Multi objective optimization in traffic signal control
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Multi objective optimization in traffic signal control
DE MONTFORT UNIVERSITY LEICESTERDMU’s Interdisciplinary research Group in Intelligent Transport Systems, (DIGITS) Faculty of Computing, Engineering an Multi objective optimization in traffic signal control nd MediaMulti-objective Optimization in Traffic Signal ControlAuthor:Phuong Thi Mai NguyenSupervisor:Prof. Yingjic YangDr. Benjamin PassowDr. Lipika DekaA thesis submitted in fulfilment of the. requirements for the, degree of Doctor of Philosophy43678AbstractTraffic Signal Control systems arc one of Multi objective optimization in traffic signal control the most popular Intelligent Transport Systems and they arc widely used around the world to regulate traffic flow. Recently, complex optimization tecMulti objective optimization in traffic signal control
hniques have been applied to traffic signal control systems to improve t heir performance. Traffic simulators are one of the most, popular tools to evDE MONTFORT UNIVERSITY LEICESTERDMU’s Interdisciplinary research Group in Intelligent Transport Systems, (DIGITS) Faculty of Computing, Engineering an Multi objective optimization in traffic signal control using simulation-based approaches. Although evaluating solutions using microscopic traffic simulators has several advantages, the simulation is very time-consuming.Multi-objective Evolutionary Algorithms (MOEAs) are in many ways superior to traditional search methods. They have been widely utilized Multi objective optimization in traffic signal control in traffic signal optimization problems. However, running MOEAs on traffic optimization problems using microscopic traffic simulators to estimate theMulti objective optimization in traffic signal control
effectiveness of solutions is time-consuming. Thus, MOEAs which can produce good solutions at. a reasonable processing time, especially at an early sDE MONTFORT UNIVERSITY LEICESTERDMU’s Interdisciplinary research Group in Intelligent Transport Systems, (DIGITS) Faculty of Computing, Engineering an Multi objective optimization in traffic signal control Therefore, optimization approaches which have good anytuno behaviour an' desirable in evaluation traffic signal optimization. Moreover, small population sizes are inevitable for scenarios where processing capabilities are limited but rcquừc quick response times. In this work, two novel optimization Multi objective optimization in traffic signal control algorithms arc introduced that, improve anytime behaviour anti can work effectively with various population sizes.NS-LS is a hybrid of Non-dominatedMulti objective optimization in traffic signal control
Sorting Genetic Algorithm II (NSGA-II) and a local search which has the ability to predict a potential search direction. NS-LS is able to produce goodDE MONTFORT UNIVERSITY LEICESTERDMU’s Interdisciplinary research Group in Intelligent Transport Systems, (DIGITS) Faculty of Computing, Engineering an Multi objective optimization in traffic signal control , computational cost is not considered in NS-LS. A siưrogatc-assistcd approach based on local search (SA-LS) which Is an enhancement of NS-LS is also introduced. SA-LS uses a surrogate model constructed using solutions which already have been evaluated by a traffic simulator in previous generations. Multi objective optimization in traffic signal control NS-LS and SA-LS are evaluated on the well-known Benchmark test funct ions: ZDT1 anti ZDT‘2. and two real-world traffic scenarios: Andrea Costa and PasMulti objective optimization in traffic signal control
ubio. The proposed algorithms are also compared to NSGA-II anil Multiobjcctivc Evolutionary Algorithm based on Decomposition (MOEA/D). The results shoDE MONTFORT UNIVERSITY LEICESTERDMU’s Interdisciplinary research Group in Intelligent Transport Systems, (DIGITS) Faculty of Computing, Engineering an Multi objective optimization in traffic signal control ood anytime behaviour and can work well with different populat ion sizes. Furthermore. SA-LS also showed to produce most ly superior results as compared to NS-LS, NSGA-II, and MOEA/D.AcknowledgementsI would like to express my sincere gratit ude to my supervisory team Prof. Yiugjic Yang, Dr. Benjamin Multi objective optimization in traffic signal control N. Passow and Dr. Lipika Dcka who provided unst inting support wit 11 their insights, expertise, and valuable comments. Without their encouragement aMulti objective optimization in traffic signal control
nd support, this thesis would not have been completed on a limited time frame. Especially, I would like to expand deepest thank to my dedicated supervDE MONTFORT UNIVERSITY LEICESTERDMU’s Interdisciplinary research Group in Intelligent Transport Systems, (DIGITS) Faculty of Computing, Engineering an Multi objective optimization in traffic signal control inspiration and encouragement play import ant role in keeping me moving forward.I gratefully t hank t he Minist ry of Educat ion and Training of Vietnam for funding me a four-ycar scholarship for my study in the UK. Without this financial sponsorship. I would not Im' able to come to study in t he UK Multi objective optimization in traffic signal control .My sincere thanks also go to the De Montfort University Interdisciplinary research Group in Intelligent Transport Systems (DIGITS) for the financialMulti objective optimization in traffic signal control
support to participate the WCCI 2016 conference in Vancouver and the International student workshop 2016 in Wroclaw, Poland. I also would like to thanDE MONTFORT UNIVERSITY LEICESTERDMU’s Interdisciplinary research Group in Intelligent Transport Systems, (DIGITS) Faculty of Computing, Engineering an Multi objective optimization in traffic signal control e throughout this journey. Especially, I owe thanks to a very special person, my husband, for his love, support, and understanding during my pursuit of Ph.D. I greatly appreciate his belief in me that gave me extra strength to got things done.ii Multi objective optimization in traffic signal control DE MONTFORT UNIVERSITY LEICESTERDMU’s Interdisciplinary research Group in Intelligent Transport Systems, (DIGITS) Faculty of Computing, Engineering anGọi ngay
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