测量自组织交通灯的复杂性
Measuring the Complexity of Self-organizing Traffic Lights
AI总结：
本文通过复杂性、涌现和自组织度衡量方法，比较传统与自组织交通协调方法在环形和非定向边界场景下的动态相变特性，揭示交通调控需根据需求动态调整复杂度。
摘要：
我们应用复杂性、涌现和自组织度的度量方法，对抽象城市交通模型进行分析，以比较传统交通协调方法与自组织方法在环形边界和非定向边界两种场景下的表现。我们展示了这些度量方法有助于识别和表征不同的动态相变。可以看出，不同的交通需求需要不同的操作模式。因此，交通不仅是一个非平稳问题，要求控制器不断适应。控制器还必须根据需求大幅改变其行为的复杂度。基于我们的度量，可以得出自组织方法的适应性水平与生物系统相当。
英文摘要：
We apply measures of complexity, emergence and self-organization to an abstract city traffic model for comparing a traditional traffic coordination method with a self-organizing method in two scenarios: cyclic boundaries and non-orientable boundaries. We show that the measures are useful to identify and characterize different dynamical phases. It becomes clear that different operation regimes are required for different traffic demands. Thus, not only traffic is a non-stationary problem, which requires controllers to adapt constantly. Controllers must also change drastically the complexity of their behavior depending on the demand. Based on our measures, we can say that the self-organizing method achieves an adaptability level comparable to a living system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
