Analysis of the impact of COVID-19 on collisions, fatalities and injuries using time series forecasting: The case of Greece

[1]  G. Yannis,et al.  Identifying the impact of the COVID-19 pandemic on driving behavior using naturalistic driving data and time series forecasting , 2021, Journal of Safety Research.

[2]  K. Al-Sahili,et al.  Practical Minimum Sample Size for Road Crash Time-Series Prediction Models , 2020, Advances in Civil Engineering.

[3]  Rob J Hyndman,et al.  Forecasting Functions for Time Series and Linear Models [R package forecast version 8.13] , 2020 .

[4]  Xiaotong Wang,et al.  Epidemiological characteristics of dengue in mainland China from 1990 to 2019 , 2020, Medicine.

[5]  G. Yannis,et al.  A descriptive analysis of the effect of the COVID-19 pandemic on driving behavior and road safety , 2020, Transportation Research Interdisciplinary Perspectives.

[6]  S. Abu-Eisheh,et al.  Analysis and Modeling of Road Crash Trends in Palestine , 2020, Arabian Journal for Science and Engineering.

[7]  Despina Stavrinos,et al.  Adolescent driving behavior before and during restrictions related to COVID-19 , 2020, Accident Analysis & Prevention.

[8]  P. Bajardi,et al.  COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown , 2020, Scientific Data.

[9]  P. Bucsky.  Modal share changes due to COVID-19: The case of Budapest , 2020, Transportation Research Interdisciplinary Perspectives.

[10]  Mathijs de Haas,et al.  How COVID-19 and the Dutch ‘intelligent lockdown’ change activities, work and travel behaviour: Evidence from longitudinal data in the Netherlands , 2020, Transportation Research Interdisciplinary Perspectives.

[11]  H. Miller,et al.  Evidence of Increased Vehicle Speeding in Ohio’s Major Cities during the COVID-19 Pandemic , 2020, Findings.

[12]  A. Brodeur,et al.  On the effects of COVID-19 safer-at-home policies on social distancing, car crashes and pollution , 2020, Journal of Environmental Economics and Management.

[13]  Rubén Cordera,et al.  Effects of the COVID-19 Lockdown on Urban Mobility: Empirical Evidence from the City of Santander (Spain) , 2020, Sustainability.

[14]  G. Christey,et al.  Variation in volumes and characteristics of trauma patients admitted to a level one trauma centre during national level 4 lockdown for COVID-19 in New Zealand. , 2020, The New Zealand medical journal.

[15]  Royce J. Wilson,et al.  Google COVID-19 Community Mobility Reports: Anonymization Process Description (version 1.0) , 2020, ArXiv.

[16]  N. Dey.  COVID-19 Outbreak , 2020, Data Analytics for Pandemics.

[17]  Zhènglì Shí,et al.  An emerging coronavirus causing pneumonia outbreak in Wuhan, China: calling for developing therapeutic and prophylactic strategies , 2020, Emerging microbes & infections.

[18]  Hing Kai Chan,et al.  Forecasting the demand of the aviation industry using hybrid time series SARIMA-SVR approach , 2019, Transportation Research Part E: Logistics and Transportation Review.

[19]  Mercedes Castro-Nuño,et al.  The complex relationship between increases to speed limits and traffic fatalities: Evidence from a meta-analysis , 2019, Safety Science.

[20]  Fotios Petropoulos,et al.  forecast: Forecasting functions for time series and linear models , 2018 .

[21]  J. Kihoro,et al.  Application of Vector Autoregressive (VAR) Process in Modelling Reshaped Seasonal Univariate Time Series , 2015 .

[22]  Yung-Ming Shiu,et al.  Vehicle insurance and the risk of road traffic accidents , 2015 .

[23]  Rebecca B Naumann,et al.  The impact of alcohol and road traffic policies on crash rates in Botswana, 2004-2011: a time-series analysis. , 2014, Accident; analysis and prevention.

[24]  George Athanasopoulos,et al.  Forecasting: principles and practice , 2013 .

[25]  Rajesh Krishnan,et al.  A computationally efficient two-stage method for short-term traffic prediction on urban roads , 2013 .

[26]  Murat Kulahci,et al.  Time Series Analysis and Forecasting by Example , 2011 .

[27]  Rob J Hyndman,et al.  Automatic Time Series Forecasting: The forecast Package for R , 2008 .

[28]  Rune Elvik,et al.  Speed and road accidents : an evaluation of the power model , 2004 .

[29]  Abdul Kareem,et al.  Review of global menace of road accidents with special reference to malaysia- a social perspective. , 2003, The Malaysian journal of medical sciences : MJMS.

[30]  M. Cropper,et al.  Traffic Fatalities and Economic Growth , 2003, Accident; analysis and prevention.

[31]  Andrew Dickerson,et al.  Road Accidents and Traffic Flows: An Econometric Investigation , 2000 .

[32]  A K Lund,et al.  Changes in motor vehicle occupant fatalities after repeal of the national maximum speed limit. , 1999, Accident; analysis and prevention.

[33]  S Oppe,et al.  Macroscopic models for traffic and traffic safety. , 1988, Accident; analysis and prevention.

[34]  Nigel Meade,et al.  Industrial and business forecasting methods, Lewis, C.D., Borough Green, Sevenoaks, Kent: Butterworth, 1982. Price: £9.25. Pages: 144 , 1983 .

[35]  C. Lewis.  Industrial and business forecasting methods : a practical guide to exponential smoothing and curve fitting , 1982 .

[36]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[37]  H. Akaike.  A new look at the statistical model identification , 1974 .

[38]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[39]  N. Allum,et al.  Learn About Time Series ACF and PACF in Stata With Data From the USDA Feed Grains Database (1876–2015) , 2020 .

[40]  Peter Cummings,et al.  Freeway speed limits and traffic fatalities in Washington State. , 2002, Accident; analysis and prevention.

[41]  Saudi Arabia,et al.  Road accidents in Saudi Arabia: a comparative and analytical study , 1996 .

[42]  D J Victor,et al.  Road accidents in India , 1989 .