Application of Geographically Weighted Regression Technique in Spatial Analysis of Fatal and Injury Crashes

AbstractGeneralized linear models (GLMs) are the most widely used models utilized in crash prediction studies. These models illustrate the relationships between the dependent and explanatory variables by estimating fixed global estimates. Since crash occurrences are often spatially heterogeneous and are affected by many spatial variables, the existence of spatial correlation in the data is examined by means of calculating Moran’s I measures for dependent and explanatory variables. The results indicate the necessity of considering spatial correlation when developing crash prediction models. The main objective of this research is to develop different zonal crash prediction models (ZCPMs) within the geographically weighted generalized linear model (GWGLM) framework in order to explore the spatial variations in association between number of injury crashes (NOICs) (including fatal, severely, and slightly injured crashes) and other explanatory variables. Different exposure, network, and sociodemographic variabl...

[1]  Tse-Chuan Yang,et al.  SAS macro programs for geographically weighted generalized linear modeling with spatial point data: Applications to health research , 2012, Comput. Methods Programs Biomed..

[2]  Jutaek Oh,et al.  Forecasting Crashes at the Planning Level: Simultaneous Negative Binomial Crash Model Applied in Tucson, Arizona , 2004 .

[3]  D. Noyce,et al.  Spatial Analysis of Weather Crash Patterns , 2008 .

[4]  P J Gruenewald,et al.  Demographic and environmental correlates of pedestrian injury collisions: a spatial analysis. , 2000, Accident; analysis and prevention.

[5]  Tarek Sayed,et al.  Macrolevel Collision Prediction Models to Enhance Traditional Reactive Road Safety Improvement Programs , 2007 .

[6]  Geert Wets,et al.  Understanding spatial concentrations of road accidents using frequent item sets. , 2005, Accident; analysis and prevention.

[7]  H. Du,et al.  Relationship Between Transport Accessibility and Land Value: Local Model Approach with Geographically Weighted Regression , 2006 .

[8]  Chao Wang,et al.  Impact of traffic congestion on road accidents: a spatial analysis of the M25 motorway in England. , 2009, Accident; analysis and prevention.

[9]  Xuesong Wang,et al.  Integrating Trip and Roadway Characteristics to Manage Safety in Traffic Analysis Zones , 2011 .

[10]  L H Nitz,et al.  Spatial analysis of Honolulu motor vehicle crashes: I. Spatial patterns. , 1995, Accident; analysis and prevention.

[11]  F. Zhao,et al.  Using Geographically Weighted Regression Models to Estimate Annual Average Daily Traffic , 2004 .

[12]  Helai Huang,et al.  County-Level Crash Risk Analysis in Florida: Bayesian Spatial Modeling , 2010 .

[13]  Tarek Sayed,et al.  Community-Based, Macrolevel Collision Prediction Model Use with a Regional Transportation Plan , 2010 .

[14]  M. Quddus,et al.  A spatially disaggregate analysis of road casualties in England. , 2004, Accident; analysis and prevention.

[15]  Simon Blainey.  Trip end models of local rail demand in England and Wales , 2010 .

[16]  L. Zhang,et al.  Comparison of bandwidth selection in application of geographically weighted regression : a case study , 2008 .

[17]  J L Martin,et al.  Comparison of road crashes incidence and severity between some French counties. , 2003, Accident; analysis and prevention.

[18]  Tarek Sayed,et al.  Macro-level collision prediction models for evaluating neighbourhood traffic safety , 2006 .

[19]  P. Jovanis,et al.  Spatial analysis of fatal and injury crashes in Pennsylvania. , 2006, Accident; analysis and prevention.

[20]  Fred L. Mannering,et al.  The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives , 2010 .

[21]  Chris Brunsdon,et al.  Geographically Weighted Regression: The Analysis of Spatially Varying Relationships , 2002 .

[22]  Benoît Flahaut,et al.  Impact of infrastructure and local environment on road unsafety. Logistic modeling with spatial autocorrelation. , 2004, Accident; analysis and prevention.

[23]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[24]  David Wheeler,et al.  Multicollinearity and correlation among local regression coefficients in geographically weighted regression , 2005, J. Geogr. Syst..

[25]  A. Shalaby,et al.  Macrolevel Accident Prediction Models for Evaluating Safety of Urban Transportation Systems , 2003 .

[26]  Fang Zhao,et al.  Transit Ridership Model Based on Geographically Weighted Regression , 2006 .

[27]  Gordon Richard Lovegrove,et al.  Community-based, macro-level collision prediction models , 2006 .

[28]  Antonio Páez,et al.  Exploring Contextual Variations in Land Use and Transport Analysis Using a Probit Model with Geographical Weights , 2006 .

