Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/29534
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dc.titleDistributed multi-agent based traffic management system
dc.contributor.authorBALAJI PARASUMANNA GOKULAN
dc.date.accessioned2011-11-30T18:00:30Z
dc.date.available2011-11-30T18:00:30Z
dc.date.issued2011-02-28
dc.identifier.citationBALAJI PARASUMANNA GOKULAN (2011-02-28). Distributed multi-agent based traffic management system. ScholarBank@NUS Repository.
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/29534
dc.description.abstractTraffic congestion is a major recurring problem faced in many countries in the world due to increased urbanization and availability of affordable vehicles. Congestion problem can be dealt with in a number of ways ¿ Increasing the capacity of the roads, promoting alternate modes of transportation or making efficient use of the existing infrastructure. Among these, the most feasible option is to improve the usage of existing roads. Adjustment of the green time in signals to allow more vehicles to cross the intersection has been the widely accepted method for solving congestion problem. Green time essentially dictates the time during which vehicles are allowed to cross an intersection, thereby avoiding conflicting movements of vehicles and improving safety at an intersection. Conventional and traditional traffic signal control methods have shown limited success in optimizing the timings in signals due of the lack of accurate mathematical models of traffic flow at an intersection and uncertainties associated with the traffic data. Traffic flow refers to the number of vehicles crossing an intersection every hour. The traffic environment is dynamic and traffic signal timings at one intersection influences the traffic flow rate at the connected intersection. This necessitates the use of hybrid computational intelligent models to predict the traffic flow and influence of the neighbouring intersection signals on the green signal timings. Increased communication overheads, reliability issues, data mining, and real-time control requirements limits the use of centralized traffic signal controls. These limitations are overcome by distributed traffic signal controls. However, a major disadvantage with distributed signal control is the partial view of each computing entity involved in the calculation of green time at an intersection. In order to improve the global view, communication and learning capabilities needs to be incorporated in the computing entity to create a model of the neighbouring computing entities. Multi-agent systems provide such an distributed architecture with learning and communication capabilities. In this dissertation, a distributed multi-agent architecture capable of learning from the traffic environment and communicating with the neighbouring intersections is developed. Four computational intelligent decision systems with different internal architectures were developed. First two approaches were offline trained methods using deductive reasoning. The third approach was based on online batch learning method to co-evolve the membership functions and rule base in type-2 fuzzy decision system. The fourth decision system developed is an online shared reward Q-learning based neuro-type2 fuzzy network. Performance of the proposed multi-agent based traffic signal controls for different traffic simulation scenarios were evaluated using a simulated urban road traffic network of Singapore. Comparative analysis performed over the benchmark traffic signal controls ¿ Hierarchical Multi-agent Systems (HMS) and GLIDE (Green Link Determine) indicated considerable improvement in travel time delay and mean speed of vehicles when using proposed multi-agent based traffic signal control methods.
dc.language.isoen
dc.subjectMulti-agent Systems, Type 2 Fuzzy logic, Neural networks, Q-learning, Symbiotic evolutionary learning, Fuzzy similarity measure
dc.typeThesis
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.contributor.supervisorSRINIVASAN, DIPTI
dc.description.degreePh.D
dc.description.degreeconferredDOCTOR OF PHILOSOPHY
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Ph.D Theses (Open)

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