement . A simulated traffic flow is generated using a car-following model (GM model) , and the distance between two cars is investigated for its dynamic properties. A positive Lyapunov exponent confirms the existence of chaotic behavior in the GM model. A new algorithm using a RBF NN (radial basis function neural network) is proposed to predict this traffic chaos. The experiment shows that the chaotic degree and predictable degree are determined by the first Lyapunov exponent . The algorithm proposed in this paper can be generalized to recognize and predict the chaos of short- time traffic flow series.     

Traffm chaos and its prediction based on a nonlinear car-following model

This paper discusses the dynamic behavior and its predictions for a simulated traffic flow based on the nonlinear response of a vehicle to the leading car＇s movement in a single lane. Traffic chaos is a promising field, and chaos theory has been applied to identify and predict its chaotic movement. A simulated traffic flow is generated using a car-following model（ GM model）, and the distance between two cars is investigated for its dynamic properties. A positive Lyapunov exponent confirms the existence of chaotic behavior in the GM model. A new algorithm using a RBF NN （radial basis function neural network） is proposed to predict this traffic chaos. The experiment shows that the chaotic degree and predictable degree are determined by the first Lyapunov exponent. The algorithm proposed in this paper can be generalized to recognize and predict the chaos of short-time traffic flow series     

Integration control for traffic corridors considering guidance information

The problem of designing integration traffic strategies for traffic corridors with the use of ramp metering, speed limit, and route guidance is considered in this paper. As an improvement to the previous work, the presented approach has the following five features： 1） modeling traffic flow to analyze traffic characteristics under the influence of variable speed limit, on-ramp metering and guidance information; 2） building a hierarchy model to realize the integration design of traffic control and route guidance in traffic corridors; 3） devising a multi-class analytical dynamic traffic assignment （DTA） model for traffic corridors, where not only the route choice process will be different for each user-class, but also the traffic flow operations are user-class specific because the travel time characteristic for each user-class is considered; 4） predicting route choice probabilities adaptively with real-time traffic conditions and route choice behaviors corresponding to variant users, rather than assuming as pre-determined; and 5） suggesting a numerical solution algorithm of the hierarchy model presented in this paper based on the modified algorithm of iterative optimization assignment （IOA）. Preliminary numerical test demonstrates the potential of the developed model and algorithm for integration corridor control.     

Energy-efficient receding horizon trajectory planning of high-speed trains using real-time traffic information

Optimal trajectory planning of high-speed trains (HSTs) aims to obtain such speed curves that guarantee safety, punctuality, comfort and energy-saving of the train. In this paper, a new shrinking horizon model predictive control (MPC) algorithm is proposed to plan the optimal trajectories of HSTs using real-time traffic information. The nonlinear longitudinal dynamics of HSTs are used to predict the future behaviors of the train and describe variable slopes and variable speed limitations based on real-time traffic information. Then optimal trajectory planning of HSTs is formulated as the shrinking horizon optimal control problem with the consideration of safety, punctuality, comfort and energy consumption. According to the real-time position and running time of the train, the shrinking horizon is updated to ensure the recursive feasibility of the optimization problem. The optimal speed curve of the train is computed by online solving the optimization problem with the Radau Pseudo-spectral method (RPM). Simulation results demonstrate that the proposed method can satisfy the requirements of energy efficiency and punctuality of the train.     

An adaptive control with optimal disturbances rejection

This paper provides a way to optimize the overall disturbances rejection performance of the adaptive control system in the presence of unknown external disturbances.Especially,the updatable non-empty admissible model set,which is consistent to the a priori knowledge of the plant parameter and the online measurements,is computed.With the overall system performance as the criteria,the nominal model is optimally chosen within the admissible model set.The optimal nominal model is subsequently used to synthesize the optimal closed-loop controller based on the 1 design methodology.Combining the above two aspects,an optimal adaptive control scheme is proposed.Because of the consistency of the identification criteria and control object,the adaptive control scheme proposed in this paper can achieve the overall optimal disturbances rejection performance,and the effect of the interplay between the identification and control of the adaptive system can be handled effectively.In addition,the computable optimal performance is also provided.     

