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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/6813

Title: Logit Model for the Determinants of Drug Driving: A Case of Commercial Drivers in Ghana
Authors: Shaibu, Osman
Issue Date: 10-Feb-2015
Abstract: Generally, significant proportion of road accidents can be attributed to drug driving globally. The recent rise in the number of road traffic accidents by a report of the Ghana National Road Safety Commission in 2012, calls for a review of drug driving. This study was conducted to asses the use of drugs by commercial drivers in Ghana and to determine the social factors that contribute to drug driving. A self administered questionnaire was used. A sample of 300 questionnaires were administered and duly edited thereafter to ensure consistency as well as clarity and reliability. The purposive sampling approach was used to select commercial bus and cargo terminals of some regions based on the locations of the terminals and the population of vehicles. The backward elimination regression model-building technique was used to select the significant variable(s) into a tted logistic regression model. A 5 percent statistical significance level is required for a variable to stay in a model. All respondents were male adults within the active productive age and 41 percent are illiterate. Approximately, 34 percent of the respondents admitted using drug when driving and 70 percent learn how to drive from unapproved driving institutions. Level of education, Time used to drive, Mode of training and Distance traveled were the most significant variables associated with the use of drug by commercial drivers. There are significant association between Levels of educational, Distance traveled, Time (hours) used to drive and Drug Use by commercial drivers in Ghana. Drug driving is a threat to the transportation industry and measures should be taking to address this problem.
Description: A thesis submitted to the Institute of Distance Learning, Kwame Nkrumah University of Science and Technology in partial fulfilment of the requirements for the award of Master of Science degree in Industrial Mathematics, 2014
URI: http://hdl.handle.net/123456789/6813
Appears in Collections:Distance Learning

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