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首页> 外文期刊>Transportation Research Part B: Methodological >A piecewise trajectory optimization model for connected automated vehicles: Exact optimization algorithm and queue propagation analysis
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A piecewise trajectory optimization model for connected automated vehicles: Exact optimization algorithm and queue propagation analysis

机译：连接的自动车辆的分段轨迹优化模型：精确优化算法和队列传播分析

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This paper formulates a simplified traffic smoothing model for guiding movements of connected automated vehicles on a general one-lane highway segment. Adapted from the shooting heuristic proposed by Zhou et al. (2017) and Ma et al. (2017), this model confines each vehicle’s trajectory as a piecewise quadratic function with no more than five pieces and lets all trajectories in the same platoon share identical acceleration and deceleration rates. Similar to the shooting heuristic, the proposed simplified model is able to control the overall smoothness of a platoon of connected automated vehicles and approximately optimize traffic performance in terms of fuel efficiency and driving comfort. While the shooting heuristic relies on numerical meta-heuristic algorithms that cannot ensure solution optimality, we discover a set of elegant theoretical properties for the general objective function and the associated constraints in the proposed simplified model, and consequentially propose an efficient analytical algorithm for solving this problem to the exact optimum. Interestingly, this exact algorithm has intuitive physical interpretations, i.e., stretching the transitional parts of the trajectories (i.e., parts with acceleration and deceleration adjustments) as far as they reach the upstream end of the investigated segment, and then balancing the acceleration and deceleration magnitudes as close as possible. This analytical exact model can be considered as a core module to a range of general trajectory optimization problems at various infrastructure settings. Numerical examples reveal that this exact algorithm has much more efficient computational performance and the same or better solution quality compared with the previously proposed shooting heuristic. These examples also illustrate how to apply this model to CAV control problems on signalized segments and at non-stop intersections. Further, we study a homogeneous special case of this model and analytically formulate the relationship between queue propagation and trajectory smoothing. One counter-intuitive finding is that trajectory smoothing may not always cause longer queue propagation but instead may mitigate queue propagation with appropriate settings. This theoretical finding has valuable implications to joint optimization of queuing management and traffic smoothing in complex transportation networks.
机译：本文制定了简化的交通平滑模型，用于指导通用单车道高速公路段上连接的自动车辆的运动。改编自Zhou等人提出的射击启发法。 （2017）和Ma et al。 （2017年），该模型将每条车辆的轨迹限制为不超过五段的分段二次函数，并且使同一排中的所有轨迹共享相同的加速和减速率。类似于射击启发法，所提出的简化模型能够控制连接的自动驾驶汽车排的总体平滑度，并在燃油效率和驾驶舒适性方面大致优化交通性能。虽然射击启发法依赖于不能确保求解最优性的数值元启发式算法，但我们在提出的简化模型中发现了一组通用目标函数和相关约束的优雅理论性质，因此提出了一种有效的解析算法来解决这一问题。问题完全精确。有趣的是，这种精确的算法具有直观的物理解释，即将轨迹的过渡部分（即具有加速和减速调整的部分）延伸到到达研究段的上游端，然后平衡加速和减速的幅度尽可能接近。在各种基础设施设置下，可以将这种精确分析模型视为解决一系列一般轨迹优化问题的核心模块。数值算例表明，与之前提出的射击启发式算法相比，该精确算法具有更高的计算性能以及相同或更好的解决方案质量。这些示例还说明了如何将此模型应用于信号灯段和不间断路口的CAV控制问题。此外，我们研究了该模型的齐次特殊情况，并解析地阐述了队列传播与轨迹平滑之间的关系。一个违反直觉的发现是，轨迹平滑可能并不总是导致更长的队列传播，而是可以通过适当的设置来减轻队列传播。这一理论发现对复杂交通网络中排队管理和交通顺畅的联合优化具有重要的启示。

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