Performance study of a Green Light Optimized Speed Advisory (GLOSA) application using an integrated cooperative ITS simulation platform

Katsaros, Konstantinos V.; Kernchen, Ralf; Dianati, Mehrdad; Rieck, David · 2011 · OpenAlex-citations

DOI: 10.1109/iwcmc.2011.5982524

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Summary

This paper evaluates the performance of a Green Light Optimized Speed Advisory (GLOSA) application, a cooperative Intelligent Transportation System (ITS) designed to reduce fuel consumption and traffic congestion. The study addresses the need for accurate simulations that account for the dynamics of vehicular environments, communication mechanisms, and driver behavior, which previous studies often oversimplified. The primary research question investigates how GLOSA impacts average fuel consumption and average stop time at traffic lights under varying conditions of vehicle penetration rates and traffic density. The researchers employed an integrated cooperative ITS simulation platform called VSimRTI, which couples the SUMO traffic simulator, the JiST/SWANS communication simulator, and a custom Java application simulator. The simulation scenario modeled a 0.965 km urban route in Guildford, UK, featuring two traffic lights with specific timing cycles. Traffic flow was governed by the microscopic Stefan Krauss car-following model, with vehicles entering via a Poisson distribution. The GLOSA algorithm utilized infrastructure-to-vehicle (I2V) communication via IEEE 802.11p to broadcast traffic light phase information. The algorithm calculated a target advisory speed for equipped vehicles to arrive at the intersection during the green phase, updating every second to ensure robustness. Two baseline scenarios were compared: one without driver assistance and one with GLOSA active. The results demonstrate that GLOSA significantly improves both fuel efficiency and traffic flow. The study identified an optimal activation distance for the advisory system of approximately 300 meters; distances shorter than this provided insufficient reaction time, while longer distances yielded diminishing returns. Regarding penetration rates, the study found that while average stop times decreased even with low adoption rates, a critical threshold of 50% equipped vehicles was necessary to observe significant fuel savings. At high penetration rates, the system achieved up to a 7% reduction in average fuel consumption. Interestingly, non-equipped vehicles also benefited from reduced stop times due to the car-following dynamics of the traffic model. Furthermore, the benefits for fuel efficiency were most pronounced in high-density traffic scenarios, whereas traffic efficiency improvements (reduced stop times) were greater in lower-density conditions, reaching up to an 89% reduction in average stop time. The significance of this work lies in its validation of GLOSA as a viable method for enhancing urban mobility and environmental sustainability through cooperative ITS. By using a comprehensive simulation platform that integrates traffic, communication, and application layers, the study provides more realistic estimates of performance than previous isolated models. The findings suggest that widespread deployment of GLOSA can yield substantial reductions in fuel consumption and congestion, particularly when penetration rates exceed 50%. The paper concludes by recommending future extensions, such as accounting for existing vehicle queues at lights and validating results with large-scale field tests.

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