Potentials and limitations of Green Light Optimal Speed Advisory systems

D. Eckhoff, Bastian Halmos, R. German
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引用次数: 72

Abstract

The reduction of CO2 emissions is one of the most anticipated features of future transportation systems. Smart traffic lights are believed to contribute to achieving this by either adapting their signal program or by informing approaching drivers. In this paper we investigate the potentials and limitations of the latter, that is, Green Light Optimal Speed Advisory (GLOSA) systems in a realistic, large scale simulation study. We examine the impact of different equipment rates of both traffic lights and vehicles on environmental related metrics but also study how these systems can increase the comfort for drivers by reducing waiting times and the number of stops. We find that at low traffic densities these systems can meet all their goals and lower CO2 emissions by up to 11.5% whereas in dense traffic several side-effects could be observed, including overall longer waiting times and even higher CO2 emissions for unequipped vehicles.
绿灯最佳速度咨询系统的潜力和局限性
减少二氧化碳排放是未来交通系统最值得期待的特征之一。智能交通灯被认为有助于实现这一目标,要么调整其信号程序,要么通知接近的司机。在本文中,我们研究了后者的潜力和局限性,即绿灯最优速度咨询(GLOSA)系统在现实的,大规模的模拟研究。我们研究了交通信号灯和车辆的不同设备率对环境相关指标的影响,但也研究了这些系统如何通过减少等待时间和停车次数来提高驾驶员的舒适度。我们发现,在低交通密度下,这些系统可以满足所有目标,并将二氧化碳排放量降低11.5%,而在密集的交通中,可以观察到一些副作用,包括总体等待时间更长,未装备车辆的二氧化碳排放量更高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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