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摘要翻译：
新冠肺炎疫情影响了旅行行为和交通系统的运营，城市正在努力解决什么样的政策对社交距离形成的分阶段重新开放有效。以前为前Covid条件开发和校准了一个基线模型，称为MATSIM-NYC。通过重新校准人口议程以包括在家工作，并重新估计MATSim-NYC的模式选择模型以适应观察到的交通和运输乘客数据，校准了一个新的COVID模型，该模型代表了新冠肺炎疫情期间的旅行行为。假设在重新开放期间行为的变化表现出惯性，我们分析了由于纽约州政府指导的分阶段重新开放计划而导致的汽车流量的增加。分析了四个重新开放阶段和两种重新开放情景（有和无运输能力限制）。以100%的运输能力重新开放的第四阶段可能只会看到高达73%的Covid前乘客量，以及高达142%的流行病前水平的汽车出行量。将交通能力限制在50%将使交通乘客人数从73%进一步减少到64%，同时将汽车出行增加到疫情前水平的143%。虽然增长似乎很小，但由于交通拥堵已经加剧，对消费者剩余的影响不成比例地大。许多旅行也被转移到其他模式，如微移动。研究结果表明，重新开放期间的交通容量限制政策需要伴随着（1）支持微移动模式，特别是在非曼哈顿区，(2)侧重于减少曼哈顿交通的缓解拥堵政策，如基于警戒线的定价。
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英文标题：
《Impact of COVID-19 behavioral inertia on reopening strategies for New
York City Transit》
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作者：
Ding Wang, Brian Yueshuai He, Jingqin Gao, Joseph Y. J. Chow, Kaan
Ozbay, Shri Iyer
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最新提交年份：
2021
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分类信息：
一级分类：Economics 经济学
二级分类：General Economics 一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Computer Science 计算机科学
二级分类：Multiagent Systems 多智能体系统
分类描述：Covers multiagent systems, distributed artificial intelligence, intelligent agents, coordinated interactions. and practical applications. Roughly covers ACM Subject Class I.2.11.
涵盖多Agent系统、分布式人工智能、智能Agent、协调交互。和实际应用。大致涵盖ACM科目I.2.11类。
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一级分类：Physics 物理学
二级分类：Physics and Society 物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
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一级分类：Quantitative Finance 数量金融学
二级分类：Economics 经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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英文摘要：
The COVID-19 pandemic has affected travel behaviors and transportation system operations, and cities are grappling with what policies can be effective for a phased reopening shaped by social distancing. A baseline model was previously developed and calibrated for pre-COVID conditions as MATSim-NYC. A new COVID model is calibrated that represents travel behavior during the COVID-19 pandemic by recalibrating the population agendas to include work-from-home and re-estimating the mode choice model for MATSim-NYC to fit observed traffic and transit ridership data. Assuming the change in behavior exhibits inertia during reopening, we analyze the increase in car traffic due to the phased reopen plan guided by the state government of New York. Four reopening phases and two reopening scenarios (with and without transit capacity restrictions) are analyzed. A Phase 4 reopening with 100% transit capacity may only see as much as 73% of pre-COVID ridership and an increase in the number of car trips by as much as 142% of pre-pandemic levels. Limiting transit capacity to 50% would decrease transit ridership further from 73% to 64% while increasing car trips to as much as 143% of pre-pandemic levels. While the increase appears small, the impact on consumer surplus is disproportionately large due to already increased traffic congestion. Many of the trips also get shifted to other modes like micromobility. The findings imply that a transit capacity restriction policy during reopening needs to be accompanied by (1) support for micromobility modes, particularly in non-Manhattan boroughs, and (2) congestion alleviation policies that focus on reducing traffic in Manhattan, such as cordon-based pricing.
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PDF链接：
https://arxiv.org/pdf/2006.13368
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