[29]  J. Neter,et al.  Applied Linear Regression Models , 1983 .

[30]  Davy Janssens,et al.  Modelling Short-Term Dynamics in Activity-Travel Patterns: Conceptual Framework of the Feathers Model , 2007 .

[31]  Mohamed Abdel-Aty,et al.  Temporal and spatial analyses of rear-end crashes at signalized intersections. , 2006, Accident; analysis and prevention.

[32]  Jean-Claude Thill,et al.  Urban Bicyclists , 2008 .

[33]  Meiwu An,et al.  Using Travel Demand Model and Zonal Safety Planning Model for Safety Benefit Estimation in Project Evaluation , 2011 .

[34]  N. Levine,et al.  Spatial analysis of Honolulu motor vehicle crashes: II. Zonal generators. , 1995, Accident; analysis and prevention.

[35]  Abishai Polus,et al.  Interchange Complexity Model and Related Safety Implications , 2013 .

[36]  Piyushimita Thakuriah,et al.  Evaluating pedestrian crashes in areas with high low-income or minority populations. , 2010, Accident; analysis and prevention.

[37]  Stephen Clark.  Estimating local car ownership models , 2007 .

[38]  Zongzhi Li,et al.  Geographically-Weighted Regression Models for Improved Predictability of Urban Intersection Vehicle Crashes , 2011 .

[39]  Young-Jun Kweon,et al.  Appropriate Regression Model Types for Intersections in SafetyAnalyst , 2012 .

[40]  Davy Janssens,et al.  Assessing the Impact of Fuel Cost on Traffic Demand in Flanders using Activity-Based Models , 2008 .

[41]  Alireza Hadayeghi,et al.  Temporal transferability and updating of zonal level accident prediction models. , 2006, Accident; analysis and prevention.

[42]  Michel Mouchart,et al.  The local spatial autocorrelation and the kernel method for identifying black zones. A comparative approach. , 2003, Accident; analysis and prevention.

[43]  Tarek Sayed,et al.  Transferability of Community-Based Collision Prediction Models for Use in Road Safety Planning Applications , 2010 .

[44]  S. Erdogan.  Explorative spatial analysis of traffic accident statistics and road mortality among the provinces of Turkey. , 2009, Journal of safety research.

[45]  Geert Wets,et al.  Application of Different Exposure Measures in Development of Planning-Level Zonal Crash Prediction Models , 2012 .

[46]  Mohamed Abdel-Aty,et al.  Macroscopic spatial analysis of pedestrian and bicycle crashes. , 2012, Accident; analysis and prevention.

[47]  Alireza Hadayeghi,et al.  Use of Advanced Techniques to Estimate Zonal Level Safety Planning Models and Examine their Temporal Transferability , 2009 .

[48]  M. Quddus.  Modelling area-wide count outcomes with spatial correlation and heterogeneity: an analysis of London crash data. , 2008, Accident; analysis and prevention.

[49]  Geert Wets,et al.  Identifying Hazardous Road Locations: Hot Spots versus Hot Zones , 2009, Trans. Comput. Sci..

[50]  Tom Brijs,et al.  Evaluating the road safety effects of a fuel cost increase measure by means of zonal crash prediction modeling. , 2013, Accident; analysis and prevention.

[51]  David W. S. Wong,et al.  Statistical Analysis with ArcView GIS , 2000 .

[52]  Mohamed Abdel-Aty,et al.  Managing Roadway Safety at the Traffic Analysis Zones Level , 2011 .

[53]  Ali Naderan,et al.  Aggregate crash prediction models: introducing crash generation concept. , 2010, Accident; analysis and prevention.

[54]  A. Shalaby,et al.  Development of planning level transportation safety tools using Geographically Weighted Poisson Regression. , 2010, Accident; analysis and prevention.

[55]  Robert B Noland,et al.  The effect of infrastructure and demographic change on traffic-related fatalities and crashes: a case study of Illinois county-level data. , 2004, Accident; analysis and prevention.

[56]  Bhagwant Persaud,et al.  Safety Prediction Models , 2007 .

[57]  A S Fotheringham,et al.  Geographically weighted Poisson regression for disease association mapping , 2005, Statistics in medicine.

[58]  Xuesong Wang,et al.  Modeling signalized intersection safety with corridor-level spatial correlations. , 2010, Accident; analysis and prevention.

[59]  Paul P Jovanis,et al.  Analysis of Road Crash Frequency with Spatial Models , 2008 .

[60]  Ernst Linder,et al.  Detecting Unsafe Roadways with Spatial Statistics: Point Patterns and Geostatistical Models , 2010 .