Stability analysis of traffic flow with extended CACC control models

To further investigate car-following behaviors in the cooperative adaptive cruise control（CACC） strategy,a comprehensive control system which can handle three traffic conditions to guarantee driving efficiency and safety is designed by using three CACC models.In this control system,some vital comprehensive information,such as multiple preceding cars’ speed differences and headway,variable safety distance（VSD） and time-delay effect on the traffic current and the jamming transition have been investigated via analytical or numerical methods.Local and string stability criterion for the velocity control（VC） model and gap control（GC） model are derived via linear stability theory.Numerical simulations are conducted to study the performance of the simulated traffic flow.The simulation results show that the VC model and GC model can improve driving efficiency and suppress traffic congestion.

Prediction and Analysis of Elevator Traffic Flow under the LSTM Neural Network

Elevators are essential components of contemporary buildings, enabling efficient vertical mobility for occupants. However, the proliferation of tall buildings has exacerbated challenges such as traffic congestion within elevator systems. Many passengers experience dissatisfaction with prolonged wait times, leading to impatience and frustration among building occupants. The widespread adoption of neural networks and deep learning technologies across various fields and industries represents a significant paradigm shift, and unlocking new avenues for innovation and advancement. These cutting-edge technologies offer unprecedented opportunities to address complex challenges and optimize processes in diverse domains. In this study, LSTM (Long Short-Term Memory) network technology is leveraged to analyze elevator traffic flow within a typical office building. By harnessing the predictive capabilities of LSTM, the research aims to contribute to advancements in elevator group control design, ultimately enhancing the functionality and efficiency of vertical transportation systems in built environments. The findings of this research have the potential to reference the development of intelligent elevator management systems, capable of dynamically adapting to fluctuating passenger demand and optimizing elevator usage in real-time. By enhancing the efficiency and functionality of vertical transportation systems, the research contributes to creating more sustainable, accessible, and user-friendly living environments for individuals across diverse demographics.

Traffic flow velocity disturbance characteristics and control strategy at the bottleneck of expressway

In the three-phase traffic flow studies, the traffic flow characteristic at the bottleneck section is a hot spot in the academic field. The controversy about the characteristics of the synchronized flow at bottleneck is also the main contradiction between the three-phase traffic flow theory and the traditional traffic flow theory. Under the framework of three-phase traffic flow theory, this paper takes the on-ramp as an example to discuss the traffic flow characteristics at the bottleneck section.In particular, this paper mainly conducts the micro-analysis to the effect of lane change under the two lane conditions, as well as the effect of the on-ramp on the main line traffic flow. It is found that when the main road flow is low, the greater the on-ramp inflow rate, the higher the average speed of the whole road section. As the probability of vehicles entering from the on-ramp increases, the flow and the average speed of the main road are gradually stabilized, and then the on-ramp inflow vehicles no longer have a significant impact on the traffic flow. In addition, this paper focuses on the velocity disturbance generated at the on-ramp, and proposes the corresponding on-ramp control strategy based on it, and the simulation verified that the control strategy can reasonably control the traffic flow by the on-ramp, which can meet the control strategy requirements to some extent.

A control method applied to mixed traffic flow for the coupled-map car-following model

In light of previous work [Phys. Rev. E 60 4000 （1999）], a modified coupled-map car-following model is proposed by considering the headways of two successive vehicles in front of a considered vehicle described by the optimal velocity function. The non-jam conditions are given on the basis of control theory. Through simulation, we find that our model can exhibit a better effect as p = 0.65, which is a parameter in the optimal velocity function. The control scheme, which was proposed by Zhao and Gao, is introduced into the modified model and the feedback gain range is determined. In addition, a modified control method is applied to a mixed traffic system that consists of two types of vehicle. The range of gains is also obtained by theoretical analysis. Comparisons between our method and that of Zhao and Gao are carried out, and the corresponding numerical simulation results demonstrate that the temporal behavior of traffic flow obtained using our method is better than that proposed by Zhao and Gao in mixed traffic systems.

