Abstract

The rise in population worldwide and especially in Egypt, together with the increase in the number of vehicles present serious complications regarding traffic congestion and road safety. The elementary solution towards improving congestion is to expand road capacities by building new lanes. This, however, requires time and effort and therefore new methodologies are being implemented. Intelligent transportation systems (ITS) try to approach traffic congestion through the application of computational and engineering techniques. Traffic signal control is a branch of intelligent transportation systems which focuses on improving traffic signal conditions. A traffic signal controllers’ main objective is to improve this assignment in a way which reduces delays. This research proposes a new approach to enhancing traffic signal control and reducing delays of a single intersection, through the integration of an aggressive driving behavior classifier. Previous approaches dealt with traffic control and driver behavior separately, and therefore their successful integration is a new challenging area in the field. Multiple experiment sets were conducted to provide an indication to the effectiveness of our approach. Firstly, an aggressive driver behavior classifier using feed-forward neural network was successfully built utilizing Virginia Tech 100-car naturalistic driving study data. Its performance was compared against long short-term memory recurrent neural networks and support vector machines, and it resulted in better performance as shown by the area under the curve. To the best of our knowledge, this classifier is the first of its kind to be built on this 100-car study data. Secondly, a representation of aggressive driving behavior was constructed in the simulated environment, based on real life data and statistics. Finally, Mamdani’s fuzzy logic controller was modified to accommodate for the integration of the aggressive behavior classifier. The integration results were encouraging and yielded significant improvements at higher traffic flow volumes when compared against the built Mamdani’s controller. The results are promising and provide an initial step towards the integration of driver behavior classification and traffic signal control.

Department

Computer Science & Engineering Department

Degree Name

MS in Computer Science

Graduation Date

2-1-2016

Submission Date

January 2017

First Advisor

Moustafa, Mohamed

Committee Member 1

Goneid, Amr

Committee Member 2

Elkharashy, Mohamed

Extent

113 p.

Document Type

Master's Thesis

Rights

The author retains all rights with regard to copyright. The author certifies that written permission from the owner(s) of third-party copyrighted matter included in the thesis, dissertation, paper, or record of study has been obtained. The author further certifies that IRB approval has been obtained for this thesis, or that IRB approval is not necessary for this thesis. Insofar as this thesis, dissertation, paper, or record of study is an educational record as defined in the Family Educational Rights and Privacy Act (FERPA) (20 USC 1232g), the author has granted consent to disclosure of it to anyone who requests a copy.

Institutional Review Board (IRB) Approval

Not necessary for this item

Comments

I praise Allah the Almighty for granting me this opportunity and allowing me to complete this thesis. My initial and final thanks always go to Him. I would like to sincerely thank my supervisor, Dr. Mohamed Moustafa, for his guidance and support throughout the thesis period. This thesis would not have been in its current form if it were not for his help and supervision. I would also like to express my gratitude to the thesis committee, Dr. Mohamed Elkharashy and Dr. Amr Goneid for their insightful comments and encouragement. Moreover, I must express my very profound gratitude to my beloved parents for their trust in me, their endless support, love, encouragement, and their sacrifice throughout their life for me to reach to this stage. My gratitude also goes to my helpful sisters and my compassionate grandmother. The most special thanks goes to my partner and best friend, my husband. I am truly indebted to him for his continuous encouragement, unconditional support, and love.

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