论文标题
Covid-19对城市规模运输和安全的影响：底特律的早期体验
Impact of COVID-19 on City-Scale Transportation and Safety: An Early Experience from Detroit
论文作者
论文摘要
共同的19日大流行使美国的地方和地区交通网络（尤其是汽车城：底特律）带来了前所未有的破坏水平。这主要是由于迅速限制性措施的结果，例如全州隔离和锁定命令，以限制病毒的传播。这项工作是通过分析与底特律相关的五种类型的现实世界数据集驱动的，从2019年1月到2020年6月，与交通量，每日案例，天气，社交距离索引以及崩溃。主要目标是确定Covid-19对运输网络使用（交通量）和安全性（崩溃）的影响，探索底特律的各种数据以及确定这些多样的数据的影响，并确定各种数据，并确定该数据。体积数据）可能是确认案例预测的有用因素。此外，使用长期短期记忆网络开发了一个深度学习模型，以预测未来一周内确认的情况的数量。该模型证明了一个有希望的预测结果，其确定系数（R^2）约为0.91。此外，为了提供确认的预测的统计评估度量并量化每种数据类型的预测有效性，对六个特征组的预测结果进行了介绍和分析。此外，提出了六个带有支持证据和分析的基本观察。本文的目的是提出一种建议的方法，可以使用针对美国其他大城市获得的类似数据集对Covid-19对运输网络的影响以及对预期的Covid-19案件的影响进行分析，以分析Covid-19对交通网络的影响，以分析提议的方法。
The COVID-19 pandemic brought unprecedented levels of disruption to the local and regional transportation networks throughout the United States, especially the Motor City: Detroit. That was mainly a result of swift restrictive measures such as statewide quarantine and lock-down orders to confine the spread of the virus. This work is driven by analyzing five types of real-world data sets from Detroit related to traffic volume, daily cases, weather, social distancing index, and crashes from January 2019 to June 2020. The primary goal is figuring out the impacts of COVID-19 on the transportation network usage (traffic volume) and safety (crashes) for the Detroit, exploring the potential correlation between these diverse data features, and determining whether each type of data (e.g., traffic volume data) could be a useful factor in the confirmed-cases prediction. In addition, a deep learning model was developed using long short-term memory networks to predict the number of confirmed cases within the next one week. The model demonstrated a promising prediction result with a coefficient of determination (R^2) of up to approximately 0.91. Moreover, in order to provide statistical evaluation measures of confirmed-case prediction and to quantify the prediction effectiveness of each type of data, the prediction results of six feature groups are presented and analyzed. Furthermore, six essential observations with supporting evidence and analyses are presented. The goal of this paper is to present a proposed approach which can be applied, customised, adjusted, and replicated for analysis of the impact of COVID-19 on a transportation network and prediction of the anticipated COVID-19 cases using a similar data set obtained for other large cities in the USA or from around the world.
