Trends in deaths from road injuries during the COVID-19 pandemic in Japan, January to September 2020

Abstract Background In Japan, the latest estimates of excess all-cause deaths through January to July 2020 showed that the overall (direct and indirect) mortality burden from the Coronavirus Disease 2019 (COVID-19) in Japan was relatively low compared to Europe and the United States. However, consistency between the reported number of COVID-19 deaths and excess all-cause deaths was limited across prefectures, suggesting the necessity of distinguishing the direct and indirect consequences of COVID-19 by cause-specific analysis. To examine whether deaths from road injuries decreased during the COVID-19 pandemic in Japan, consistent with a possible reduction of road transport activity connected to Japan’s state of emergency declaration, we estimated the exiguous deaths from road injuries in each week from January to September 2020 by 47 prefectures. Methods To estimate the expected weekly number of deaths from road injuries, a quasi-Poisson regression was applied to daily traffic fatalities data obtained from Traffic Accident Research and Data Analysis, Japan. We set two thresholds, point estimate and lower bound of the two-sided 95% prediction interval, for exiguous deaths, and report the range of differences between the observed number of deaths and each of these thresholds as exiguous deaths. Results Since January 2020, in a few weeks the observed deaths from road injuries fell below the 95% lower bound, such as April 6–12 (exiguous deaths 5–21, percent deficit 2.82–38.14), May 4–10 (8–23, 21.05–43.01), July 20–26 (12–29, 30.77–51.53), and August 3–9 (3–20, 7.32–34.41). However, those less than the 95% lower bound were also observed in weeks in the previous years. Conclusions The number of road traffic fatalities during the COVID-19 pandemic in Japan has decreased slightly, but not significantly, in several weeks compared with the average year. This suggests that the relatively small changes in excess all-cause mortality observed in Japan during the COVID-19 pandemic could not be explained simply by an offsetting reduction in traffic deaths. Considering a variety of other indirect effects, evaluating an independent, unbiased measure of COVID-19-related mortality burden could provide insight into the design of future broad-based infectious disease counter-measures and offer lessons to other countries.

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SafeRoadMaps: Communication of Location and Density of Traffic Fatalities through Spatial Visualization and Heat Map Analysis

Information Visualization ◽

10.1057/ivs.2010.14 ◽

2010 ◽

Vol 10 (1) ◽

pp. 82-96 ◽

Cited By ~ 9

Author(s):

Brian N. Hilton ◽

Thomas A. Horan ◽

Richard Burkhard ◽

Benjamin Schooley

Keyword(s):

Motor Vehicle ◽

Road Traffic ◽

Safety Information ◽

Geographic Location ◽

The United States ◽

Transportation Safety ◽

Spatial Density ◽

Traffic Fatalities ◽

Road Transportation ◽

Rural And Urban

Road traffic injuries are the number one, non-disease-related, cause of death in the world; more than 1.2 million people die each year on the roads, and between 20 and 50 million sustain non-fatal injuries. In 2008, in the United States, there were 37 261 motor vehicle fatalities – the result of 34 017 motor vehicle crashes. Clearly, there is an urgent need for governmental agencies, and other key institutions, to increase and sustain action to prevent motor vehicle injuries. This article reports on the iterative development of SafeRoadMaps, a publicly accessible system for presenting accident frequencies and characteristics based on geographic location ( www.saferoadmaps.org ). The system was developed to visually communicate and allow analysis of public health issues related to rural and urban road transportation safety. One of the distinctive features of this online system is the use of ‘heat maps’ as a visual means for communicating the spatial density of traffic fatalities. The article begins with a review of the action research design approach utilized for the analysis, design and implementation of this system, continues with an overview of the system and its visualization methods to communicate safety information to travelers and other stakeholders, and concludes with a summary of findings from end-user feedback, including the system's potential to raise user awareness and affect driving behavior.

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ROAD TRAFFIC FATALITIES AND ACCIDENT RISK IN UTTARAKHAND STATE OF INDIA: A GEOGRAPHICAL ANALYSIS

ANNALS OF THE NATIONAL ASSOCIATION OF GEOGRAPHERS INDIA ◽

10.32381/atnagi.2020.40.02.3 ◽

2020 ◽

Vol 40 (2) ◽

pp. 234-247

Author(s):

Santosh Kumari ◽

◽

D.D SHARMA ◽

VIRENDER SINGH ◽

◽

...

