Variable speed limit: A microscopic analysis in a connected vehicle environment
Variable speed limit: A microscopic analysis in a connected vehicle environment
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DOI：
10.1016/j.trc.2015.07.014
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发表时间：
2015-09-01
影响因子：
8.3
通讯作者：
Kattan, Lina
中科院分区：
文献类型：
--
作者：
Khondaker, Bidoura;Kattan, Lina
本文提出了一种变速限制（VSL）控制算法，以同时最大化网联汽车环境中的移动性、安全性和环境效益。联网汽车(CV)/自动驾驶汽车（AV）技术的发展有可能提供微观层面的基本数据，从而更好地了解驾驶员的实时行为。本文通过使用模型预测控制（MPC）方法，通过关注个体驾驶员的行为（例如加速和减速），采用微观方法研究了VSL控制算法。建立了一个多目标优化函数，旨在寻找机动性、安全性和可持续性之间的平衡。采用微观交通流预测模型计算总行程时间；采用替代安全措施碰撞时间（Time To Collision， TTC）来衡量瞬时安全性；并采用微观油耗模型（VT-Micro）对环境影响进行测度。利用实时驾驶员对张贴限速的遵从性来调整最优限速值。进行敏感性分析以比较所开发方法对目标函数中不同权重和两种不同CV百分比的性能。结果表明，在100%渗透率的情况下，开发的VSL方法始终优于不受控制的方案，可将总行程时间缩短20%，将安全性提高6-11%，将燃油消耗降低5-16%。我们的研究结果表明，与多准则优化相比，仅针对安全性进行优化的方案产生了更多的优化改进。因此，可以认为，在CVs渗透率为100%的情况下，仅对安全性进行优化就足以实现所有措施的同时和最佳改进。然而，在渗透率较低的情况下，只考虑机动性或油耗的优化结果显示碰撞风险较高，结果好坏参半。这表明，在这样的渗透率下，多准则优化对于实现所审查措施的最优和平衡效益至关重要。(C) 2015年作者。Elsevier Ltd.出版。这是一篇基于CC BY-NC-ND许可的开放获取文章。
This paper presents a Variable Speed Limit (VSL) control algorithm for simultaneously maximizing the mobility, safety and environmental benefit in a Connected Vehicle environment. Development of Connected Vehicle (CV)/Autonomous Vehicle (AV) technology has the potential to provide essential data at the microscopic level to provide a better understanding of real-time driver behavior. This paper investigated a VSL control algorithm using a microscopic approach by focusing on individual driver's behavior (e.g., acceleration and deceleration) through the use of Model Predictive Control (MPC) approach. A multi-objective optimization function was formulated with the aim of finding a balanced trade-off among mobility, safety and sustainability. A microscopic traffic flow prediction model was used to calculate Total Travel Time (TIT); a surrogate safety measure Time To Collision (TTC) was used to measure instantaneous safety; and, a microscopic fuel consumption model (VT-Micro) was used to measure the environmental impact. Real-time driver's compliance to the posted speed limit was used to adjust the optimal speed limit values. A sensitivity analysis was conducted to compare the performance of the developed approach for different weights in the objective function and for two different percentages of CV. The results showed that with 100% penetration rate, the developed VSL approach outperformed the uncontrolled scenario consistently, resulting in up to 20% of total travel time reductions, 6-11% of safety improvements and 5-16% reduction in fuel consumptions. Our findings revealed that the scenario which optimized for safety alone, resulted in more optimum improvements as compared to the multi-criteria optimization. Thus, one can argue that in case of 100% penetration rates of CVs, optimizing for safety alone is enough to achieve simultaneous and optimum improvements in all measures. However, mixed results were obtained in case of lower % penetration rate which showed higher collision risk when optimizing for only mobility or fuel consumption. This indicates that with such % penetration rate, multi-criteria optimization is crucial to realize optimum and balanced benefits for the examined measures. (C) 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.