Time series count data models: An empirical application to traffic accidents

Accident Analysis and Prevention

Volumn 40, Issue 5, 2008, Pages 1732-1741

  Quddus, Mohammed A ^a  

^a LOUGHBOROUGH UNIVERSITY   (United Kingdom)

Author keywords

Accident prediction models; Integer valued autoregressive; Negative binomial; Time series count data; Traffic accidents

Indexed keywords

ACCIDENTS; CORRELATION METHODS; FOOD PROCESSING; HIGHWAY ACCIDENTS; MODAL ANALYSIS; POISSON DISTRIBUTION; POISSON EQUATION; RANDOM PROCESSES; VECTORS;

ACCIDENT PREDICTION MODELS; ARIMA MODELS; AUTO REGRESSIVE INTEGRATED MOVING AVERAGE; AUTO-REGRESSIVE; CONGESTION CHARGING; COUNT DATA MODELING; GOODNESS OF FIT; GREAT BRITAIN; INTEGER-VALUED AUTOREGRESSIVE; NEGATIVE BINOMIAL; NEGATIVE BINOMIALS; NEW CLASS; POISSON MODELS; POISSON REGRESSION; PRIMARY OBJECTIVE; SEASONALITY; SERIAL CORRELATION; TIME SERIES COUNT DATA; TIME SERIES MODELING; TIME-SERIES; TIME-SERIES DATA; TRAFFIC ACCIDENTS; UNOBSERVED HETEROGENEITY;

TIME SERIES ANALYSIS;

ARTICLE; HUMAN; MORTALITY; STATISTICAL MODEL; STATISTICS; TRAFFIC ACCIDENT; UNITED KINGDOM;

ACCIDENTS, TRAFFIC; GREAT BRITAIN; HUMANS; MODELS, STATISTICAL;

EID: 51749111064     PISSN: 00014575     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.aap.2008.06.011     Document Type: Article

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