摘要翻译：
为了帮助缓解因交通需求不断增长而造成的道路拥堵，许多交通当局实施了管理车道政策。管理车道通常与高速公路的标准通用(GP)车道平行，但仅限于某些类型的车辆。人们最初认为，管理车道将通过激励拼车等需求管理行为来改善现有基础设施的使用，但实施的特点往往是无法预测的现象，这些现象往往会损害系统的性能。本文介绍了几种宏观交通建模工具，用于研究设置管理车道的高速公路，或“管理车道-高速公路网络”。所提出的框架是基于广泛应用的一阶运动波理论。在该模型中，GP和被管理车道被建模为由节点连接的并行链路，其中特定类型的交通可以在GP和被管理车道链路之间切换。考虑了两种类型的管理车道拓扑：全接入，车辆可以在任何地方在GP和管理车道之间切换；并且是分开的，这样的交换只允许在被称为门的特定位置进行。我们还描述了将三种现象纳入我们的模型的方法，这三种现象是管理车道-高速公路网络特有的。惯性效应反映了司机倾向于尽可能长时间地停留在车道上，只有在明显改善行车条件的情况下才会切换。摩擦效应反映了经验观察到的司机害怕在管理车道上快速行驶，而邻近的GP车道上的交通因拥堵而缓慢行驶。平滑效应描述了管理车道如何通过减少车道变更来增加瓶颈处的吞吐量。我们给出了这些现象中每一种的简单模型，这些模型都符合一般的宏观理论。
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英文标题：
《Macroscopic Modeling, Calibration, and Simulation of Managed
  Lane-Freeway Networks, Part I: Topological and Phenomenological Modeling》
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作者：
Matthew A. Wright, Roberto Horowitz, Alex A. Kurzhanskiy
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最新提交年份：
2019
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Systems and Control        系统与控制
分类描述：cs.SY is an alias for eess.SY. This section includes theoretical and experimental research covering all facets of automatic control systems. The section is focused on methods of control system analysis and design using tools of modeling, simulation and optimization. Specific areas of research include nonlinear, distributed, adaptive, stochastic and robust control in addition to hybrid and discrete event systems. Application areas include automotive and aerospace control systems, network control, biological systems, multiagent and cooperative control, robotics, reinforcement learning, sensor networks, control of cyber-physical and energy-related systems, and control of computing systems.
cs.sy是eess.sy的别名。本部分包括理论和实验研究，涵盖了自动控制系统的各个方面。本节主要介绍利用建模、仿真和优化工具进行控制系统分析和设计的方法。具体研究领域包括非线性、分布式、自适应、随机和鲁棒控制，以及混合和离散事件系统。应用领域包括汽车和航空航天控制系统、网络控制、生物系统、多智能体和协作控制、机器人学、强化学习、传感器网络、信息物理和能源相关系统的控制以及计算系统的控制。
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Systems and Control        系统与控制
分类描述：This section includes theoretical and experimental research covering all facets of automatic control systems. The section is focused on methods of control system analysis and design using tools of modeling, simulation and optimization. Specific areas of research include nonlinear, distributed, adaptive, stochastic and robust control in addition to hybrid and discrete event systems. Application areas include automotive and aerospace control systems, network control, biological systems, multiagent and cooperative control, robotics, reinforcement learning, sensor networks, control of cyber-physical and energy-related systems, and control of computing systems.
本部分包括理论和实验研究，涵盖了自动控制系统的各个方面。本节主要介绍利用建模、仿真和优化工具进行控制系统分析和设计的方法。具体研究领域包括非线性、分布式、自适应、随机和鲁棒控制，以及混合和离散事件系统。应用领域包括汽车和航空航天控制系统、网络控制、生物系统、多智能体和协作控制、机器人学、强化学习、传感器网络、信息物理和能源相关系统的控制以及计算系统的控制。
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一级分类：Physics        物理学
二级分类：Cellular Automata and Lattice Gases        元胞自动机与格子气体
分类描述：Computational methods, time series analysis, signal processing, wavelets, lattice gases
计算方法，时间序列分析，信号处理，小波，格子气体
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英文摘要：
  To help mitigate road congestion caused by the unrelenting growth of traffic demand, many transit authorities have implemented managed lane policies. Managed lanes typically run parallel to a freeway's standard, general-purpose (GP) lanes, but are restricted to certain types of vehicles. It was originally thought that managed lanes would improve the use of existing infrastructure through incentivization of demand-management behaviors like carpooling, but implementations have often been characterized by unpredicted phenomena that is often to detrimental system performance.   This paper presents several macroscopic traffic modeling tools we have used for study of freeways equipped with managed lanes, or "managed lane-freeway networks." The proposed framework is based on the widely-used first-order kinematic wave theory. In this model, the GP and the managed lanes are modeled as parallel links connected by nodes, where certain type of traffic may switch between GP and managed lane links. Two types of managed lane topologies are considered: full-access, where vehicles can switch between the GP and the managed lanes anywhere; and separated, where such switching is allowed only at certain locations called gates.   We also describe methods to incorporate in three phenomena into our model that are particular to managed lane-freeway networks. The inertia effect reflects drivers' inclination to stay in their lane as long as possible and switch only if this would obviously improve their travel condition. The friction effect reflects the empirically-observed driver fear of moving fast in a managed lane while traffic in the adjacent GP lanes moves slowly due to congestion. The smoothing effect describes how managed lanes can increase throughput at bottlenecks by reducing lane changes. We present simple models for each of these phenomena that fit within the general macroscopic theory.
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PDF链接：
https://arxiv.org/pdf/1609.0947