康振

入学时间：2020级

答辩时间：2025年

论文题目：考虑减排的多交叉口多目标协同控制方法研究

中文摘要

摘要

在全球积极应对气候变化的背景下，中国明确提出“碳达峰”与“碳中和”的战略目标。道路交通领域作为重要的排放源，其碳排放约占全国总排放量的8%至10%，已成为碳减排工作的重点领域。交通控制因其较高的效费比优势，长期以来被视为缓解道路交通问题的核心手段。传统交通控制手段主要为基于集计交通流建模的信号控制（如单交叉口信号控制、多交叉口绿波协调等），尽管在缓堵上有所成效，但其均质化假设忽略了车辆微观排放差异，导致排放计算精度不足，难以支撑精准的减排优化。随着网联通信与自动驾驶技术的快速发展，车辆端的微观控制手段（生态驾驶、速度引导）被广泛用于道路交通缓堵减排。然而，此类方法基于车载感知执行个体决策，缺乏对交通系统的整体认知与建模，难以实现系统层面的最优。

因此，本文聚焦于城市道路多交叉口场景，以“调控车辆微观速度，实现系统减排增效”为核心学术思想，分别从交叉口上游路段控制、单交叉口控制、多交叉口控制三个层次开展研究，以“车辆与交通流协同、车辆与信号协同、多交叉口协同”为三类协同控制手段，形成支撑减排增效的协同控制方法体系。

首先，针对现有控制方法中存在的交叉口排放特征不明、系统层面优化效果不足、控制框架不完善等问题，研究构建了面向减排增效多交叉口协同控制框架。基于真实的轨迹数据集，精准识别多交叉口排放特征，系统梳理面向减排增效的控制场景与控制对象。进一步地，分别针对微观的车辆排放特征与系统层面的排放优化进行了预实验分析，明确了基于加速工况优化实现减排的车辆端微观控制方法，以及“车辆与交通流协同、车辆与信号协同、多交叉口协同”三类实现交叉口减排增效的控制需求。在此基础上，归纳总结了面向减排的城市道路多交叉口信号控制的基本需求、基本条件与基本优化目标，明确了协同控制方法研究的总体理论框架。

其次，面向交叉口上游路段控制场景，考虑现有车辆端的微观控制策略多以个体最优为目标，对系统最优的考量不足，导致车辆群体的排放激增问题，提出了“车辆与交通流协同”的控制方法。该方法构建了基于“幽灵密度”量化CAV对交通流影响的系统动态模型。幽灵密度作为统一的状态量，同时参与车辆端和路侧端的控制决策：车辆端的协调速度上限受到其所在路段幽灵密度的约束；路侧端信号控制则以幽灵密度作为信号控制优化的状态变量，动态调整信号相位。微观仿真实验评估了在不同场景下（不同的CAV渗透率、交通需求水平）与生态驾驶策略相比的性能。所提出的方法可以实现路段下游交叉口排放量减少0.51%至21.52%，通行能力增加11.83%。

再次，面向单交叉口控制场景，针对交叉口已有信号控制方法未考虑车辆层面的行进过程、信号绿灯时间未能被车辆充分利用、引发车辆频繁启停增排的问题，研究构建了“车辆与信号协同”的双层控制框架。该框架通过解耦设计车辆侧与路侧的控制目标，提出了一个双层优化模型：上层对路侧端的信号配时优化，最小化交叉口延误以提升通行效率；下层对微观车辆速度引导优化，分别提出了全局排放最小的直接优化模型，和交叉口停车次数及停车时长最小的间接优化模型，实验证明间接优化模型在牺牲2.3%~2.8%的最优解性能下，能够提升求解效率以满足实时控制的需求。微观仿真实验评估了在不同场景下（不同的受引导车辆渗透率、交通需求水平、不均衡交通需求）与已有控制方法相比的性能优势：所提出的方法可实现碳排放减少5.24%至17.60%，延误减少22.82%至52.62%。此外，针对现有车辆速度引导方法落地应用困难的问题，研究创新性地提出一种网联环境下的碳激励机制，以经济激励的方式保障车辆对速度引导的服从性。在碳激励机制下，车辆每通过一个交叉口的碳激励平均奖励在0.0015至0.016元之间，交叉口的碳减排成本为7.5至31.5元/吨。