A Framework for Agent-Based Traffic Light Control

As traffic congestion rises within large cities, intelligent traffic control is becoming increasingly important. In this filed, intelligent traffic light control has received much attention. Considering the distributed characteristic of traffic light, multi-agent system can simulate the traffic light control easily. Based on analyzing the traffic light pattern at junctions, an agent-based framework is proposed in this paper, which consists of two kinds of agents： system agent and junction agent. System agent is responsible for gathering the history information of different junction agents to preset the time for each traffic light pattern. While, junction agent controls the traffic light at a road junction by preset-observe-ponder-act cycle. The detailed procedure of pondering is also given. In the authors method, not only the history information but also the dynamic data and communication between agents are considered. People expect the proposed framework can provide a more balanced, coordinated and optimal method for the traffic light control.     

Data-driven cooperative optimal output regulation for linear discrete-time multi-agent systems by online distributed adaptive internal model approach

In this study, a data-driven learning algorithm was developed to estimate the optimal distributed cooperative control policy, which solves the cooperative optimal output regulation problem for linear discretetime multi-agent systems. Notably, the dynamics of all the agent systems and exo-system is completely unknown. By combining adaptive dynamic programming with an internal model, a model-free off-policy learning method is proposed to estimate the optimal control gain and the distributed adapti...     

Finite horizon optimal control of discrete-time nonlinear systems with unfixed initial state using adaptive dynamic programming

In this paper, we aim to solve the finite horizon optimal control problem for a class of discrete-time nonlinear systems with unfixed initial state using adaptive dynamic programming (ADP) approach. A new ε-optimal control algorithm based on the iterative ADP approach is proposed which makes the performance index function converge iteratively to the greatest lower bound of all performance indices within an error according to ε within finite time. The optimal number of control steps can also be obtained by the proposed ε-optimal control algorithm for the situation where the initial state of the system is unfixed. Neural networks are used to approximate the performance index function and compute the optimal control policy, respectively, for facilitating the implementation of the ε-optimal control algorithm. Finally, a simulation example is given to show the results of the proposed method.     

基于Stackelberg博弈的动态用户最优配流和信号控制

研究了动态用户最优配流与信号控制的组合问题.首先基于交通分配将交通流分配到合适的路网上由信号控制来适应这些交通流的思想,并由此建立了交通分配和信号控制的Stackelberg博弈模型,模型的上层是动态用户最优,下层是信号控制优化.然后,通过对模型离散化应用模拟退火算法进行求解.最后,对一个简单的交通网络进行仿真,仿真结果表明所提方法的有效性.     

交叉口有交通信号控制时用户最优动态配流模型

针对智能交通系统（ＴＴＳ）基础上中主要基础理论之一的动态交通分配问题,在现有研究成果的基础上,提出了更接近真实路网的多起点多旋点交叉口设置有交通信号控制（Ｔｒａｆｆｉｃ Ｓｉｇｎａｌ Ｃｏｎｔｒｏｌ）时的动态配流模型,给出了对现有的通过Ｆｒａｎｋ Ｗｏｉｆｅ算法所得的ＤＵＯ配流解进行修正的原则,最后的算例表明由修正后的模型和算法所得到的ＤＵＯ配流解满足ＴＳＣ约束。     

基于TdPN的中小流量交叉口信号控制研究

针对城市中小流量交叉口交通拥堵问题,提出了一种基于时延Petri网(Timed Petri Net, TdPN)的可变相序信号控制模型。利用TdPN建立交叉口车流模型和信号控制模型,结合马尔可夫链,建立交通流的动态生成模型。通过将通行权赋予当前等待车辆数最大的相位来实现相位的随机选择。以平均延迟时间最小为优化目标,通过遗传算法求解最优相位配时。在信号周期固定的情况下,分析基于TdPN的四相位可变相序控制模型在不平衡交通流下对交叉口平均排队长度的影响,并将此模型与四相位固定相序控制模型进行对比。研究结果表明,该方案在单位时间内有效地减少了交叉口的平均排队长度。     

