MODELLING AIR PASSENGER TRAFFIC FLOW IN MURTALA MUHAMMAD INTERNATIONAL AIRPORT LAGOS, NIGERIA: A TIME SERIES APPROACH

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dc.contributor.authorOMOLOHUNNU, Funsho Olalekan
dc.date.accessioned2018-01-08T12:57:25Z
dc.date.available2018-01-08T12:57:25Z
dc.date.issued2016-12
dc.descriptionA DISSERTATION SUBMITTED TO THE SCHOOL OF POSTGRADUATE STUDIES, AHMADU BELLO UNIVERSITY, ZARIA, NIGERIA. IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF MASTER OF SCIENCE (M.Sc.) DEGREE IN STATISTICS DEPARTMENT OF STATISTICS AHMADU BELLO UNIVERSITY, ZARIA, NIGERIA.en_US
dc.description.abstractAviation is a key sector of the Nigerian economy. Over the years, there have been remarkable influence of aviation on the economy. Murtala Muhammad International Airport (MMIA) Lagos is the busiest airport in Nigeria, accounting for over 60% of the total air passenger and aircraft movement in the country. In such an increasingly competitive aviation sector, it is imperative to make fairly accurate forecast so as to enable long-term planning, short term planning and decision regarding infrastructure development, flight networks and effective management.In this study, Artificial Neural Network (ANN), Seasonal Auto-Regressive Integrated Moving Average (SARIMA) and Holt-Winters Exponential Smoothing (HWES) models are used to model air passenger traffic flow in MMIA.The performances of these proposed models are compared for in-sample and out-of-sample performance by employing static forecast procedure over January 2014 to December 2015 forecast horizon. The best models from the SARIMA, ANN and HWES were selected by employing some performance metrics comprising, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) and residual diagnostics. The selected models forecasting performances were compared using the statistical loss functions, Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) for the measurement of forecast accuracy. Results show that ANN outperforms the other models in the domestic sector, while the HWES had the best performance in the international sector even though it was outperformed by SARIMA in the domestic sector. ANN yielded the best in-sample performance for domestic and international air passenger traffic. It was concluded that the ANN, which represents a class of non-linear time series model is very efficient in mimicking time series pattern and giving good forecast.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/9894
dc.language.isoenen_US
dc.subjectMODELLINGen_US
dc.subjectAIR PASSENGERen_US
dc.subjectTRAFFIC FLOWen_US
dc.subjectMURTALA MUHAMMAD INTERNATIONAL AIRPORTen_US
dc.subjectLAGOSen_US
dc.subjectA TIME SERIES APPROACHen_US
dc.titleMODELLING AIR PASSENGER TRAFFIC FLOW IN MURTALA MUHAMMAD INTERNATIONAL AIRPORT LAGOS, NIGERIA: A TIME SERIES APPROACHen_US
dc.typeThesisen_US
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