最后，面向多交叉口控制场景，针对传统多交叉口协调控制方法难以协调不同路径上的微观车辆通行需求，不同路径的车辆会在某一交叉口聚集、拥堵与排队，导致全局减排增效困难的问题，研究提出了考虑减排的多交叉口分布式协同控制方法。在分布式控制框架下，研究将大规模路网问题拆分为小区域局部子问题，使各交叉口能够基于局部车辆路径信息独立完成多目标优化。进一步地，将小规模交叉口区域局部子问题拆解为相邻交叉口的供需匹配问题与单交叉口的多目标优化问题。二者在局部子问题中互为上下游，可以实现数据与控制方法的互通，二者协同可以避免局部瓶颈的转移与扩散。微观仿真实验结果表明，本研究提出的控制方法相较于传统的周期-绿信比-相位差协同配时方法，可降低路网平均排放7.64%至32.15%，降低平均延误5.84%至25.01%，提升路网通过量9.15%~16.30%。

本文以“调控车辆微观速度，实现系统减排增效”为研究主线，从面向减排增效的交通控制基础问题解析出发，系统梳理了交叉口信号控制的对象、场景与控制框架，构建了面向减排增效的车辆与交通流协同、车辆与信号协同、多交叉口协同的控制方法，初步形成了考虑减排的多交叉口多目标协同控制基本架构，具有重要的理论意义与实用价值。

关键词：城市交通，节能减排，网联环境，协同控制，多目标优化

英文摘要

ABSTRACT

Against the backdrop of global efforts to address climate change, China has explicitly set the strategic targets of “carbon peaking” and “carbon neutrality.” As a major emission source, the road transportation sector accounts for approximately 8%–10% of the country’s total carbon emissions, making it a priority area for emission reduction. Owing to its high cost-effectiveness, traffic control has long been regarded as a core approach to mitigating road traffic problems. Traditional control strategies primarily rely on macroscopic models based on aggregated traffic flow (e.g., isolated intersection signal control, green-wave coordination for multiple intersections). However, their homogeneity assumptions neglect the heterogeneity of vehicle-level emissions, resulting in insufficient accuracy in emission estimation and limiting the effectiveness of emission-oriented optimization. With the rapid advancement of connected communication and autonomous driving technologies, vehicle-level microscopic control strategies (such as eco-driving and speed guidance) have been increasingly adopted for emission reduction. Nevertheless, vehicle-side methods are executed through onboard perception for individual decision-making, lacking holistic awareness and modeling of the macroscopic traffic system. Consequently, a disjunction emerges between macroscopic traffic-flow control and microscopic vehicle control: macroscopic strategies are difficult to implement at the microscopic level, whereas microscopic strategies fail to respond to macroscopic objectives. As a result, relying on a single type of control measure cannot fully unlock the optimization potential of the system, hindering the dual goals of emission reduction and efficiency improvement.

To address this gap, this study focuses on urban road networks with multiple intersections and advances the concept of “macro–micro coordination,” wherein vehicle-level speed regulation is harnessed to achieve system-wide emission reduction and efficiency improvement. The research spans three levels—upstream road segments, single intersections, and multiple intersections—through three types of coordination: vehicle–traffic flow coordination, vehicle–signal coordination, and multi-intersection coordination. Together, these form a methodological framework for macro–micro coordinated traffic control to support emission reduction and efficiency gains.

First, to tackle the disjunction between macroscopic and microscopic control in existing methods—which impedes emission reduction and efficiency improvement—this study develops a macro–micro coordinated control framework. Based on real trajectory datasets, the emission characteristics of multiple intersections are identified with high precision, and the control scenarios and objects for emission reduction are systematically clarified. Pre-experiments are conducted on microscopic vehicle emission characteristics and macroscopic system-level optimization, confirming vehicle-level control strategies that reduce emissions through acceleration pattern optimization, and identifying three system-level control needs: vehicle–traffic flow coordination, vehicle–signal coordination, and multi-intersection coordination. On this basis, the fundamental requirements, preconditions, and optimization objectives for emission-oriented urban intersection signal control are summarized, leading to the definition of the overall theoretical framework for coordinated control.

