Crash Prediction Models (CPMs) have been used in the developed countries as a useful tool by road Engineers and Planners. There is however a limited literature on the prediction of road traffic crashes in Low and Middle Income Countries (LMICs) including Ghana. This book studies crash data and develops a prediction model for injury road traffic crashes occurring on rural highways in the Ashanti Region of Ghana. Data was collected from rural highway sections of varying lengths. Data collected for each segment comprised injury crash data, traffic flow and speed data, and roadway characteristics and road geometry data. The Generalised Linear Model (GLM) with Negative Binomial (NB) error structure was used to estimate the model parameters. Crash rates were initially related to each explanatory variable in turn to ascertain if any relationships existed. Two types of models, the ?core' model which included key exposure variables only and the ?full' model which included a wider range of variables were developed and interpretations given. Road and Traffic Engineers and Planners can apply the crash prediction model as a tool in safety improvement works and in the design of safer roads.
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Research Scientist, M.Phil., B.Sc., Dip., M.GhIE: Building and Road Research Institute (Council for Scientific and Industrial Research), Traffic and Transportation Engineering Division, Kumasi, Ghana.
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Crash Prediction Models (CPMs) have been used in the developed countries as a useful tool by road Engineers and Planners. There is however a limited literature on the prediction of road traffic crashes in Low and Middle Income Countries (LMICs) including Ghana. This book studies crash data and develops a prediction model for injury road traffic crashes occurring on rural highways in the Ashanti Region of Ghana. Data was collected from rural highway sections of varying lengths. Data collected for each segment comprised injury crash data, traffic flow and speed data, and roadway characteristics and road geometry data. The Generalised Linear Model (GLM) with Negative Binomial (NB) error structure was used to estimate the model parameters. Crash rates were initially related to each explanatory variable in turn to ascertain if any relationships existed. Two types of models, the 'core' model which included key exposure variables only and the 'full' model which included a wider range of variables were developed and interpretations given. Road and Traffic Engineers and Planners can apply the crash prediction model as a tool in safety improvement works and in the design of safer roads. 108 pp. Englisch. Bestandsnummer des Verkäufers 9783843378260
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ackaah WilliamsResearch Scientist, M.Phil., B.Sc., Dip., M.GhIE: Building and Road Research Institute (Council for Scientific and Industrial Research), Traffic and Transportation Engineering Division, Kumasi, Ghana.Crash Predicti. Bestandsnummer des Verkäufers 5467692
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Crash Prediction Models (CPMs) have been used in the developed countries as a useful tool by road Engineers and Planners. There is however a limited literature on the prediction of road traffic crashes in Low and Middle Income Countries (LMICs) including Ghana. This book studies crash data and develops a prediction model for injury road traffic crashes occurring on rural highways in the Ashanti Region of Ghana. Data was collected from rural highway sections of varying lengths. Data collected for each segment comprised injury crash data, traffic flow and speed data, and roadway characteristics and road geometry data. The Generalised Linear Model (GLM) with Negative Binomial (NB) error structure was used to estimate the model parameters. Crash rates were initially related to each explanatory variable in turn to ascertain if any relationships existed. Two types of models, the 'core' model which included key exposure variables only and the 'full' model which included a wider range of variables were developed and interpretations given. Road and Traffic Engineers and Planners can apply the crash prediction model as a tool in safety improvement works and in the design of safer roads.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 108 pp. Englisch. Bestandsnummer des Verkäufers 9783843378260
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Crash Prediction Models (CPMs) have been used in the developed countries as a useful tool by road Engineers and Planners. There is however a limited literature on the prediction of road traffic crashes in Low and Middle Income Countries (LMICs) including Ghana. This book studies crash data and develops a prediction model for injury road traffic crashes occurring on rural highways in the Ashanti Region of Ghana. Data was collected from rural highway sections of varying lengths. Data collected for each segment comprised injury crash data, traffic flow and speed data, and roadway characteristics and road geometry data. The Generalised Linear Model (GLM) with Negative Binomial (NB) error structure was used to estimate the model parameters. Crash rates were initially related to each explanatory variable in turn to ascertain if any relationships existed. Two types of models, the 'core' model which included key exposure variables only and the 'full' model which included a wider range of variables were developed and interpretations given. Road and Traffic Engineers and Planners can apply the crash prediction model as a tool in safety improvement works and in the design of safer roads. Bestandsnummer des Verkäufers 9783843378260
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Taschenbuch. Zustand: Neu. Road Traffic Crashes on Rural Highways in the Ashanti Region of Ghana | Factors Relating to Road Geometric Design and Traffic Data Characteristics | Williams Ackaah | Taschenbuch | 108 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783843378260 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Bestandsnummer des Verkäufers 107140352
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