Document Type

Dissertation

Rights

This item is available under a Creative Commons License for non-commercial use only

Disciplines

1.2 COMPUTER AND INFORMATION SCIENCE

Publication Details

A dissertation submitted in partial fulfilment of the requirements of Dublin Institute of Technology for the degree of M.Sc. in Computing (Advanced Software Development) 2017.

Abstract

With growing cities and the increased use of vehicles for transportation purposes, there is a demand to make the traffic management in cities smarter. An intelligent traffic light control that dynamically adapts to the existing traffic conditions can help reduce traffic congestion and CO2 emissions. This thesis reviews the popular traffic light control approaches - static, actuated and adaptive – based on their influences on recorded traffic conditions in Dublin. The Irish capital relies heavily on busses for public transport adding to the number of already moving vehicles in the city centre. Using vehicle count data from inductive loop detectors installed at the stop lines of numerous intersections in Dublin, the comparison and its results can be applied to the real world. Moreover, rainfall data is added to the dataset to provide insight into the influence of rainfall on the traffic conditions in Dublin. The findings reveal noteworthy differences in the daily volume development for the day categories weekday, weekend and public holiday. The proposed adaptive traffic light control algorithm based on inductive loop sensor technology has the main intent of simplifying the traffic light program and providing a truly adaptive scheduling approach, while minimising the cost of implementing and integrating different kinds of sensor technology at the same time.

The results of the comparison indicate that the adaptive algorithm provides the shortest waiting times and highest average vehicle speeds compared with static and actuated traffic lights across all approaching vehicle volume levels. The adaptive approach also presents to be the best solution for varying traffic conditions of rapidly increasing or decreasing traffic volumes for every day as found in the input analysis. An adaptively controlled traffic light system outperforms the static and actuated approaches in the generated average waiting time by 41% and 17%, respectively.

DOI

10.21427/D7J89B

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