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https://doi.org/10.1007/s11227-025-07725-6

Abstract: Autonomous driving technology, as the core pillar of vehicle intelligence, shows great potential in improving road network vehicle speed, ride comfort, and traffic safety. However, this technology still faces many serious challenges, such as the high complexity of scenarios, insufficient coupling between decision-making and control, and the difficulty of ensuring safety in environments with dense vehicle interactions, especially in scenarios that require high computational power and real-time response. To address these challenges, this study proposes an integrated decision-making and control framework based on deep reinforcement learning. This framework cleverly combines the discrete double delayed deep Q-network (D3QN) for lane-changing decisions and the continuous twin delayed deep deterministic policy gradient (TD3) algorithm for car-following control. Additionally, the study introduces a context-aware lane-changing benefit function and a coordinated longitudinal control strategy to balance individual vehicle performance with the optimization of overall traffic flow. To achieve efficient coordination between the two layers, D3QN and TD3 are trained in a coupled manner. Experimental results on the simulation of urban mobility simulation platform show that under conditions of 2000 pcu/h traffic flow and 50% penetration rate of connected and autonomous vehicles (CAVs), this method can increase the average road network vehicle speed by up to 4.58% and improve the cruising speed of CAVs by 4.32% compared to the baseline model, with this advantage becoming more pronounced as traffic flow increases. These achievements fully validate that this framework can help CAVs make more intelligent and real-time decisions, thereby significantly improving the efficiency of the entire traffic system.

https://link.cnki.net/urlid/42.1824.U.20250320.1020.013.

摘要：在大力发展公共交通的今天，智能网联车和自动驾驶车辆成为人们的研究热点，私家车与公共交通工具都有无人驾驶技术的运用，然而距离道路交通完全实现自动驾驶还有很长的路要走，因此聚焦于人工驾驶车辆与智能网联车辆混行的驾驶环境，结合公交优先战略的政策，在人工驾驶车辆与智能网联车分离的前提下，将智能网联技术的应用精准化，即运用于公交车辆，由拓扑行为放行网联公交车，与信号灯配合，实现公交车的无信号交叉口控制，人工驾驶车辆由信号灯控制，并以单位时间放行效率最大化为优化目标。拟采用SUMO与Python的二次开发作为建模基础，模拟车辆在交叉口的放行状态，最终得出，在交叉口网联公交车渗透率为0.2-0.3，饱和度为0.65-0.85时，道路放行效率提升23.21%，在交叉口车流量为6000～7000veh/h，车辆通过率为95%。

https://link.cnki.net/urlid/42.1824.U.20250320.1748.076.

摘要：为实现城市路网区域绿波控制，基于经典的MULTIBAND-96模型，以绿波带宽度为优化目标，建立了路网子区信号协调控制Asymmetrical Multi-Band model for network（AM-BAND-N）模型，将子区划分方法与信号协调控制相结合，通过绿波协调控制，来实现区域的最优信号协调控制。以武汉市洪山区6×3路网为例进行VISSIM仿真，仿真求解结果表明：该子区划分的绿波协调控制模型与经典绿波控制模型相比能显著降低系统延误时间和停车时长，提高行程速度，大大提高了整体交通服务水平。

https://doi.org/10.1049/itr2.12523

Abstract: Connected and autonomous driving technologies offer a novel solution for intersection control optimization. Connected and autonomous vehicles (CAVs) can access signal plans and optimize trajectories to minimize delays and reduce fuel consumption. However, optimizing trajectories for individual vehicles significantly increases complexity, especially for joint optimization of traffic signals and vehicle trajectories. Given the current technical, regulatory, and policy constraints, a superior intersection management approach is necessary before fully automated driving is achieved. This paper introduces an adaptive coupling control (ACC) method based on vehicle platooning to optimize signal timings and vehicle trajectories in mixed traffic. Initially, vehicle platoon segmentation is conducted, led by CAVs. The study then proposes a single-layer coupled optimization model based on vehicle platoons, simplifying the joint optimization model. To address logistic constraint difficulties, a linearization of the coupled model (LCM) method is developed. Numerical experiments demonstrate that the ACC method significantly reduces vehicle delay and fuel consumption. At high CAV penetration rates (0.8 < R <1) and high traffic volumes (over 900 pcu/h), vehicle platoon control delivers excellent performance, with delays and fuel consumption even lower than in a fully automated environment (R = 1). This surprising result suggests that the mixed platoon system (ACC method) positively impacts mixed traffic.

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摘要：随着城市的进步和不断发展，智能驾驶车辆逐渐代替路段中的部分人工驾驶车辆，但在未来较长时间内人工驾驶车辆并不会被完全取代，此时出现网联车与人工驾驶车辆的混驾环境，即目前以及未来时间内我们面临的驾驶环境。网联车与人工驾驶车辆驾驶行为在路段内相互干扰，造成混合车流行驶效率低下。为减弱2种车辆间的相互作用，提出一种分离混驾环境下网联车和人工驾驶车辆的分阶段动态车道引导算法(dynamic lane guidance algorithm for separating CAVs and HDVs in mixed traffic environment, SCHME)。通过该算法分离在交叉口上游路段的混合流车辆集合，调整智能驾驶车辆的行驶路线并进行实时动态更新，在满足运动学约束收敛的条件下，人工驾驶车辆根据网联车的动态路线进行相应调整，实现在每辆车广义安全损失成本最小的情况下提高路段内混驾环境下车辆运行效率。通过MATLAB模拟车辆在进入交叉口前的车辆运行状态，结果表明，SCHME算法可在广义安全损失成本最小的情况下提高路段内平均车辆通行效率(17.29%),同时当车辆优化数组越大，车辆集合距离交叉口越远时，智能驾驶车辆渗透率越低，每辆车的道路广义安全损失成本越低。 