动态平衡运量配流问题及其稳态伴随解算法

动态平衡运量配流问题的建模和求解是发展智能车辆导驶系统的理论基础.本文对多起点单讫点交通网络建立了连续时间最优控制模型,该模型的最优解对应于动态用户平衡解.文中提出的稳态伴随解算法避免了解复杂的两点边界值问题,可以获得原问题的近似解.该算法利用了交通网络和计算机网络的结构特性,计算速度较高,可用于城市交通流的实时控制.     

基于路口相位方案优化设计的城市道路交通信号迭代学习控制方法研究

城市道路交叉口信号控制是交管工作持续关注的课题,关于协调好有限的道路资源与日益增长的交通需求之间的矛盾,有着至关重要的作用。由于道路自身条件约束,交通流的组成特点复杂,路网交通路呈现非线性动态特征,无法进行精准的数学建模控制。本文提出的迭代学习控制方法,根据交通流的组成和变化特点调整信号控制周期及有效绿灯时长,实现交通信号动态优化控制,保证车辆在路网中能够高效、平稳地通行,是针对非线性动态交通流的一种动态寻优控制算法,能够有效减少路口车辆等待时间、提高通行效率。考虑对不同相位设计方案的适应性,在传统配时优化模型的基础上,构建综合相位设计元素的交通信号迭代学习控制模型,并通过Vissim仿真软件和Python编程语言搭建仿真测试环境,验证了提出模型的有效性。     

基于变结构离散动态BN的最优交通路径规划

为了确保城市路网交通流平稳运行和各路段交通流量合理分配,提出了一种基于变结构动态BN的最优交通路径规划方法。该方法考虑驾驶员偏好,按时间序列建立适用于交通路径规划的变结构离散动态BN模型,采用最大似然估计算法和参数的自适应产生算法学习网络参数,用基于时间窗的动态BN近似推理算法中固定窗口宽度方法进行在线推理。结合实例对算法进行仿真,并与Dijkstra算法所得结果进行比较。实验结果表明变结构离散动态BN能利用实时采集到的信息对最优路径进行实时更新,在线决策。     

协调控制子区快速动态划分方法研究

通过建立基于交叉口关联度的协调控制子区划分模型,设计最佳控制子区划分方 案获取流程,实现了交通信号控制子区的动态划分;针对最佳控制子区划分方案求 解过程中可能面临的维数灾难问题,提出利用降维处理与遗传算法两种方法进行 子区划分方案的快速寻优,并给出了一套协调控制子区快速动态划分流程.通过算 例分析说明,在一定规模路网之下本文提出的控制子区快速动态划分方法将以一个 较大概率搜索到次优子区划分方案,且因其计算时间可以被有效控制,因此完全能够 满足控制子区划分的实时性要求,可推广应用于城市区域交通信号实时控制系统中.     

多车道交通流非线性优化控制模型及遗传算法

多车道高速公路交通流的递阶控制结构分为协调控制层、路段控制层和车辆控制层,文中提出了路段控制层的优化控制模型。该模型以路段上交通流量与平均速度最大化为控制目标,以该路段上车辆在各车道的分配比例为决策变量。决策变量的个数比传统线性模型大为减少。为避免一般优化算法可能得到局部最优解的缺点,采用遗传算法求解所提出的非线性规划模型。仿真算例验证了模型及求解算法的有效性。     

最优动态配流问题的离散模型及其解

对一般路网建立了具有良好结构形式的离散动态配流模型，该模型在多起点单讫点路网中的最优解满足交通流守恒约束和动态平衡原则。在提出了完全不同路段概念的基础上，对最优解的奇异性进行了分析，得到稳态最优解为非奇异解的结论。给出的算例验证了结论的正确性。