导航系统可能会降低交通网络的稳定性
Navigation Systems May Deteriorate Stability in Traffic Networks
AI总结：
该研究提出驾驶员模仿他人路径偏好的动态交通模型，发现模仿学习可提升系统承载量，但延误函数斜率或模仿速率不当会导致系统失稳，揭示了导航普及对交通网络稳定性的潜在负面影响。
摘要：
先进交通导航系统可基于实时拥堵信息为驾驶员提供路线推荐，如今已被道路运输用户广泛采用。然而，截至目前，这类工具的普及对交通动态产生的新兴影响在很大程度上仍未得到探究。本文提出了一种动态模型，其中驾驶员会模仿先前驾驶员的路径偏好，并研究了该模型平衡点的特性。我们的模型是经典交通分配框架的动态泛化版本，通过同时纳入路径决策过程与网络交通流的动态性对其进行了扩展。研究表明，当出行者通过模仿其他出行者来学习最短路径时，整体交通系统会从这一机制中受益，能够承载最大可容许的交通需求。另一方面，我们证明，当出行延误函数的斜率不足，或驾驶员模仿先前驾驶员的速率选择不当时，交通系统的轨迹可能无法收敛到平衡点，从而不具备渐近稳定性。结合高速公路传感器实测数据的说明性数值仿真验证了我们的研究发现。
英文摘要：
Advanced traffic navigation systems, which provide routing recommendations to drivers based on real-time congestion information, are nowadays widely adopted by roadway transportation users. Yet, the emerging effects on the traffic dynamics originating from the widespread adoption of these tools have remained largely unexplored until now. In this paper, we propose a dynamic model where drivers imitate the path preferences of previous drivers, and we study the properties of its equilibrium points. Our model is a dynamic generalization of the classical traffic assignment framework, and extends it by accounting for dynamics both in the path decision process and in the network's traffic flows. We show that when travelers learn shortest paths by imitating other travelers, the overall traffic system benefits from this mechanism and transfers the maximum admissible amount of traffic demand. On the other hand, we demonstrate that when the travel delay functions are not sufficiently steep or the rates at which drivers imitate previous travelers are not adequately chosen, the trajectories of the traffic system may fail to converge to an equilibrium point, thus failing asymptotic stability. Illustrative numerical simulations combined with empirical data from highway sensors illustrate our findings.
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。