A Survey of Model Predictive Control Methods for Traffic Signal Control

Enhancing traffic efficiency and alleviating(even circumventing) traffic congestion with advanced traffic signal control(TSC) strategies are always the main issues to be addressed in urban transportation systems. Since model predictive control(MPC) has a lot of advantages in modeling complex dynamic systems, it has been widely studied in traffic signal control over the past 20 years. There is a need for an in-depth understanding of MPC-based TSC methods for traffic networks. Therefore, this paper presents the motivation of using MPC for TSC and how MPC-based TSC approaches are implemented to manage and control the dynamics of traffic flows both in urban road networks and freeway networks. Meanwhile, typical performance evaluation metrics, solution methods, examples of simulations,and applications related to MPC-based TSC approaches are reported. More importantly, this paper summarizes the recent developments and the research trends in coordination and control of traffic networks with MPC-based TSC approaches. Remaining challenges and open issues are discussed towards the end of this paper to discover potential future research directions.

A control method for congested traffic in the coupled map car-following model

Based on the pioneer work of Konishi et al, a new control method is presented to suppress the traffic congestion in the coupled map （CM） car-following model under an open boundary. A control signal concluding the velocity differences of the two vehicles in front is put forward. The condition under which the traffic jam can be contained is analyzed. The results axe compared with that presented by Konishi et al [Phys. Rev. 1999 E 60 4000-4007]. The simulation results show that the temporal behavior obtained by our method is better than that by the Konishi＇s et al. method, although both the methods could suppress the traffic jam. The simulation results are consistent with the theoretical analysis.

Control Chaos of a Unidirectionally Traffic Coupled Map Lattice Model

Spatiotemporal chaos is studied by using the unidirectional traffic coupled lattice model with hyperbolic tangent local map. The coupled map lattice (CML) model can simulate the complex traffic flow phenomenon which is similar to the traditional traffic flow model. The nonlinear feedback method is used to study the control of the chaotic system of the unidirectionally traffic coupled map lattice model. The stability of spatiotemporal chaos in the coupled map lattice is realized. The results of numerical simulation show that there is a relationship between control results and control parameters when controlling spatiotemporal chaos to a uniform stable state in a certain phase space compression parameter region.

交通流优化膨胀控制遗传规划算法

针对遗传规划(GP)算法在大规模动态交通流分配中训练超启发式策略时,算法迭代次数的增加而个体平均大小不断膨胀的问题,提出应用不同GP控制膨胀方法来限制种群中大尺寸个体的遗传,让算法能够在训练过程中找到更小且性能更优的超启发式策略。考虑到超启发式策略在如网格式、环形放射式、自由式的不同结构路网上可能存在性能差异,会影响算法在训练过程中对个体的选择,采用不同结构的路网训练出超启发式策略以进行分析比较。训练后的超启发式策略在不同规模和车流量的大城市路网上进行模拟测试。结论是基于双锦标赛的膨胀控制方法对不同结构路网的效果最优,得到的GP算法对比现有调度方法能获得路网整体更短的平均旅行时间,更精简有效的超启发式策略,提高决策效率。

An improved cellular automaton model considering the effect of traffic lights and driving behaviour

This paper proposes an improved cellular automaton model to describe the urban traffic flow with the consideration of traffic light and driving behaviour effects. Based on the model, the characteristics of the urban traffic flow on a single- lane road are investigated under three different control strategies, i.e., the synchronized, the green wave and the random strategies. The fundamental diagrams and time-space patterns of the traffic flows are provided for these strategies respectively. It finds that the dynamical transition to the congested flow appears when the vehicle density is higher than a critical level. The saturated flow is less dependent on the cycle time and the strategies of the traffic light control, while the critical vehicle density varies with the cycle time and the strategies. Simulated results indicate that the green wave strategy is proven to be the most effective one among the above three control strategies.

Iterative learning control approach for ramp metering

An iterative learning control scheme is developed to the traffic densitycontrol in a macroscopic level freeway environment. With rigorous analysis, the proposed intelligentcontrol scheme guarantees the asymptotic convergence of the traffic density to the desired one. Thecontrol scheme is applied to a freeway model, and simulation results confirm the efficacy of theproposed approach.

