DEVELOPMENT OF CLOUD-BASED VEHICULAR AD-HOC NETWORK AND TRAFFIC CONTROL STRATEGY IN REDUCING TRAFFIC CONGESTION

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dc.contributor.authorSULAIMON, Hakeem Adewale
dc.date.accessioned2017-07-24T15:28:05Z
dc.date.available2017-07-24T15:28:05Z
dc.date.issued2017-01
dc.descriptionA THESIS SUBMITTED TO THE SCHOOL OF POSTGRADUATE STUDIES, AHMADU BELLO UNIVERSITY, ZARIA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF DOCTOR OF PHILOSOPHY (Ph.D) DEGREE IN COMPUTER SCIENCE DEPARTMENT OF COMPUTER SCIENCE FACULTY OF PHYSICAL SCIENCES AHMADU BELLO UNIVERSITY, ZARIA, NIGERIAen_US
dc.description.abstractRoad traffic congestion occurs when the volume of traffic is too close to the maximum capacity of a road network. Traffic congestion always has negative effects on lives and environment such as fuel consumption and air pollution. More so, travel time increases as a result of traffic congestion and the situation will continue to deteriorate unless better traffic control strategies are implemented. Researches revealed that information systems need traffic flow model to provide prediction, such as, travel time prediction, route choice and flow rate. Existing traffic flow models are inadequate in handling mixed traffic stream. This research develops a conceptual framework for traffic control strategy to deliver an improved traffic congestion reduction system is developed. It also looks at route guiding scheme that can make routing decisions easy for drivers and also looks at how much expansion of road capacity could reduce traffic congestion. The developed traffic control strategies are implemented with VISSIM traffic simulation tool, CloudSim cloud simulator and JAVA programming Language. Percentage of Improvement (PoI) is used to evaluate the simulation framework. Root Mean Square Error (RMSE) and Root Mean Square Normalized Error (RMSNE) are used to validate the simulation model. Route Information System is developed for drivers to make routing decision. Evaluation results revealed that the proposed road expansion capacity could reduce traffic congestion in an urban area by approximately 11 and 47% when expanded by one lane and two lanes respectively. Also, a free flow is achieved by approximately 54% and 34.5% when drivers are guided with route information over traffic in non-signalized traffic stream and traffic control with traffic signal respectively.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/9104
dc.language.isoenen_US
dc.subjectCLOUD-BASED VEHICULAR AD-HOCen_US
dc.subjectNETWORK AND TRAFFIC CONTROLen_US
dc.subjectREDUCING TRAFFIC CONGESTIONen_US
dc.titleDEVELOPMENT OF CLOUD-BASED VEHICULAR AD-HOC NETWORK AND TRAFFIC CONTROL STRATEGY IN REDUCING TRAFFIC CONGESTIONen_US
dc.typeThesisen_US
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