A Poisson-lognormal conditional-autoregressive model for multivariate spatial analysis of pedestrian crash counts across neighborhoods

Accident Analysis and Prevention

Volumn 60, Issue , 2013, Pages 71-84

  Wang, Yiyi ^a   Kockelman, Kara M ^b  

^a Idaho National Laboratory   (United States)

^b The University of Texas at Austin   (United States)

Author keywords

Count models; Crash modeling; Multivariate conditional autoregressive models; Pedestrian crashes; Spatial data

Indexed keywords

CONDITIONAL AUTOREGRESSIVE MODELS; COUNT MODELS; CRASH MODELING; PEDESTRIAN CRASHES; SPATIAL DATA;

AUTOCORRELATION; LAND USE; MODEL AUTOMOBILES; MONTE CARLO METHODS; PAVEMENTS; PEDESTRIAN SAFETY;

ACCIDENTS;

ARTICLE; BAYES THEOREM; COUNT MODELS; CRASH MODELING; DEMOGRAPHY; GEOGRAPHIC INFORMATION SYSTEM; HUMAN; MONTE CARLO METHOD; MULTIVARIATE ANALYSIS; MULTIVARIATE CONDITIONAL AUTOREGRESSIVE MODELS; PEDESTRIAN CRASHES; POISSON DISTRIBUTION; POPULATION DENSITY; PROBABILITY; REGRESSION ANALYSIS; SAFETY; SPATIAL ANALYSIS; SPATIAL DATA; STATISTICAL MODEL; STATISTICS; TRAFFIC ACCIDENT; UNITED STATES; WALKING;

COUNT MODELS; CRASH MODELING; MULTIVARIATE CONDITIONAL AUTOREGRESSIVE MODELS; PEDESTRIAN CRASHES; SPATIAL DATA;

ACCIDENTS, TRAFFIC; BAYES THEOREM; GEOGRAPHIC INFORMATION SYSTEMS; HUMANS; MARKOV CHAINS; MODELS, STATISTICAL; MONTE CARLO METHOD; MULTIVARIATE ANALYSIS; POISSON DISTRIBUTION; POPULATION DENSITY; REGRESSION ANALYSIS; RESIDENCE CHARACTERISTICS; SAFETY; SPATIAL ANALYSIS; TEXAS; WALKING;

EID: 84883875745     PISSN: 00014575     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.aap.2013.07.030     Document Type: Article

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