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DISSERTATION O Attribution COPYRIGHT AND CITATION CONSIDERATIONS FOR THIS THESIS/ DISSERTATION o Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. o NonCommercial — You may not use the material for commercial purposes. o ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. How to cite this thesis Surname, Initial(s). (2012) Title of the thesis or dissertation. PhD. (Chemistry)/ M.Sc. (Physics)/ M.A. (Philosophy)/M.Com. (Finance) etc. [Unpublished]: University of Johannesburg. Retrieved from: https://ujcontent.uj.ac.za/vital/access/manager/Index?site_name=Research%20Output (Accessed: Date). The design of a vehicular traffic flow prediction model for Gauteng freeways using ensemble learning by TEBOGO EMMA MAKABA A dissertation submitted in fulfilment for the Degree of Magister Commercii in Information Technology Management Faculty of Management UNIVERSITY OF JOHANNESBURG Supervisor: Dr B.N Gatsheni 2016 DECLARATION I certify that the dissertation submitted by me for the degree Master’s of Commerce (Information Technology Management) at the University of Johannesburg is my independent work and has not been submitted by me for a degree at another university. TEBOGO EMMA MAKABA i ACKNOWLEDGEMENTS I hereby wish to express my gratitude to the following individuals who enabled this document to be successfully and timeously completed: Firstly, GOD Supervisor, Dr BN Gatsheni Mikros Traffic Monitoring(Pty) Ltd (MTM) for providing me with the traffic flow data My family and friends Prof M Pillay and Ms N Eland Faculty of Management for funding me with the NRF Supervisor linked bursary ii DEDICATION This dissertation is dedicated to everyone who supported me during the project, from the start till the end. iii ABSTRACT Traffic congestion is a major problem in the cities of Gauteng Province (GP). There is high vehicle traffic congestion especially on the Ben Schoeman freeway from Johannesburg to Pretoria during peak travelling times of 06:00 to 09:00 and 15:00 to 18:00. The increasing number of vehicles in the freeways of GP often leads to accidents, which in turn worsen traffic congestion. Traffic impacts negatively on commuters and the businesses around Gauteng Province. Several intervention programmes were implemented by the Gauteng authorities to minimize the rapid increase of traffic volume but this has not solved the traffic congestion problem. The aim of this study was to construct a vehicular traffic prediction model using ensemble learning methods and machine learning algorithms. Vehicle traffic flow data was obtained from Mikro’s Traffic Monitoring (MTM), a company contracted by the Gauteng Department of Transport (DoT) to collect vehicle traffic data. The vehicle traffic flow data for the freeway that links Johannesburg with Pretoria (i.e. M1 North extending to the N1 North) was used in this study. Ensemble learning methods used to construct vehicle traffic prediction models, namely Bagging, Boosting, Stacking and Random Forest together with machine learning algorithms that include Decision Trees, Support Vector Machine and Multi-Layer Perceptron. A cross-validation (CV) method was used for evaluating the models. The best prediction model was selected by computing the cost prediction by using a combination of a loss matrix and a confusion matrix. The results showed that the models constructed using Random Forest ensemble method achieved the best prediction for traffic congestion at 99.991%. Commuters wishing to travel on the Ben Schoeman freeway can predict traffic flow by using an App. The App can allow the commuters to enter variables such as day of week, travel time and traffic volume. The entered variables will predict travel conditions by examining target concepts such as Freeflow, FlowingCongestion and Congested. The commuters will only be able to predict traffic flow although they will not full have knowledge of the actual vehicle traffic volume in the freeway. In that case they will depend on the media (e.g. radio) traffic reports. The implications of the results are the improvement in the competitiveness of Gauteng Province as an investment destination. This model can inform commuters of traffic flow patterns ahead of time and this enables commuters to make appropriate travel arrangements. iv Table of Contents Declaration ......................................................................................................................................... i Acknowledgements ........................................................................................................................... ii Dedication ........................................................................................................................................ iii Abstract ........................................................................................................................................... iv LIST OF TABLES ................................................................................................................................ viii LIST OF FIGURES ................................................................................................................................ x DEFENITION OF TERMS .................................................................................................................... xiv CHAPTER 1: INTRODUCTION .............................................................................................................. 1 1.1 Introduction ........................................................................................................................... 1 1.2 Research Problem .................................................................................................................. 3 1.3 Research Statement ............................................................................................................... 3 1.4 Aim of the study .................................................................................................................... 3 1.5 Objectives .............................................................................................................................. 4 1.5.1 Sub-Objectives ............................................................................................................... 4 1.6 Research Methodology .......................................................................................................... 4 1.7 Dissertation Contribution ....................................................................................................... 5 1.8 Structure of the Dissertation .................................................................................................. 5 CHAPTER 2: LITERATURE REVIEW ...................................................................................................... 6 2.1 Introduction ........................................................................................................................... 6 2.2 Related Research ................................................................................................................... 6 2.3 Theoretical Framework of the study…………………………………………………………………………………….11 2.4 Chapter Conclusion .............................................................................................................. 13 CHAPTER 3: METHODS .................................................................................................................... 14 3.1 Introduction ......................................................................................................................... 14 3.2 Research Methodology ........................................................................................................ 14 3.2.1 Research Approach ...................................................................................................... 14 3.2.2 Research Design ........................................................................................................... 15 3.2.3 Research Settings ......................................................................................................... 15 3.2.4 Research Methods ....................................................................................................... 16 3.2.5 Dataset Collection ........................................................................................................ 17 3.2.6 The Data Collection Procedure ..................................................................................... 18 3.2.7 Validations ................................................................................................................... 18 3.2.8 Limitations of the study ................................................................................................ 19 3.3 Attribute Selection ............................................................................................................... 19 3.4 Machine Learning Algorithms Used for the Study ................................................................. 21 v 3.4.1 Decision Trees C4.5 (J48) .............................................................................................. 21 3.4.2 Multi-Layer Perceptron (MLP) ...................................................................................... 24 3.4.3 Support Vector Machine (SVM)
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