Data-Driven Modeling Reveals the Impact of Stay-at-Home Orders on Human Mobility during the COVID-19 Pandemic in the U.S

Xiong, Chenfeng; Hu, Songhua; Yang, Mofeng; Younes, Hannah; Luo, Weiyu; Ghader, Sepehr; Zhang, Lei

doi:10.48550/arxiv.2005.00667

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…Moreover, The U.S. government proclaimed a national state of emergency on March 13 th , 2020. And by April 10 th , only 8 states had not issued "Stay-at-home" orders(Xiong et al 2020).…”

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confidence: 99%

Modeling indoor-level non-pharmaceutical interventions during the COVID-19 pandemic: A pedestrian dynamics-based microscopic simulation approach

et al. 2021

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Mathematical modeling of epidemic spreading has been widely adopted to estimate the threats of epidemic diseases (i.e., the COVID-19 pandemic) as well as to evaluate epidemic control interventions. The indoor place is considered to be a significant epidemic spreading risk origin, but existing widely-used epidemic spreading models are usually limited for indoor places since the dynamic physical distance changes between people are ignored, and the empirical features of the essential and non-essential travel are not differentiated. In this paper, we introduce a pedestrian-based epidemic spreading model that is capable of modeling indoor transmission risks of diseases during people’s social activities. Taking advantage of the before-and-after mobility data from the University of Maryland COVID-19 Impact Analysis Platform, it’s found that people tend to spend more time in grocery stores once their travel frequencies are restricted to a low level. In other words, an increase in dwell time could balance the decrease in travel frequencies and satisfy people’s demands. Based on the pedestrian-based model and the empirical evidence, combined non-pharmaceutical interventions from different operational levels are evaluated. Numerical simulations show that restrictions on people’s travel frequency and open hours of indoor places may not be universally effective in reducing average infection risks for each pedestrian who visit the place. Entry limitations can be a widely effective alternative, whereas the decision-maker needs to balance the decrease in risky contacts and the increase in queue length outside the place that may impede people from fulfilling their travel needs. The results show that a good coordination among the decision-makers can contribute to the improvement of the performance of combined non-pharmaceutical interventions, and it also benefits the short-term and long-term interventions in the future.

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“…Moreover, The U.S. government proclaimed a national state of emergency on March 13 th , 2020. And by April 10 th , only 8 states had not issued "Stay-at-home" orders(Xiong et al 2020).…”

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confidence: 99%

Modeling indoor-level non-pharmaceutical interventions during the COVID-19 pandemic: A pedestrian dynamics-based microscopic simulation approach

et al. 2021

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“…Lots of applications have been developed using the LBS data. For instance, a recent smartphone-enhanced travel survey conducted in the U.S. used a mobile application, rMove developed by Resource Systems Group (RSG), to collect high frequency location data and let respondents recall their trips by showing the trajectories in rMove (RSG 2014;RSG 2015;RSG 2015;RSG 2017); Airsage leveraged LBS data to develop a traffic platform that can estimate traffic flow, speed, congestion and road user sociodemographic for every road and time of day (Airsage 2020); Maryland Transportation Institude (MTI) at the University of Maryland (UMD) developed the COVID-19 Impact Analysis Platform (data.covid.umd.edu) to provide insight on COVID-19's impact on mobility, health, economy and society across the U.S. (Zhang et al 2020;Xiong et al 2020) In summary, these three types of MDLD are different in terms of spatiotemporal coverage of population and its mobility, and LRI. The GPS data has the lowest LRI (usually 1 second), but it usually covers a small percentage of the population, and thus cannot reflect population-level travel behavior without a statistical weighting process.…”

Section: Location-based Service Datamentioning

confidence: 99%

A data-driven travel mode share estimation framework based on mobile device location data

et al. 2021

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Mobile device location data (MDLD) contains abundant travel behavior information to support travel demand analysis. Compared to traditional travel surveys, MDLD has larger spatiotemporal coverage of the population and its mobility. However, ground truth information such as trip origins and destinations, travel modes, and trip purposes are not included by default. Such important attributes must be imputed to maximize the usefulness of the data. This paper targets at studying the capability of MDLD on estimating travel mode share at aggregated levels. A data-driven framework is proposed to extract travel behavior information from MDLD. The proposed framework first identifies trip ends with a modified Spatiotemporal Density-based Spatial Clustering of Applications with Noise algorithm. Then three types of features are extracted for each trip to impute travel modes using machine learning models. A labeled MDLD dataset with ground truth information is used to train the proposed models, resulting in a 95% recall rate in identifying trip ends and over 93% tenfold cross-validation accuracy in imputing the five travel modes (drive, rail, bus, bike and walk) with a random forest (RF) classifier. The proposed framework is then applied to two large-scale MDLD datasets, covering the Baltimore-Washington metropolitan area and the United States, respectively. The estimated trip distance, trip time, trip rate distribution, and travel mode share are compared against travel surveys at different geographies. The results suggest that the proposed framework can be readily applied in different states and metropolitan regions with low cost in order to study multimodal travel demand, understand mobility trends, and support decision making.

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“…Based on data collected from mobile devices, researchers also found that the national emergency declaration and stay-at-home orders encouraged social distancing in the U.S. but violation behavior also existed in some states/counties (21). About 5% of the trip reduction (expressed by the average of daily trips per person and daily average person-miles traveled) was related to the stay-at-home orders (22). The impact of stay-at-home orders reached a ceiling and stopped contributing to the decrease of trip rates or travel miles (23).…”

Section: Human Mobility Patterns During the Early Outbreakmentioning

confidence: 99%

Predicting Grocery Store Visits During the Early Outbreak of COVID-19 with Machine Learning

Bian

Murray‐Tuite

Wolshon

2021

Transportation Research Record: Journal of the Transportation R

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While non-essential travel was canceled during the coronavirus infectious disease (COVID-19) pandemic, grocery shopping was essential. The objectives of this study were to: 1) examine how grocery store visits changed during the early outbreak of COVID-19, and 2) estimate a model to predict the change of grocery store visits in the future, within the same phase of the pandemic. The study period (February 15–May 31, 2020) covered the outbreak and phase-one re-opening. Six counties/states in the United States were examined. Grocery store visits (in-store or curbside pickup) increased over 20% when the national emergency was declared on March 13 and then decreased below the baseline within a week. Grocery store visits on weekends were affected more significantly than those on workdays before late April. Grocery store visits in some states (including California, Louisiana, New York, and Texas) started returning to normal by the end of May, but that was not the case for some of the counties (including those with the cities of Los Angeles and New Orleans). With data from Google Mobility Reports, this study used a long short-term memory network to predict the change of grocery store visits from the baseline in the future. The networks trained with the national data or the county data performed well in predicting the general trend of each county. The results from this study could help understand mobility patterns of grocery store visits during the pandemic and predict the process of returning to normal.

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Data-Driven Modeling Reveals the Impact of Stay-at-Home Orders on Human Mobility during the COVID-19 Pandemic in the U.S

Cited by 5 publications

References 8 publications

Modeling indoor-level non-pharmaceutical interventions during the COVID-19 pandemic: A pedestrian dynamics-based microscopic simulation approach

Modeling indoor-level non-pharmaceutical interventions during the COVID-19 pandemic: A pedestrian dynamics-based microscopic simulation approach

A data-driven travel mode share estimation framework based on mobile device location data

Predicting Grocery Store Visits During the Early Outbreak of COVID-19 with Machine Learning

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