Abstract：
Mobility and environmental benefits of Green Light Optimal Speed Advisory (GLOSA) systems have been reported by many previous research studies, however, there is insufficient knowledge on the safety implications of such an application. For safe deployment of GLOSA system, it is most critical to identify and address potential safety issues in the design process. It can be argued that implementation of GLOSA system can improve safety by reducing traffic conflicts associated with the interrupted traffic flow at signalised intersections. However, more research findings are needed from field and simulation based studies to evaluate the impacts on safety under a variety of real-world scenarios. As part of the LEVITATE (Societal Level Impacts of Connected and Automated Vehicles) project under European Union\'s Horizon 2020 Programme, the main objective of this study is to examine the safety impacts of GLOSA under mixed traffic compositions with varying market penetration rates (MPR) of connected and automated vehicles (CAVs). A calibrated and validated microsimulation model (developed in Aimsun) of the greater Manchester area was used for this study where three signalised intersections in a corridor were identified for implementing GLOSA system. An improved algorithm was developed by identifying the potential issues/limitations in some of the GLOSA algorithms found in literature. Behaviours of CAVs were modelled based on the findings of a comprehensive literature review. Safety analysis was performed through processing the simulated vehicular trajectories in the surrogate safety assessment model (SSAM) by the Federal Highway Administration (FHWA). The surrogate safety assessment results showed small improvement in safety with the GLOSA implementation at multiple intersections in the test network only at low MPR (20%) scenarios of CAVs, as compared to the respective without GLOSA scenarios. No or rather slightly lower improvement in safety was observed with GLOSA implementation under mixed fleet scenarios with 40 % or higher 1st Generation or 2nd Generation CAVs, as compared to the respective scenarios without GLOSA. The implementation of GLOSA system was also found to have some impact on the traffic conflict types (although not consistent across all MPR scenarios), where rear-end conflicts were found to decrease while a slight increase was observed in lane-change conflicts.
摘要：
绿灯最佳速度咨询（GLOSA）系统的移动性和环境效益已被许多先前的研究报告，然而,对此类应用程序的安全影响了解不足。为了安全部署GLOSA系统，在设计过程中识别和解决潜在的安全问题是最关键的。可以说，实施GLOSA系统可以通过减少与信号交叉口中断的交通流相关的交通冲突来提高安全性。然而,更多的研究结果需要从现场和基于模拟的研究来评估对安全的影响在各种现实世界的情景。作为欧盟地平线2020计划下的LEVITATE（互联和自动化车辆的社会水平影响）项目的一部分，这项研究的主要目的是研究GLOSA在混合交通组成下的安全影响，这些交通组成具有不同的互联和自动驾驶汽车（CAV）的市场渗透率（MPR）。本研究使用了大曼彻斯特地区的经过校准和验证的微观仿真模型（在Aimsun开发），其中确定了走廊中的三个信号交叉口以实施GLOSA系统。通过识别文献中发现的一些GLOSA算法中的潜在问题/限制来开发改进的算法。CAV的行为是基于综合文献综述的结果进行建模的。安全分析是由联邦公路管理局（FHWA）通过处理替代安全评估模型（SSAM）中的模拟车辆轨迹进行的。替代安全评估结果显示，仅在CAV的低MPR（20％）场景下，在测试网络的多个交叉点实施GLOSA的安全性略有改善，与各自没有GLOSA的情况相比。在具有40%或更高的第一代或第二代CAV的混合车队方案下，GLOSA实施在安全性方面没有观察到或略有下降。与没有GLOSA的情况相比。还发现GLOSA系统的实施对流量冲突类型有一些影响（尽管在所有MPR场景中并不一致），发现后端冲突减少，而变道冲突略有增加。
