Low-cast smart traffic management system

dc.contributor.authorDonkor, Jude Asare
dc.date.accessioned2021-01-26T11:03:24Z
dc.date.available2021-01-26T11:03:24Z
dc.date.issued2020-05
dc.descriptionCapstone Project submitted to the Department of Engineering, Ashesi University in partial fulfillment of the requirements for the award of Bachelor of Science degree in Computer Engineering, May 2020en_US
dc.description.abstractIn Ghana and other parts of Africa, most traffic management systems are implemented by the use of a timer at each phase. This method of traffic management is inefficient because equal length of green light is assigned to each lane at the intersection; resulting in long wait times for vehicles behind a traffic light with the red signal on, especially when other lanes are vacant. This project addresses problems such as the one stated in the previous sentence through designs and implementation of a low-cost smart traffic management system. The project is carried out by the use of electrical components that are affordable, easy to maintain and reliable. An inductive loop is used as the vehicle detection device for each lane. Traffic density acquisition is then done using an algorithm running on a single board computer, Raspberry Pi, to obtain the number of vehicles on each lane. After the acquisition of traffic density, the data is processed to obtain the green light length for a lane. After green light length determination, the traffic density acquired earlier is sent to a database on a server via Wi-Fi to enable another microcontroller to use the data to execute traffic coordination between a lane and its preceding lane. In testing the coordinated control system, the statistical analysis showed that the coordinated traffic control between a lane and its preceding lane can be improved to gain the optimal performance.en_US
dc.description.sponsorshipAshesi Universityen_US
dc.identifier.urihttp://hdl.handle.net/20.500.11988/600
dc.language.isoen_USen_US
dc.subjecttraffic lightsen_US
dc.subjectcontrol systemsen_US
dc.subjecttraffic managementen_US
dc.subjectRaspberry Pien_US
dc.subjectvehicle detectionen_US
dc.titleLow-cast smart traffic management systemen_US
dc.typeSenior projecten_US

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