Accurately estimating human mobility and gauging its relationship with virus transmission is critical for the control of COVID-19 spreading. Using mobile device location data over 100 million monthly active samples, we compute origin–destination travel demand aggregate inflow at each US county from March 1 to June 9, 2020. Then, quantify change across nation statistically model time-varying between infections. We find that external other counties decreased by 35% soon after entered emergency...

ABSTRACT The research team has utilized privacy-protected mobile device location data, integrated with COVID-19 case data and census population to produce a impact analysis platform that can inform users about the effects of spread government orders on mobility social distancing. is being updated daily, continuously decision-makers impacts their communities using an interactive analytical tool. processed anonymized identify trips produced set variables including distancing index, percentage...

The research team has utilized privacy-protected mobile device location data, integrated with COVID-19 case data and census population to produce a impact analysis platform that can inform users about the effects of spread government orders on mobility social distancing. is being updated daily, continuously decision-makers impacts their communities, using an interactive analytical tool. processed anonymized identify trips produced set variables, including distancing index, percentage people...

One approach to delaying the spread of novel coronavirus (COVID-19) is reduce human travel by imposing restriction policies. Understanding actual mobility response such policies remains a challenge owing lack an observed and large-scale dataset describing during pandemic. This study uses integrated dataset, consisting anonymized privacy-protected location data from over 150 million monthly active samples in USA, COVID-19 case census population information, uncover changes under stay-at-home...

Vehicle volume serves as a critical metric and the fundamental basis for traffic signal control, transportation project prioritization, road maintenance planning, more. Traditional methods of quantifying vehicle rely on manual counting, video cameras, loop detectors at limited number locations. These efforts require significant labor cost expansions. Researchers private sector companies have also explored alternative solutions, such probe data, although this still suffers from low...

To increase traffic mobility and safety, several types of active management (ATM) strategies, such as variable speed limit (VSL), ramp metering, dynamic message signs, hard shoulder running (HSR), are adopted in many countries. While all kinds ATM strategies show promise releasing congestion, studies indicate that stand-alone have very limited capability. remedy the defects cooperative caught researchers’ attention different combinations been studied. In this paper, a coordinated VSL HSR...

Along with the development of information and positioning technologies, there emerges passively collected location data that contain observations time from various types mobile devices. Passive are known for their large sample size continuous behavior observations. However, they also require careful comprehensive processing modeling algorithms privacy protection practical applications. In meantime, travel demand estimation origin–destination (OD) tables is fundamental in transportation...

Real-time traffic flow forecasting is of vital significance to the intelligent transportation system (ITS). Providing real-time and efficient information for travellers, forecast aims reduce travel times, control pressure, pollution. The time-series autoregressive integrated moving average (ARIMA) model has been widely used in advanced management systems. Compared traditional ARIMA model, with exogenous inputs (ARIMAX) can take impact covariates on into account improve comprehensiveness...

One approach to delay the spread of novel coronavirus (COVID-19) is reduce human travel by imposing restriction policies. It yet unclear how effective those policies are on suppressing mobility trend due lack ground truth and large-scale dataset describing during pandemic. This study uses real-world location-based service data collected from anonymized mobile devices uncover changes COVID-19 under 'Stay-at-home' state orders in U.S. The measures with two important metrics: daily average...

Variable speed limit (VSL) control dynamically adjusts the displayed to harmonize traffic speed, prevent congestions, and reduce crash risks based on prevailing stream weather conditions. Previous research studies examine impacts of VSL reducing corridor-level improving bottleneck throughput. However, less attention focuses utilizing real-world data see how compliant drivers are under different values aggregated driving behavior changes. This study aims fill gap. With high-resolution...

This study leverages big location-based service data collected from mobile devices in 2019 to conduct a pedestrian and bicyclist safety analysis. Statistical models are estimated for crash frequency at Maryland intersections using as risk exposure data. The analysis is performed by employing prominent modeling methodologies, including Poisson, negative binomial, zero-inflated binomial regression techniques. findings indicate that inclusion of the improves performance models. Further, results...

Traffic flow models are of vital significance to study the traffic system and reproduce typical phenomena. In process establishing models, human factors need be considered particularly enhance performance models. Accordingly, a series car-following cellular automaton were proposed based on comprehensive consideration various driving behaviors. Based comfortable (CD) model, this paper innovatively an improved model incorporating impaired driver’s radical feature (RF). The was added with...

Variable speed limit (VSL) control is a flexible restriction on the rate at which motorists can drive given stretch of road. Effective VSL increase safety and provide clear guidance for motorists. Previous traffic flow models were mostly based influence average (macro) or driver’s expected (micro). Few considered actual driving behavior. In this paper, we first briefly introduce big data involved in study explain mapping relationship between Then, analyze behavior under control. an improved...

During the outbreak of COVID-19 pandemic, Non-Pharmaceutical and Pharmaceutical treatments were alternative strategies for governments to intervene. Though many these intervention methods proved be effective stop spread COVID-19, i.e., lockdown curfew, they also posed risk economy; in such a scenario, an analysis on how strike balance becomes urgent. Our research leverages mobility big data from University Maryland Impact Analysis Platform employs Generalized Additive Model (GAM), understand...

Mobile device location data (MDLD) have been popularly utilized in various fields. Yet large-scale applications are limited because of either biased or insufficient spatial coverage the from individual vendors. One approach to improve is leverage different vendors and integrate them build a more representative dataset. To extract reliable statistics MDLD, certain preprocessing steps crucial ensure accuracy analysis. these development framework remove duplicated devices several that belong...

Abstract Mobile device location data (MDLD) contains abundant travel behavior information to support demand analysis. Compared traditional surveys, MDLD has larger spatiotemporal coverage of the population and its mobility. However, ground truth such as trip origins destinations, modes, purposes are not included by default. Such important attributes must be imputed maximize usefulness data. This paper targets at studying capability on estimating mode share aggregated levels. A data-driven...