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

2021 ◽  
Author(s):  
Mofeng Yang ◽  
Yixuan Pan ◽  
Aref Darzi ◽  
Sepehr Ghader ◽  
Chenfeng Xiong ◽  
...  
Keyword(s):  
Mobile Device ◽  
Data Driven ◽  
Travel Mode ◽  
Location Data ◽  
Mode Share ◽  
Device Location

scholarly journals A Data-Driven Travel Mode Share Estimation Framework based on Mobile Device Location Data

2021 ◽  
Author(s):  
Mofeng Yang ◽  
Yixuan Pan ◽  
Aref Darzi ◽  
Sepehr Ghader ◽  
Chenfeng Xiong ◽  
...  
Keyword(s):  
Mobile Device ◽  
Travel Behavior ◽  
Travel Demand ◽  
Ground Truth ◽  
Data Driven ◽  
Travel Mode ◽  
Location Data ◽  
Mode Share ◽  
Device Location ◽  
Ground Truth Information

Abstract 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 (ST-DBSCAN) 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 a 93% 10-fold 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.

scholarly journals AN INTERACTIVE COVID-19 MOBILITY IMPACT AND SOCIAL DISTANCING ANALYSIS PLATFORM

2020 ◽  
Author(s):  
Lei Zhang ◽  
Sepehr Ghader ◽  
Michael L. Pack ◽  
Chenfeng Xiong ◽  
Aref Darzi ◽  
...  
Keyword(s):  
Mobile Device ◽  
Research Team ◽  
Impact Analysis ◽  
Population Data ◽  
Public Officials ◽  
Process Data ◽  
Social Distancing ◽  
Location Data ◽  
Analysis Platform ◽  
Device Location

ABSTRACTThe research team has utilized privacy-protected mobile device location data, integrated with COVID-19 case data and census population data, to produce a COVID-19 impact analysis platform that can inform users about the effects of COVID-19 spread and government orders on mobility and social distancing. The platform is being updated daily, to continuously inform decision-makers about the impacts of COVID-19 on their communities using an interactive analytical tool. The research team has processed anonymized mobile device location data to identify trips and produced a set of variables including social distancing index, percentage of people staying at home, visits to work and non-work locations, out-of-town trips, and trip distance. The results are aggregated to county and state levels to protect privacy and scaled to the entire population of each county and state. The research team are making their data and findings, which are updated daily and go back to January 1, 2020, for benchmarking, available to the public in order to help public officials make informed decisions. This paper presents a summary of the platform and describes the methodology used to process data and produce the platform metrics.

scholarly journals Interactive COVID-19 Mobility Impact and Social Distancing Analysis Platform

2021 ◽  
pp. 036119812110438
Author(s):  
Lei Zhang ◽  
Aref Darzi ◽  
Sepehr Ghader ◽  
Michael L. Pack ◽  
Chenfeng Xiong ◽  
...  
Keyword(s):  
Mobile Device ◽  
Research Team ◽  
Impact Analysis ◽  
Population Data ◽  
Public Officials ◽  
Process Data ◽  
Social Distancing ◽  
Location Data ◽  
Analysis Platform ◽  
Device Location

The research team has utilized privacy-protected mobile device location data, integrated with COVID-19 case data and census population data, to produce a COVID-19 impact analysis platform that can inform users about the effects of COVID-19 spread and government orders on mobility and social distancing. The platform is being updated daily, to continuously inform decision-makers about the impacts of COVID-19 on their communities, using an interactive analytical tool. The research team has processed anonymized mobile device location data to identify trips and produced a set of variables, including social distancing index, percentage of people staying at home, visits to work and non-work locations, out-of-town trips, and trip distance. The results are aggregated to county and state levels to protect privacy, and scaled to the entire population of each county and state. The research team is making their data and findings, which are updated daily and go back to January 1, 2020, for benchmarking, available to the public to help public officials make informed decisions. This paper presents a summary of the platform and describes the methodology used to process data and produce the platform metrics.

scholarly journals Impact of close interpersonal contact on COVID-19 incidence: evidence from one year of mobile device data