Scheduling of high-speed rail traffic based on discrete-time movement model

In this paper, a new simulation approach for solving the mixed train scheduling problem on the high-speed double-track rail line is presented. Based on the discrete-time movement model, we propose control strategies for mixed train movement with different speeds on a high-speed double-track rail line, including braking strategy, priority rule, travelling strategy, and departing rule. A new detailed algorithm is also presented based on the proposed control strategies for mixed train movement. Moreover, we analyze the dynamic properties of rail traffic flow on a high-speed rail line. Using our proposed method, we can effectively simulate the mixed train schedule on a rail line. The numerical results demonstrate that an appropriate decrease of the departure interval can enhance the capacity, and a suitable increase of the distance between two adjacent stations can enhance the average speed. Meanwhile, the capacity and the average speed will be increased by appropriately enhancing the ratio of faster train number to slower train number from 1.

Real-Time Traffic Signal Timing for Urban Road Multi-Intersection

this paper develops a real-time traffic signal timing model which is to be integrated into a single intersection for urban road, thereby solving the problem of traffic congestion. We analyze the current situation of the traffic flow with release matrix firstly, and then put forward the basic models to minimize total delay time of vehicles at the intersection. The optimal real-time signal timing model (non-fixed cycle and non-fixed split) is built with the Webster split optimal model. At last, the simulated results, which are compared with conventional model, manifest the promising properties of proposed model.

A new control method based on the lattice hydrodynamic model considering the double flux difference

A new feedback control method is derived based on the lattice hydrodynamic model in a single lane. A signal based on the double flux difference is designed in the lattice hydrodynamic model to suppress the traffic jam. The stability of the model is analyzed by using the new control method. The advantage of the new model with and without the effect of double flux difference is explored by the numerical simulation. The numerical simulations demonstrate that the traffic jam can be alleviated by the control signal.

异质交通流环境下无信号T形交叉口控制策略

针对无信号控制T形交叉口,提出一种异质交通流环境下交叉口的分级限速控制模型.根据异质交通流的车辆运行特性提出分级限速的概念,并建立交叉口的车辆信息矩阵.构建以车流通过冲突区域时间最短为优化目标,以保证冲突车辆安全行驶的时间间隔为约束条件的线性规划模型.通过MATLAB搭建仿真平台对所提出的模型有效性进行试验验证.结果表明:在混合行驶的交通环境下,当车辆到达率为0.30~0.50时,本研究所提模型可以在传统控制模型的基础上提升交叉口通行能力约20%;分级限速控制模型下智能网联车辆渗透率达到0.90时,最多可以增加交叉口约17%的通行能力;所提模型能有效提高道路资源利用效率,保证主路运行通畅的情况下大幅降低支路车辆延误.

Modified coupled map car-following model and its delayed feedback control scheme

A modified coupled map car-following model is proposed, in which two successive vehicle headways in front of the considering vehicle is incorporated into the optimal velocity function. The steady state under certain conditions is obtained. An error system around the steady state is studied further. Moreover, the condition for the state having no traffic jam is derived. A new control scheme is presented to suppress the traffic jam in the modified coupled map car-following model under the open boundary. A control signal including the velocity differences between the following and the considering vehicles, and between the preceding and the considering vehicles is used. The condition under which the traffic jam can be well suppressed is analysed. The results are compared with that presented by t^onishi et al. （the KKH model）. The simulation results show that the temporal behaviour obtained in our model is better than that in the KKH model. The simulation results are in good agreement with the theoretical analysis.

基于不同渗透率下的自适应与协同自适应巡航的仿真研究

随着自动驾驶技术的愈加成熟,在未来道路上必将呈现出不同级别自动驾驶车辆混合行驶的场景。文章利用VTH可变车头时距控制策略对ACC跟驰模型研究,结果显示车辆能够稳定跟驰,且不同渗透率下的道路车辆平均车速有所提高。对CACC协同自适应巡航模型进行仿真对比发现道路通行效率得到提高。最后对不同等级自动驾驶车辆进行仿真,结果显示在高速路段,随着渗透率提高车辆平均速度等都有不同程度的提高。因此证实了随着自动驾驶车辆大规模进入道路,对交通流有着积极的影响。

