AI 中文总结
本文提出LMP-GNN模型，用于仅概率重建缺失车道流量，通过透明输入规则适配带符号最大压力控制器，在CityFlow网络实验中表现出更优性能，参数更少、延迟更低且交通效益显著。
AI 中文摘要
自适应交通信号控制依赖及时的车道级观测，但检测器故障、视觉遮挡和通信故障可能导致部分交通状态不可用，进而干扰信号决策。现有研究分别推进了交通数据插补、状态恢复和基于估计状态的控制，但在三者的接口处仍存在空白：目前仍不清楚如何利用当前决策时刻的可用信息仅对缺失的车道流量进行概率重建，同时保留所有观测到的测量值，并通过不变的带符号最大压力（Signed Max-Pressure）控制器追踪重建的影响。为解决这一空白，本文提出LMP-GNN，一种紧凑的车道运动图神经网络，可预测每条车道的均值和边际不确定性。三条透明的输入规则将这些输出转换为缺失车道控制器的输入，而观测到的流量、合法相位、压力计算和相位选择保持不变。该设计将重建效果与策略重新设计隔离开来，可评估其在车道恢复、压力与相位保真度以及闭环交通场景中的表现。在五个CityFlow网络上开展的综合研究包括对需求变化、学习比较器、架构、效率以及SUMO迁移的额外验证。LMP-GNN能准确重建缺失的车道状态，且通常比确定性的道路均值（Road Mean）基线更好地保留控制器决策。在相关缺失情况下，固定车道折扣（Fixed Lane Discount）可使累计平均出行时间最多降低13.74%，而严重的随机缺失则会反转该增益。与两种决策时刻学习的自适应方法相比，所提出的模型使用的参数少89.4%-96.6%，且中位数模型路径延迟降低81.1%-95.0%。总体而言，LMP-GNN提供了一种轻量且可审计的重建到控制接口，具有经验证的交通效益和明确的运行边界。
英文摘要
Missing lane-count observations can distort pressure-based signal decisions even when neighboring detectors remain operational. We propose a lane-movement probabilistic graph neural network (LMP-GNN) that uses the movement relations involved in pressure computation to predict a mean and standard deviation for each lane. Three rules convert these outputs into replacement counts using the mean alone, a fixed uncertainty discount, or a staleness-dependent discount. Observed counts remain unchanged, and the completed state is supplied to an unchanged Signed Max-Pressure controller. Evaluation covers reconstruction and uncertainty calibration, decision-time diagnostics, and closed-loop traffic performance. Across 4,333,392 masked lane events, reconstruction achieved a mean absolute error of 0.7873 vehicles per lane. In a separate stored-trace audit of 372 network-outage-seed cells, pressure-score error was strongly associated with phase disagreement, with a Spearman correlation of 0.929, identifying pressure fidelity as a key decision-level diagnostic. Across five fixed-demand CityFlow networks, the fixed-discount rule reduced accrued average travel time by up to 13.74% relative to road-level mean imputation under correlated missingness. It also reduced travel time at 60% random missingness, whereas mean imputation performed better at 80% and 90%. The selected model has 63,362 parameters and a median single-thread inference time of 0.983 ms on a central processing unit. These results support lightweight probabilistic lane reconstruction as a practical input to pressure-based control, with traffic benefits that depend on the missingness regime.
Comments24 pages, 15 figures, 6 tables
