AI 中文总结
本研究通过240次城市路网仿真，发现感应式信号控制的时序设置（如最大绿灯倍数）会显著改变其相对定周期控制的性能，且扫描未现最优，提示评估控制器前需先测试时序敏感性。
AI 中文摘要
自适应与定周期交通信号之间的比较，在仅改变其时序设置时可能会发生变化。我们在一个基于OpenStreetMap的城市路网上进行了240次仿真运行，使用八种配置、三种到达率和十对需求种子来检验这种敏感性。实验改变了基于存在的感应控制的最大绿灯时间，重新调整了定周期基准的配时，并引入了一种基于放行的相位终止规则。终点指标是每趟出行在有限时间窗口内的耗时，包括入口等待时间以及因移除或放弃行程而产生的惩罚。在测试的最高负荷下，最大绿灯时间为计划绿灯时长两倍的感应控制，其平均终点指标比原始定周期计划高57.4%。将该倍数降至1.25后，其平均终点指标比同一计划低70.7%。与三个测试定周期计划中最佳者相比，基于放行的相位终止规则在三种负荷下分别将平均终点指标降低了14.8%、15.8%和18.3%。两次时序扫描均偏向其最短测试设置，因此均未识别出最优值。这项探索性研究使用了合成需求、可忽略的启动损失时间以及对持续静止车辆的物理移除。结果支持在将性能归因于控制器类别之前先测试时序敏感性；但并未确立适用于实际部署的设置。
英文摘要
A comparison between adaptive and fixed-time traffic signals can change when only their timing settings change. We examine this sensitivity in 240 simulation runs on one OpenStreetMap-derived urban network, using eight configurations, three arrival rates and ten paired demand seeds. The experiment varies the maximum green of presence-based actuation, retimes the fixed baseline, and includes a discharge-based phase-termination rule. The endpoint is finite-window time spent per offered trip, including entry waiting and penalties for removed or abandoned trips. At the highest tested load, actuation with a maximum green of twice the planned split has a mean endpoint 57.4 per cent above the original fixed plan. Reducing that multiplier to 1.25 puts it 70.7 per cent below the same plan. Against the best of three tested fixed plans, discharge-based termination reduces the mean endpoint by 14.8, 15.8 and 18.3 per cent across the three loads. Both timing sweeps favour their shortest tested setting, so neither identifies an optimum. This exploratory study uses synthetic demand, negligible start-up lost time and physical removal of persistently stationary vehicles. The results support testing timing sensitivity before attributing performance to a controller family; they do not establish a setting for real-world deployment.
Comments13 pages, 1 figure, 12 tables. Code, network and experiment configurations at https://github.com/nitaiaharoni1/traffic-simulator
