A hybrid intersection control strategy for CAVs under fluctuating traffic demands: A value approximation approach

IF 7.6 1区 工程技术 Q1 TRANSPORTATION SCIENCE & TECHNOLOGY
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引用次数: 0

Abstract

Confronted with growing and fluctuating traffic demands, urban transportation system has been encountering mounting challenges in traffic congestion, especially at intersections. With enhanced traffic control precision enabled by the emerging Connected and Automated Vehicle (CAV) technologies, this study proposes a hybrid control strategy for connected and automated traffic at urban intersections, which enables the integration of diverse control schemes to harness their strengths and mitigate their weaknesses. With rolling horizon strategy, a nonlinear optimization model is developed to determine the optimal traffic control plans considering both current status and forthcoming vehicle arrivals. Vehicle delays are elaborately characterized without relying on any empirical assumptions. The original model is converted to a Mixed Integer Programming with Quadratic Constraints (MIP-QC) by employing appropriate linearization techniques, which could be solved by commercial solvers. For the acquisition of instant and reliable solutions, a multilayer feedforward network-based approximate algorithm is developed, referred as Value Approximation Control (VAC) algorithm. Theoretical derivation is provided to validate the capability of VAC algorithm in the precise approximations of the value function in the traffic plan optimization problem, and ultimately enabling to acquire global optimal solutions via specific network design and training techniques. Numerical experiments on both artificial and researcher-collected datasets demonstrate that our proposed VAC algorithm achieves performance nearly equivalent to the mathematical model. Significantly, it outperforms current state-of-art traffic control methods in terms of both intersection throughput and average vehicle delay. Moreover, sensitivity analysis reveals the robustness of the VAC algorithm against inaccuracy in vehicle arrival information, and the stable performance even in the presence of significant disturbances.

交通需求波动下的 CAV 混合交叉口控制策略:值近似方法
面对不断增长和波动的交通需求,城市交通系统在交通拥堵方面遇到了越来越多的挑战,尤其是在交叉路口。随着新兴的车联网和自动驾驶(CAV)技术提高了交通控制精度,本研究针对城市交叉路口的车联网和自动驾驶交通提出了一种混合控制策略,该策略能够整合多种控制方案,以利用其优势并减轻其劣势。通过滚动视野策略,开发了一个非线性优化模型,以确定考虑当前状态和即将到来的车辆的最佳交通控制计划。在不依赖任何经验假设的情况下,对车辆延迟进行了详细描述。通过采用适当的线性化技术,原始模型被转换为带二次约束的混合整数编程(MIP-QC),可由商用求解器求解。为获得即时可靠的解决方案,开发了一种基于多层前馈网络的近似算法,称为值近似控制(VAC)算法。理论推导验证了 VAC 算法在交通规划优化问题中精确逼近值函数的能力,并通过特定的网络设计和训练技术最终获得全局最优解。在人工数据集和研究人员收集的数据集上进行的数值实验表明,我们提出的 VAC 算法几乎达到了与数学模型相当的性能。在交叉路口吞吐量和平均车辆延误方面,该算法明显优于目前最先进的交通控制方法。此外,敏感性分析表明了 VAC 算法对车辆到达信息不准确的鲁棒性,以及即使存在重大干扰也能保持稳定的性能。
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来源期刊
CiteScore
15.80
自引率
12.00%
发文量
332
审稿时长
64 days
期刊介绍: Transportation Research: Part C (TR_C) is dedicated to showcasing high-quality, scholarly research that delves into the development, applications, and implications of transportation systems and emerging technologies. Our focus lies not solely on individual technologies, but rather on their broader implications for the planning, design, operation, control, maintenance, and rehabilitation of transportation systems, services, and components. In essence, the intellectual core of the journal revolves around the transportation aspect rather than the technology itself. We actively encourage the integration of quantitative methods from diverse fields such as operations research, control systems, complex networks, computer science, and artificial intelligence. Join us in exploring the intersection of transportation systems and emerging technologies to drive innovation and progress in the field.
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