Second, for upstream road segment control, given that existing vehicle-level microscopic control strategies inadequately account for macroscopic system benefits—and may induce traffic flow fluctuations, causing excessive acceleration/deceleration emissions—a “vehicle–traffic flow coordination” method is proposed. This method constructs a system dynamics model in which the “phantom density” quantifies the impact of connected and automated vehicles (CAVs) on traffic flow. Phantom density serves as a unified state variable simultaneously governing decisions on both the vehicle and infrastructure sides: vehicle coordination speed is constrained by the phantom density of its segment, while signal control uses phantom density as the state variable for dynamic phase optimization. Microscopic simulations evaluate performance under various conditions (different CAV penetration rates, traffic demand levels), compared with eco-driving. Results show that the proposed method reduces emissions by 0.51%–21.52% and increases intersection throughput by 11.83%.

Third, for single-intersection control, considering that conventional macroscopic signal control methods ignore microscopic vehicle progression and underutilize green times—causing frequent stop-and-go behavior and additional emissions—a bi-level “vehicle–signal coordination” framework is developed. By decoupling objectives for vehicles and signals, a bi-level optimization model is designed: the upper level optimizes signal timing to minimize delay and enhance efficiency, while the lower level optimizes vehicle speed guidance. Two formulations are proposed for the lower level: (i) a direct optimization model minimizing global emissions, and (ii) an indirect optimization model minimizing the number and duration of stops. Experiments demonstrate that the indirect model sacrifices only 2.3%–2.8% performance but achieves greater computational efficiency to support real-time implementation. Microscopic simulations across scenarios (different guided-vehicle penetration rates, demand levels, and demand imbalance) reveal substantial benefits: emissions reduced by 5.24%–17.60% and delays reduced by 22.82%–52.62%. Furthermore, recognizing the practical difficulty of implementing vehicle speed guidance, this study innovatively proposes a carbon incentive mechanism in connected environments. By providing economic incentives, the mechanism enhances vehicle compliance with guidance. The average carbon incentive reward per vehicle per intersection is 0.0015–0.016 CNY, corresponding to a carbon reduction cost of 7.5–31.5 CNY per ton.

Finally, for multi-intersection control, traditional macroscopic coordination methods struggle to reconcile vehicle progression needs along different paths, often resulting in vehicle clustering, congestion, and queues at certain intersections—hindering global emission reduction and efficiency improvement. To address this, a distributed multi-intersection coordination method is proposed. Under the distributed framework, the large-scale network problem is decomposed into smaller regional subproblems, allowing each intersection to independently perform multi-objective optimization using local vehicle path information. Further, each subproblem is decomposed into two interdependent components: (i) upstream–downstream supply–demand matching between adjacent intersections, and (ii) single-intersection multi-objective optimization. These components exchange data and coordinate decisions, thereby preventing the propagation of local bottlenecks. Simulation results show that, compared to conventional cycle–green ratio–offset coordination, the proposed method reduces average network emissions by 7.64%–32.15%, average delays by 5.84%–25.01%, and increases network throughput by 16.30%.

In summary, this study advances the research paradigm of “regulating vehicle-level speeds to achieve system-level emission reduction and efficiency gains.” Beginning with the fundamental issues of emission-oriented traffic control, it systematically analyzes the objects, scenarios, and frameworks of intersection signal control, and develops coordinated control methods for vehicle–traffic flow, vehicle–signal, and multi-intersection coordination. Collectively, these efforts establish a foundational architecture for multi-objective, emission-oriented coordinated control of urban intersections, offering both theoretical insights and practical value.

Key Words:Urban Traffic, Energy Conservation and Emission Reduction, Connected Environments, Cooperative Control, Multi-Objective Optimization