Keyword(s):

Road Traffic ◽

Accident Risk ◽

Traffic Fatalities ◽

Geographical Analysis

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Road Traffic Fatalities by Type 2013

Wolfram Research Data Repository ◽

10.24097/wolfram.48469.data ◽

2017 ◽

Author(s):

Keyword(s):

Road Traffic ◽

Traffic Fatalities

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Acoustic Modeling of Meteorological Effects on Roadway Noise

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198121994584 ◽

2021 ◽

pp. 036119812199458

Author(s):

Roger L. Wayson ◽

Kenneth Kaliski

Keyword(s):

Road Traffic ◽

Traffic Noise ◽

Model Development ◽

The United States ◽

Noise Model ◽

Road Traffic Noise ◽

Atmospheric Conditions ◽

Noise Propagation ◽

Transportation Research ◽

Meteorological Effects

Modeling road traffic noise levels without including the effects of meteorology may lead to substantial errors. In the United States, the required model is the Traffic Noise Model which does not include meteorology effects caused by refraction. In response, the Transportation Research Board sponsored NCHRP 25-52, Meteorological Effects on Roadway Noise, to collect highway noise data under different meteorological conditions, document the meteorological effects on roadway noise propagation under different atmospheric conditions, develop best practices, and provide guidance on how to: (a) quantify meteorological effects on roadway noise propagation; and (b) explain those effects to the public. The completed project at 16 barrier and no-barrier measurement positions adjacent to Interstate 17 (I-17) in Phoenix, Arizona provided the database which has enabled substantial developments in modeling. This report provides more recent information on the model development that can be directly applied by the noise analyst to include meteorological effects from simple look-up tables to more precise use of statistical equations.

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Probabilistic Prediction of Separation Buffer to Compensate for the Closing Effect on Final Approach

Aerospace ◽

10.3390/aerospace8020029 ◽

2021 ◽

Vol 8 (2) ◽

pp. 29

Author(s):

Stanley Förster ◽

Michael Schultz ◽

Hartmut Fricke

Keyword(s):

Quantile Regression ◽

Prediction Interval ◽

Quantile Function ◽

Air Traffic ◽

Point Estimate ◽

Probabilistic Prediction ◽

Traffic System ◽

Final Approach ◽

Model Training ◽

Prediction Approach

The air traffic is mainly divided into en-route flight segments, arrival and departure segments inside the terminal maneuvering area, and ground operations at the airport. To support utilizing available capacity more efficiently, in our contribution we focus on the prediction of arrival procedures, in particular, the time-to-fly from the turn onto the final approach course to the threshold. The predictions are then used to determine advice for the controller regarding time-to-lose or time-to-gain for optimizing the separation within a sequence of aircraft. Most prediction methods developed so far provide only a point estimate for the time-to-fly. Complementary, we see the need to further account for the uncertain nature of aircraft movement based on a probabilistic prediction approach. This becomes very important in cases where the air traffic system is operated at its limits to prevent safety-critical incidents, e.g., separation infringements due to very tight separation. Our approach is based on the Quantile Regression Forest technique that can provide a measure of uncertainty of the prediction not only in form of a prediction interval but also by generating a probability distribution over the dependent variable. While the data preparation, model training, and tuning steps are identical to classic Random Forest methods, in the prediction phase, Quantile Regression Forests provide a quantile function to express the uncertainty of the prediction. After developing the model, we further investigate the interpretation of the results and provide a way for deriving advice to the controller from it. With this contribution, there is now a tool available that allows a more sophisticated prediction of time-to-fly, depending on the specific needs of the use case and which helps to separate arriving aircraft more efficiently.

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Military Coup in Burma Draws International Condemnation and Pressure

The American Journal of International Law ◽

10.1017/ajil.2021.31 ◽

2021 ◽

Vol 115 (3) ◽

pp. 558-567

Keyword(s):

United States ◽

Social Media ◽

International Organizations ◽

The United States ◽

Military Coup ◽

State Of Emergency ◽

Media Companies ◽

One Year ◽

Head Of Government ◽

The Military

On February 1, 2021, the military in Burma overthrew the democratically elected government, declared a one-year state of emergency, and installed Senior General Min Aung Hlaing as the head of government. Since the coup, the military has cracked down on protestors, killing over 800 people and detaining many more. Numerous countries and international organizations, including the United States and the United Nations, have condemned the coup and ensuing violence and called for the restoration of a democratic government. The United States and other countries have also imposed rigorous sanctions on the Burmese military, its officials and affiliated corporations, and social media companies have imposed content restrictions to prevent the spread of pro-military propaganda.

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