Assessment of Crash Occurrence Using Historical Crash Data and a Random Effect Negative Binomial Model: A Case Study for a Rural State
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[1] E. D. Love,et al. A MEASURED ANALYSIS OF THE WHISTLEBLOWER PROVISIONS OF THE 2015 FAST ACT (FIXING AMERICA’S SURFACE TRANSPORTATION ACT) , 2017 .
[2] Sandeep Datla,et al. Impact of cold and snow on temporal and spatial variations of highway traffic volumes , 2008 .
[3] Gudmundur F. Ulfarsson,et al. Model of Relationship between Interstate Crash Occurrence and Geometrics , 2011 .
[4] J. Guldmann,et al. Employment Distribution and Land-Use Structure in the Metropolitan Area of Columbus, Ohio , 2015 .
[5] D. Eisenberg. The mixed effects of precipitation on traffic crashes. , 2004, Accident; analysis and prevention.
[6] D. Eisenberg,et al. Effects of snowfalls on motor vehicle collisions, injuries, and fatalities. , 2005, American journal of public health.
[7] Sudeshna Mitra,et al. Spatial Autocorrelation and Bayesian Spatial Statistical Method for Analyzing Intersections Prone to Injury Crashes , 2009 .
[8] Jennifer T. Wong,et al. Comparison of Methodology Approach to Identify Causal Factors of Accident Severity , 2008 .
[9] Mohamed Abdel-Aty,et al. A Bayesian spatial random parameters Tobit model for analyzing crash rates on roadway segments. , 2017, Accident; analysis and prevention.
[10] Mohsen Jafari,et al. A Data-Driven Approach for Driving Safety Risk Prediction Using Driver Behavior and Roadway Information Data , 2018, IEEE Transactions on Intelligent Transportation Systems.
[11] T. Breurch,et al. A simple test for heteroscedasticity and random coefficient variation (econometrica vol 47 , 1979 .
[12] Lei Zhang,et al. Equitable and progressive distance-based user charges design and evaluation of income-based mileage fees in Maryland , 2016 .
[13] Jian Zhang,et al. Image Encryption Algorithm Based on DNA Encoding and Chaotic Maps , 2014 .
[14] N. Stamatiadis,et al. Identifying high-risk commercial vehicle drivers using sociodemographic characteristics. , 2020, Accident; analysis and prevention.
[15] Meiwu An,et al. Crash Prediction and Risk Evaluation Based on Traffic Analysis Zones , 2014 .
[16] S. Hernandez,et al. A spatial panel regression model to measure the effect of weather events on freight truck traffic , 2020 .
[17] Jeffrey M. Wooldridge,et al. Solutions Manual and Supplementary Materials for Econometric Analysis of Cross Section and Panel Data , 2003 .
[18] Hsin-Li Chang,et al. MODELING THE RELATIONSHIP OF ACCIDENTS TO MILES TRAVELED , 1986 .
[19] Ali Naderan,et al. Crash Generation Models: Forecasting Crashes in Urban Areas , 2010 .
[20] Chandra R. Bhat,et al. Unobserved heterogeneity and the statistical analysis of highway accident data , 2016 .
[21] John Steward,et al. The Impact of Built Environment on Pedestrian Crashes and the Identification of Crash Clusters on an Urban University Campus , 2010, The western journal of emergency medicine.
[22] G. Perrault. Bureau , 2021, La boussole du confiné.
[23] Ida van Schalkwyk,et al. Incorporating Safety into Long-Range Transportation Planning , 2006 .
[24] George Yannis,et al. Review and ranking of crash risk factors related to the road infrastructure. , 2019, Accident; analysis and prevention.
[25] Mohamed Abdel-Aty,et al. Macroscopic spatial analysis of pedestrian and bicycle crashes. , 2012, Accident; analysis and prevention.
[26] Gudmundur F. Ulfarsson,et al. Random parameter models of interstate crash frequencies by severity, number of vehicles involved, collision and location type. , 2013, Accident; analysis and prevention.
[27] Mohamed Abdel-Aty,et al. Bayesian random effect models incorporating real-time weather and traffic data to investigate mountainous freeway hazardous factors. , 2013, Accident; analysis and prevention.
[28] R Kulmala,et al. Measuring the contribution of randomness, exposure, weather, and daylight to the variation in road accident counts. , 1995, Accident; analysis and prevention.
