Signal Green Time Estimation Method for Connected VehicletoInfrastructure Applications Connected and autonomous vehicles (CAV) are becoming more integrated with traffic signal infrastructure for V2I applications, such as traffic light indication and automated driving. However, modern traffic signal controllers allocate green time using vehicle sensors and therefore the anticipated green time has significant stochastic variation. This study develops a methodology to characterize green time stochastic variation for actuatedcoordinated operation. During the peak hours where the demand was highly consistent, green intervals can be predicted with high certainty. In contrast, during midday and late evening, stochastic variation increased significantly due to the varying arrival patterns and associated realtime responsiveness of the traffic signal controller. The statistical characterization methods presented in this paper are important for green light optimized speed advisory (GLOSA) and ecodriving, technologies that rely on having an accurate estimate of the beginning of green (BOG) and end of green (EOG). Prior knowledge on typical values of how early to stop or shutdown the vehicles at a traffic signal approach can significantly improve efficiency and manage emissions for CAV. The paper concludes with a proposed graphical performance measure chart that can be used by traffic engineers and automotive vendors to frame the discussion on traffic signal operation.

Toward a V2Ibased Solution for Traffic Lights Optimization The demographic trends and the consequent increase of urbanization lead us to answer to the growing need for mobility, by ensuring high levels of safety and travel comfort, as well as consumption reduction. In this regard, the optimal design of traffic light settings plays a fundamental role in the definition of solutions to achieve a safe and sustainable mobility in urban environments. The efficient operation of the traffic light network involves different approaches that can be classified in terms of the considered network configuration, modelling framework and strategy. In this paper, a vehicletoinfrastructure (V2I) cooperation for traffic management is considered, in order to maximize the intersection capacity. Thus, the flow variability detected through V2I communications is taken into account by the responsive traffic light plans. The proposed strategy is compared with a video analyticsbased solution performed by cameras monitoring the intersection, with the aim to evaluate the performance robustness of both camera and V2Ibased approaches. Our numerical results reveal that outdoor conditions severely impact the quality of camerainformation inferred from the captured images, while V2Ibased solution is able to achieve better performances in terms of intersection capacity. A Channel occupancy evaluation is also conducted in order to further validate the proposed solution.