2021 ◽  
Author(s):  
Forrest W. Crawford ◽  
Sydney A. Jones ◽  
Matthew Cartter ◽  
Samantha G. Dean ◽  
Joshua L. Warren ◽  
...  
Keyword(s):  
Mobile Device ◽  
Close Contact ◽  
Population Level ◽  
First Year ◽  
Contact Rate ◽  
Interpersonal Contact ◽  
Location Data ◽  
Frequency Of Contact ◽  
One Year ◽  
Device Location

AbstractClose contact between people is the primary route for transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19). We sought to quantify interpersonal contact at the population-level by using anonymized mobile device geolocation data. We computed the frequency of contact (within six feet) between people in Connecticut during February 2020 – January 2021. Then we aggregated counts of contact events by area of residence to obtain an estimate of the total intensity of interpersonal contact experienced by residents of each town for each day. When incorporated into a susceptible-exposed-infective-removed (SEIR) model of COVID-19 transmission, the contact rate accurately predicted COVID-19 cases in Connecticut towns during the timespan. The pattern of contact rate in Connecticut explains the large initial wave of infections during March–April, the subsequent drop in cases during June–August, local outbreaks during August–September, broad statewide resurgence during September–December, and decline in January 2021. Contact rate data can help guide public health messaging campaigns to encourage social distancing and in the allocation of testing resources to detect or prevent emerging local outbreaks more quickly than traditional case investigation.One sentence summaryClose interpersonal contact measured using mobile device location data explains dynamics of COVID-19 transmission in Connecticut during the first year of the pandemic.

scholarly journals Data-Driven 3-D Human Body Customization With a Mobile Device

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 27939-27948 ◽  
Author(s):  
Dan Song ◽  
Ruofeng Tong ◽  
Jiang Du ◽  
Yun Zhang ◽  
Yao Jin
Keyword(s):  
Mobile Device ◽  
Human Body ◽  
Data Driven

scholarly journals Social distancing responses to COVID-19 emergency declarations strongly differentiated by income

2020 ◽  
Vol 117 (33) ◽  
pp. 19658-19660 ◽  
Author(s):  
Joakim A. Weill ◽  
Matthieu Stigler ◽  
Olivier Deschenes ◽  
Michael R. Springborn
Keyword(s):  
Severe Acute Respiratory Syndrome ◽  
Mobile Device ◽  
General Pattern ◽  
State Level ◽  
Data Sources ◽  
Health Conditions ◽  
Lower Income ◽  
Double Burden ◽  
Social Distancing ◽  
Device Location

In the absence of a vaccine, social distancing measures are one of the primary tools to reduce the transmission of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, which causes coronavirus disease 2019 (COVID-19). We show that social distancing following US state-level emergency declarations substantially varies by income. Using mobility measures derived from mobile device location pings, we find that wealthier areas decreased mobility significantly more than poorer areas, and this general pattern holds across income quantiles, data sources, and mobility measures. Using an event study design focusing on behavior subsequent to state emergency orders, we document a reversal in the ordering of social distancing by income: Wealthy areas went from most mobile before the pandemic to least mobile, while, for multiple measures, the poorest areas went from least mobile to most. Previous research has shown that lower income communities have higher levels of preexisting health conditions and lower access to healthcare. Combining this with our core finding—that lower income communities exhibit less social distancing—suggests a double burden of the COVID-19 pandemic with stark distributional implications.

scholarly journals Potential for inter-state spread of Covid-19 from Arizona, USA: analysis of mobile device location and commercial flight data

2020 ◽  
Vol 27 (8) ◽  
Author(s):  
Alexander Watts ◽  
Natalie H Au ◽  
Andrea Thomas-Bachli ◽  
Jack Forsyth ◽  
Obadia Mayah ◽  
...  
Keyword(s):  
United States ◽  
Mobile Device ◽  
The United States ◽  
Significant Rise ◽  
Flight Data ◽  
Need To Evaluate ◽  
Commercial Flight ◽  
Device Location ◽  
Inter State

A significant rise of SARS-CoV-2 transmission in Arizona in June 2020 prompted the need to evaluate potential dispersion to other regions in the United States. We evaluate the potential for domestic dissemination of SARS-CoV-2 from Arizona using mobile device-location and scheduled flights data.

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