The COVID-19 outbreak and ensuing social distancing behaviors resulted in substantial reduction on traffic, making this a unique experiment on observing the air quality. Such an experiment is also supplemental to the smart city concept as it can help to identify whether there is a delay on air quality improvement during or after a sharp decline on traffic and to determine what, if any, factors are contributing to that time lag. As such, this study investigates the immediate impacts of COVID-19 causing abrupt declines on traffic and NO2 concentration in all Florida Counties through March 2020. Daily tropospheric NO2 concentrations were extracted from the Sentinel-5 Precursor satellite and vehicle mile traveled (VMT) estimates were acquired from cell phone mobility records. It is observed that overall impacts of the COVID-19 response in Florida have started in the first half of the March 2020, two weeks earlier than the official stay-at-home orders, and resulted in 54.07% and 59.68% decrease by the end of the month on NO2 and VMT, respectively. Further, a cross-correlation based dependency analysis was conducted to analyze the similarities and associated time lag between 7-day moving averages of VMT and NO2 concentrations of the 67 counties. Although such reduction is unprecedented for both data sets, results indicate a strong correlation and this correlation increases with the identification of a time lag between VMT and NO2 concentration. Majority of the counties have no time lag between VMT and NO2 concentration; however, a cluster of South Florida counties presents earlier decrease on NO2 concentration compare to VMT, which indicates that the air quality improvements in those counties are not traffic related. Investigation on the socioeconomic factors indicates that population density and income level have no significant impact on the time lag between traffic and air quality improvements in light of COVID-19.
For almost a century, several models have been developed to calibrate the pairwise relationship between traffic flow variables, that is, speed, density, and flow. Multi-regime models are well known for being superior over single-regime models in fitting the speed–density relationship. However, in modeling multi-regime models, breakpoints that separate the regimes are visually established based on the subjective judgment of data characteristics. Thus, this study proposes a data-driven approach to estimate the breakpoints of multi-regime models. It applies the Bayesian model for calibrating multi-regime models (two and three-regime models) for fitting traffic flow fundamental diagram. Furthermore, the analysis presented accounts for the random characteristics associated with the flow. To demonstrate the application of the proposed algorithm, traffic flow data from Interstate 10 (I-10) freeway in Jacksonville, Florida, were used in the analysis. The results demonstrate the potential benefit of using the proposed model in calibrating the fundamental diagram. The proposed approach can also quantify uncertainty and encode prior knowledge about the breakpoints in the model if the model developer wishes.
During extreme weather events like hurricanes, trees can cause significant challenges for the local communities with roadway closures or power outages. Local responders must act quickly with information regarding the extent and severity of hurricane damage to better manage recovery procedures following natural disasters. This paper proposes an approach to automatically identify fallen trees on roadways using highresolution satellite imagery before and after a hurricane. The approach detects fallen trees on roadways via a co-voting strategy of three different algorithms and tailored dissimilarity scores. The proposed method does not rely on the large manually labeled satellite image data, making it more practical than existing approaches. Our solution has been implemented and validated on an actual roadway closure dataset from Hurricane Michael in Tallahassee, Florida, in October 2018.
Hurricanes affect thousands of people annually, with devastating consequences such as loss of life, vegetation and infrastructure. Vegetation losses such as downed trees and infrastructure disruptions such as toppled power lines often lead to roadway closures. These disruptions can be life threatening for the victims. Emergency officials, therefore, have been trying to find ways to alleviate such problems by identifying those locations that pose high risk in the aftermath of hurricanes. This paper proposes an integrated methodology that utilizes both Google Earth Engine (GEE) and geographical information systems (GIS). First, GEE is used to access Sentinel-2 satellite images and calculate the Normalized Difference Vegetation Index (NDVI) to investigate the vegetation change as a result of Hurricane Michael in the City of Tallahassee. Second, through the use of ArcGIS, data on wind speed, debris, roadway density and demographics are incorporated into the methodology in addition to the NDVI indices to assess the overall impact of the hurricane. As a result, city-wide hurricane impact maps are created using weighted indices created based on all these data sets. Findings indicate that the northeast side of the city was the worst affected because of the hurricane. This is a region where more seniors live, and such disruptions can lead to dramatic consequences because of the fragility of these seniors. Officials can pinpoint the identified critical locations for future improvements such as roadway geometry modification and landscaping justification.
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