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Global Journal of Engineering and Technology Advances
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Traffic crashes prediction of states in Nigeria using time series analysis

Authors: Aderinola Olumuyiwa Samson; Laoye Abdulrahman Adewale;

Traffic crashes prediction of states in Nigeria using time series analysis

Abstract

Death and injuries associated with traffic crashes is now acknowledged to be a general phenomenon in Nigeria. The major problem for most communities in developing countries is road accident which requires serious attention in searching for preventive measures to minimize it. This study is aimed at using time series analysis to predict the number of crashes for years 2016-2018, taking Yobe, Katsina, Abia and Ogun states as case studies. The available 1999-2018 crash data were sourced from the Federal Roads Safety Corps which was used for forecasting and 2016-2018 data for validating. The three forecasting models using time series analysis for MINITAB software (that is, quadratic, growth and linear) were used. The choice of a model was made from the computed data and compared with the actual data. It was recommended that policies be made to control, enforce, regulate and educate drivers by relevant government agencies such as the Federal Road Safety Corps. Increased efforts by relevant government agencies in the provision of road infrastructures with a view to reducing traffic crashes in the cities were also recommended.

Keywords

Traffic crash; MINITAB software; Quadratic model; Growth model; Linear model.

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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