[29] B. Brown,et al. Seasonal Variation in Frequencies and Rates of Highway Accidents as Function of Severity , 1997 .
[30] D. Alex Quistberg,et al. Multilevel models for evaluating the risk of pedestrian-motor vehicle collisions at intersections and mid-blocks. , 2015, Accident; analysis and prevention.
[31] S. Pulugurtha,et al. Examining the influence of network, land use, and demographic characteristics to estimate the number of bicycle-vehicle crashes on urban roads , 2020 .
[32] J. T. Wulu,et al. Regression analysis of count data , 2002 .
[33] R. Stine. Graphical Interpretation of Variance Inflation Factors , 1995 .
[34] P. Yakovlev,et al. Mind the Weather: A Panel Data Analysis of Time-Invariant Factors and Traffic Fatalities , 2010 .
[35] Shubhayu Saha,et al. Adverse weather conditions and fatal motor vehicle crashes in the United States, 1994-2012 , 2016, Environmental Health.
[36] Brian C Tefft. Motor Vehicle Crashes, Injuries, and Deaths in Relation to Weather Conditions,United States, 2010 – 2014 , 2016 .
[37] K. Evenson,et al. Awareness of Vision Zero among United States’ road safety professionals , 2018, Injury Epidemiology.
[38] Weixu Wang,et al. Using Geographically Weighted Poisson Regression for county-level crash modeling in California , 2013 .
[39] Luis F Miranda-Moreno,et al. Quantifying safety benefit of winter road maintenance: accident frequency modeling. , 2010, Accident; analysis and prevention.
[40] Z. Griliches,et al. Econometric Models for Count Data with an Application to the Patents-R&D Relationship , 1984 .
[41] Mohamed M. Ahmed,et al. Assessment of Interaction of Crash Occurrence, Mountainous Freeway Geometry, Real-Time Weather, and Traffic Data , 2012 .
[42] Ahmet Tortum,et al. Accident analysis with aggregated data: the random parameters negative binomial panel count data model , 2015 .
[43] Fred L Mannering,et al. A note on modeling vehicle accident frequencies with random-parameters count models. , 2009, Accident; analysis and prevention.
[44] Pengpeng Xu,et al. Revisiting crash spatial heterogeneity: A Bayesian spatially varying coefficients approach. , 2017, Accident; analysis and prevention.
[45] Y. Zou,et al. A semi-nonparametric Poisson regression model for analyzing motor vehicle crash data , 2018, PloS one.
[46] Yongdoo Lee,et al. Statistical modeling of total crash frequency at highway intersections , 2016 .
[47] M. Abdel-Aty,et al. A correlated random parameter approach to investigate the effects of weather conditions on crash risk for a mountainous freeway , 2014 .
[48] Ali Pirdavani,et al. Socioeconomic and sociodemographic inequalities and their association with road traffic injuries , 2017 .
[49] Srinivas S Pulugurtha,et al. Traffic analysis zone level crash estimation models based on land use characteristics. , 2013, Accident; analysis and prevention.
[50] Feng Guo,et al. Driver crash risk factors and prevalence evaluation using naturalistic driving data , 2016, Proceedings of the National Academy of Sciences.
[51] C. Prasad,et al. TRAFFIC ANALYSIS ZONE LEVEL ROAD TRAFFIC ACCIDENT PREDICTION MODELS BASED ON LAND USE CHARACTERISTICS , 2019, INTERNATIONAL JOURNAL FOR TRAFFIC AND TRANSPORT ENGINEERING.
[52] P. Betaubun,et al. Modeling factor as the cause of traffic accident losses using multiple linear regression approach and generalized linear models , 2019, IOP Conference Series: Earth and Environmental Science.
[53] Michael E Rakauskas,et al. Identification of differences between rural and urban safety cultures. , 2009, Accident; analysis and prevention.
[54] M. Quddus. Modelling area-wide count outcomes with spatial correlation and heterogeneity: an analysis of London crash data. , 2008, Accident; analysis and prevention.
[55] Bhagwant Persaud,et al. Safety Prediction Models , 2007 .
[56] Ghulam H Bham,et al. Crash Frequency Modeling using Negative Binomial Models: An Application of Generalized Estimating Equation to Longitudinal Data , 2014 .
[57] Lin Liu,et al. Developing a New Spatial Unit for Macroscopic Safety Evaluation Based on Traffic Density Homogeneity , 2020 .