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摘要：基于公交信号优先策略，对MULTIBAND模型进行改进，重新定义了权重系数、车道清空时间和最小绿波带宽，结合不同相位放行方式实施红灯早断或绿灯延长控制策略，并在此基础上考虑公交车辆的车速与停靠时间，建立对公交车辆的约束，提出基于公交优先的干线协调信号控制改进模型。通过VISSIM仿真，将该模型与传统干线协调信号控制模型对比分析，结果表明：与传统协调控制模型相比，模型使社会车辆平均延误降低了4.77%,停车次数降低了3.77%,公交车辆平均延误降低了11.35%,停车次数降低了2.06%,人均延误降低了7.22%,且在不同的公交流量下均有较好的效果。

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摘要：文中提出了基于先到先通行策略的智能车辆在交叉口的控制方法(FCFS-OCM),构建了以最小车辆通行时间为目标的非线性优化模型，该模型通过计算投影车辆间的安全车头时距，判断车辆冲突情况，同时利用安全车头时距与超车行为等对车辆进行约束，通过遗传算法进行求解得到所有投影车辆的最优控制命令(加速度),并将控制命令反馈到智能车辆，使得车辆平均延误降低，提高了交叉口的运行效率.通过SUMO仿真平台进行验证.结果表明：在交叉口饱和度较低的情况下，FCFS-OCM策略相较传统FCFS与信号控制策略车辆平均延误降低的并不明显，随着交叉口饱和度的增加，FCFS-OCM策略的优势会逐渐增大，但在交叉口饱和度大于0.75时，优势减小，但相较于FCFS策略与信号控制策略车辆平均延误分别降低72%、16%,同时交叉口的通行能力也相较上述两种方案分别提高了15%与68%.

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摘要：文中提出了考虑公交优先的公交车与CAV的联合专用道控制策略.根据CAV的平均车速、公交车的期望车速以及公交站点个数设置公交清空距离模型，根据CAV是否妨碍公交优先设置CAV进入和离开联合专用道的换道控制模型，其中公交车以出行时间最小为优化目标.结果表明：与完全式公交专用道控制方法相比，车均出行时间和人均出行时间分别减少了7.71%和8.19%,且联合专用道的利用率提升了27.16%以上；与不考虑公交优先的控制方法相比，车均出行时间和人均出行时间分别增长了3.25%和2.95%,但公交车的车均出行时间减少了10.35%.且该控制方法在网联车渗透率处于0.3～0.4的区间内得到的控制效果最为显著.

https://doi.org/10.3390/app13105883

Abstract: This paper proposes a dynamic right-of-way priority control approach for emergency vehicles (PDR-EVs) to improve their efficiency on basic road sections in the city based on a cooperative vehicle infrastructure system. Specifically, a movable physical function area was set in front of the EVs to prohibit connected vehicles (CVs) from entering a lane or to request them to change lanes to avoid a collision. Setting up a dynamic monitoring area at the EV’s front end affords real-time monitoring of the CV’s headway distribution in the inner lane. Moreover, a lane change request is sent when the CVs enter the buffer area, and the traversal search method predicts the optimal time and rate of speed to change the lane change and guides the CVs ahead of the EVs to merge into the target gap. Extensive simulations using the SUMO platform revealed that the priority of the dynamic right-of-way (PDR) control method reduced the average delay of the EVs by more than 70%, given that the road saturation did not exceed 0.8 and hardly increased the delay of the CVs (not more than 8%). Moreover, the simulations revealed that the long buffer area was suitable for low-volume conditions, and the short one was suitable for high-volume conditions. The proposed methodology fully employs the road space resources and enhances the EV’s operating efficiency on basic road sections while considering the CV’s operating efficiency.

https://doi.org/10.1155/2022/4879049

Abstract: Traffic signal coordination on urban arterials often requires that timing plans be divided either spatially into clusters of signalized intersections or temporally as time-of-day-based plans. This research proposes a method of dividing timing plans by both spatial and temporal factors simultaneously, in order to provide a dynamic coordinated signal control plan suitable for handling variations in intersection demands and fluctuations in traffic flow. The optimal coordination phase difference of adjacent space coordination subarea is obtained through the method of set operation, so that the spatial subareas can be connected. Similarly, timing plans are dynamically grouped into times of day using the concept of risk decision-making by solving the minimum value of the risk function. Divisions can be further adjusted in real time by changing the conditions, thus resulting in dynamic coordinated signal control. The proposed method was tested in a microscopic simulation of a real-world arterial based on empirical volumes and turning movements. The results showed that the proposed model produced greater reductions in delay and queue length when compared to the methods that subdivide by spatial or temporal thresholds alone. Sensitivity analysis revealed that the proposed method was better suited to imbalances in directional volumes when compared to spatial or temporal division methods alone.

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内容提要：本书针对我国高速公路改扩建交通组织进行研究和探讨。主要介绍高速公路改扩建发展与研究概况，探讨交通组织研究、设计、实施的内容与方法。本书更侧重于从设计者角度进行探讨，主要为参与高速公路改扩建工作的设计人员、管理者、施工技术人员提供技术参考。本书亦可作为相关教学、研究参考用书。