An affordable density-based traffic management system
Traffic management systems at road intersections of most African cities is based on fixed timings where equal length of green light is assigned to each lane at the intersection. This method of traffic management is inefficient because it causes unnecessarily long waiting times for vehicles at the intersections especially during periods when some lanes have few or no vehicles passing through them. In this project, an affordable density based traffic management system is designed and implemented. This is achieved by selecting components that are low cost, consume low amounts of power and do not require digging onto the road pavements. A pi camera with night vision capability is used as the vehicle detection device for every lane. Image processing is then done using an algorithm running on a raspberry pi to determine the number of vehicles on each lane. This information on the number of vehicles is sent to a central raspberry pi via a radio transceiver which then uses an algorithm to determine the next lane to receive green light, as well as the green light duration. In testing the system, the best position of the camera relative to the lane is investigated in an experiment. The furthest distance the camera can be mounted is determined to be 30m. The night vision capability of the camera is also tested. Despite the advantages that come with this low-cost system, it has some limitations. The camera quality is low hence the image processing algorithm may not be completely accurate in distinguishing between objects in the image. The camera’s field of view is also small, necessitating the use of an extra lens that will increase its field of view.
Capstone Project submitted to the Department of Engineering, Ashesi University in partial fulfillment of the requirements for the award of Bachelor of Science degree in Electrical and Electronic Engineering, April 2019
vehicle detection, intelligent transport systems (ITS), Pugh Matrix, hardware components, traffic control system