Dynamic Eco-Driving on Signalized Arterial Corridors during the Green Phase for the Connected Vehicles

Abstract

Inappropriate driving behaviours can result in additional fuel consumption and emissions. Drivers can be informed of the accurate signal phase and timing (SPaT) and distance information of the current intersection and downstream intersections via vehicle-to-everything (V2X) communications. The real-time information has been utilized to assist drivers in taking reasonable manoeuvres and gain lots of benefits on fuel consumption and emissions in some existing studies. In order to cooperatively address the optimization problem on the signalized arterial corridors, this paper presents an eco-driving optimization model considering preceding SPaT and position information. This model can be applied to pass two successive traffic signals cooperatively during green phase. In this study, a multi-stage optimal approach is proposed to minimize the fuel consumption. Field experiments are carried out for comparative analysis between the connected vehicle with speed advisory and the uninformed vehicle without speed advisory. The results indicate that the fuel saving of the connected vehicle guided by the dynamic optimization algorithm shows significant improvement. In addition, the rolling optimization among three signalized intersections is conducted and the results show that a considerable improvement can be obtained compared with the one-by-one optimization.

Navigating Electric Vehicles Along a Signalized Corridor via Reinforcement Learning: Toward Adaptive Eco-Driving Control

“…The dynamic information of the possible front vehicle is supposed to be obtained by the ego vehicle for safety, and it is attainable by utilizing vehicle-to-vehicle (V2V) communication or is provided by RSUs, considering that RSUs can estimate the speed and position of the vehicles on the road. Furthermore, some related works only consider the information about the first downstream traffic signal, whereas the cooperation of two continuous intersections can further optimize the results ( 12 ). Thus, this paper takes two downstream successive traffic signals to be a part of the state.…”

“…The EVs equipped with communication devices only need to make optimum decisions according to the information about traffic state and SPaT message when approaching the intersections. More precisely, in most existing published works the term ''decision'' refers to the adjustment of longitudinal movement of the vehicle, and they focus on motion planning or speed guidance without lane changing (11)(12)(13)(14). Therefore, this paper also focuses on the speed profile generating task for EVs in a cooperative vehicle-infrastructure system (CVIS), which provides an environment for vehicles and roadside units (RSUs) sharing the information to achieve efficient collaboration (15,16).…”

“…As for the traffic scenarios near signalized intersections, there are also several proposed methods. The goal of economy is often achieved by the optimal control model ( 11, 17 ), heuristic method ( 12, 18, 19 ), or optimization model ( 20 ). In these cases where SPaT information is given, the speed profile of the vehicle can be generated by the algorithm.…”

Modeling Automated Driving in Microscopic Traffic Simulations for Traffic Performance Evaluations: Aspects to Consider and State of the Practice

“…Their findings can be used to validate the findings from traffic simulation studies. In Zhao et al [36] vehicles connected to signalized intersections in an arterial corridor receiving speed advise had a significant reduction in fuel consumption and emissions compared to nonconnected vehicles. Qi et al [37] conducted a field test in an interstate corridor in the U.S. to evaluate overall traffic conditions, safety and the operational impacts of a VSL system on both connected and non-connected vehicles, and found that the VSL system improved traffic operation even at a low penetration rate of connected vehicles.…”

An efficient path planning GLOSA‐based approach over large scale and realistic traffic scenario

“…The proposed algorithm shows better energy saving compared to other tested optimization methods. Reference 9 presented a dynamic eco‐driving optimization method based on GLOSA and trajectories optimization via evaluating the fuel consumption per trip. The proposed eco‐driving model showed a significant improvement in fuel consumption.…”