考虑条件风险价值的公路交通能源系统多能流管控策略

为了实现公路交通背景下多能源系统和电动汽车换电站的经济运行与利益共赢,该文提出考虑条件风险价值(CVaR)的公路交通能源系统(HTES)多能流管控策略。首先,建立了主从博弈双层优化模型,其中上层模型以最小化HTES的综合成本为目标,对其进行多能流的合理管控与优化定价;在下层模型中,换电站根据HTES制定的价格灵活地调整各电池的充放电功率,在满足电能需求的前提下最小化与HTES进行能量交互的成本。然后基于多场景法,引入CVaR衡量可再生能源出力的不确定性所带来的风险成本,利用Karush-Kuhn-Tucker(KKT)条件和强对偶定理将原主从博弈双层模型转换为单层混合整数线性规划问题以计算其纳什均衡解,进而获取最优的运行和定价方案。最后,进行算例仿真。结果表明,所提策略可以兼顾HTES和换电站不同主体间的经济利益,实现运行成本和风险成本的平衡。

基于实时交通流的上海市机动车碳污同源排放清单

为满足交通碳污减排政策多样化、差别化和精细化发展需求,扩大交通排放测算范围、提高测算准确度和时空分辨力,提出了一种基于实时交通流的高时空分辨力机动车碳污同源排放清单构建方法,综合交通模型、IVE模型和相关算法建立了覆盖上海全市的机动车碳污同源排放清单。结果显示,二氧化碳、氮氧化物周排放规律相近,呈现出明显的周末低工作日高的特征;二氧化碳分时变化规律呈现明显的双峰,而氮氧化物受重型车影响,峰值出现在夜间;由于各车型的碳污分担率不同且活动区域不同造成了上海市机动车二氧化碳和氮氧化物排放的空间分布差异明显;碳污同源排放清单中,NOx、PM、CO、VOCs、CO_(2)的月均排放量分别为5160t、440t、1.7×10^(4)t、732t和2.46×10^(6)t;排放分担率表明,NOx、PM等污染物主要来自重货车及大货车排放;CO、VOCs和CO_(2)排放来源以小客车排放占比最高。

目标检测下的车型对交通流速度影响研究

针对传统目标检测跟踪算法识别精度低,实时性差等缺点,提出了一种基于YOLOv5s和DeepSort算法模型的视频车辆实时车流量和速度检测方法。构建了包含25 877个目标样本的数据集,采用YOLOv5s算法模型实现视频车辆的检测,利用DeepSort算法对车辆进行跟踪计数与测速,实现了路段监控对车辆的实时检测。基于深度学习获取的速度与交通量数据,构建了不同车型对交通流速度影响模型,探究畅通状态、稳定状态、拥挤状态下车型的速度与交通量之间的关系,结果表明:算法模型对视频车辆的检测效果良好,平均准确度达到91.4%;不同状态下,车型对交通量速度的影响程度不同;畅通状态和稳定状态下,出租车的速度对交通量影响较大;拥挤状态下,私家车的车速受交通量影响较大。

新型混合交通流场景下交叉口信号控制和轨迹控制协同优化方法

针对人类驾驶车辆(human driven vehicle,HDV)和智能网联车辆(connected and autonomous vehicle,CAV)组成的新型混合交通流场景,现有的交叉口协同控制方法中,集中控制和单车控制分别对中央控制器的算力和车载计算单元的算力要求较高。本文研究了1种将元胞传输模型(cell transmission model,CTM)与双层规划模型相结合的协同优化方法,利用可调整的元胞长度平衡求解信号控制与CAV轨迹优化2个问题所需的算力,从而灵活地根据中央控制器和车载计算单元的算力分配计算资源;通过上层模型预测交通流状态并优化信号控制参数,引入动态调整元胞长度规则,降低中央控制器的计算负担;基于上层的交通状态预测结果,利用下层模型对CAV轨迹进行全局规划,进一步提升交叉口通行效率。同时,为了提升解的最优性和求解的实时性,采用结合随机梯度下降法和遗传算法的迭代优化算法,避免陷入局部最优的同时提升求解效率。最后以无锡市先锋中路与春风南路交叉口数据为例,验证了不同CAV渗透率下优化的效果,结果表明:①相较于基准方案,本文提出的协同优化方案最高可以降低交叉口8.09%的车均行程时间,降低了交叉口拥堵向上游的传播;②当CAV渗透率为30%、60%和90%时,优化比例为2.51%、5.08%和7.88%;③在进口道流量大于3000 pcu/h时,仍能在100s内获得最优信号控制方案,可支持实时优化。该方法可以有效改善城市交通拥堵,提高新型混合交通流场景下交叉口的通行效率。