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Effectiveness of a Speed Advisory Traffic Signal System for Conventional and Automated vehicles in a Smart City
Linköping University, Department of Science and Technology, Communications and Transport Systems.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This thesis project investigates the state-of-the-art in traffic management "Green Light Optimal Speed Advisory (GLOSA)" for vehicles in a smart city. GLOSA utilizes infrastructure and vehicles communication through using current signal plan settings and updated vehicular information in order to influence the intersection approach speeds.

The project involves traffic microscopic simulations for a mixed traffic environment of conventional and automated vehicles (AVs) both connected to the intersection control and guided by a speed advisory traffic management system. Among the project goals is to assess the effects on traffic performance when human drivers comply to the speed advice. The GLOSA management approach is accessed for its potential to improve traffic efficiency in a full market penetration of connected AVs with absolute compliance. The project also aims to determine the possible outcome resulting from enhancing the AVs capabilities such as implementing short time headways between vehicles in the future. 

The best traffic performance results achieved by operating GLOSA goes for connected AVs with the lowest simulated time headway (0.3 sec). The waiting time reduction reaches 95% and trip delay lessens to 88 %.

Place, publisher, year, edition, pages
2019. , p. 64
Keywords [en]
Traffic management, Microscopic simulation, Traffic flow efficiency (Waiting Time, Travel Time, Trip Delay), Green Light Optimal Speed Advisory (GLOSA), V2I communication, Conventional vehicles, connected vehicles, Automated vehicles, Mixed Traffic Environment, smart cities.
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:liu:diva-156650ISRN: LiU-ITN-TEK-A--19/003--SEOAI: oai:DiVA.org:liu-156650DiVA, id: diva2:1313385
Subject / course
Master's Programme in Intelligent Transport Systems, 120 ECTS
Presentation
2019-04-01, 15:15 (English)
Supervisors
Examiners
Available from: 2019-05-06 Created: 2019-05-03 Last updated: 2019-05-06Bibliographically approved

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CiteExportLink to record
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  • apa
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