Estimating the Impact of Green Light Optimized Speed Advisory (Glosa) on Exhaust Emissions Through the Integration Ofvissim and Moves

This study introduces a green light speed advisory strategy in a connected vehicle environment, which helps vehicles avoid unnecessary stopping before intersections and improves fuel consumption efficiency. A number of existing studies have worked on Green Light Optimal Speed Advisory (GLOSA), but most of them focus on the macro level, ignoring the process of speed changes. Rakha et al. considered the acceleration process in his research, but there is no simulation and field data to support his results, and fuel consumption was considered in terms of total consumption in the time period, which is not reasonable. When vehicles approach intersections at different speeds, the fuel consumption will be different. This paper formulates explicit fuel consumption objective functions based on the VT Micro model to measure fuel consumption for different speed profiles. A strategy is developed to reduce fuel consumption. Over 100,000 runs of the experiment have been conducted in MATLAB and the results show that the strategy can save up to 56% of fuel if the vehicle follows the system’s speed advice. The average fuel savings for green light signals is 56% and for red signal scenarios it is 21%. An android app has also been developed to collect field data in the connected vehicle environment. The results from the field show that vehicles with GLOSA reduce fuel consumption by 13% compared to vehicles without GLOSA.

Green light optimized speed advisory achieves fuel savings and CO2 emission reduction by profoundly impacting driving behavior

Energy saving and emission reduction effects from the application of green light optimized speed advisory on plug-in hybrid vehicle

Should drivers be informed about the equipment of drivers with green light optimal speed advisory (GLOSA)?

In recent years, rapid technological breakthroughs in infrastructure-to-vehicle, vehicle-to-vehicle, and vehicle-to-infrastructure wireless communications have created the possibility of new concepts for traffic signal control. The green light optimized speed advisory (GLOSA) approach uses traffic signal control information and the current position of a vehicle to recommend an appropriate speed for each vehicle, reducing the number of stops at traffic signals. Previous research mainly focused on the impact of GLOSA on the performance of movements on major streets. The purpose of this paper is to examine the effects of GLOSA on delay, capacity, and surrogate safety measures for vehicles that arrive from unsignalized side driveways and access roads. A highly calibrated and validated Vissim simulation model of a five-intersection segment of 3500 South, an urban corridor in Salt Lake City, Utah, was used as a test environment. Scenarios of various traffic loads and GLOSA activations have been defined and simulated. Outputs from Vissim were used to measure the impact of GLOSA. Vehicular trajectory files were postprocessed in the FHWA Surrogate Safety Assessment Model to analyze vehicular conflict data and thereby evaluate the traffic safety effects of GLOSA. The findings show that GLOSA often significantly affects delay of the side-street traffic. However, in general, GLOSA has only a minor impact on the number of conflicts. Regarding the impact of type of traffic control on GLOSA’s operations, the findings show that the fixed-time signals work better than actuated–coordinated signals, and road geometry and proximity of the signalized intersections affect the impact of GLOSA on the side-street traffic.

International audience

Standard environmental evaluation framework reveals environmental benefits of green light optimized speed advisory: A case study on plug-in hybrid electric vehicles

Exploring GLOSA systems in the field: Technical evaluation and results

The highest fuel consumption on urban arterials is associated with driving in congested traffic, characterized by higher speed fluctuations and frequent stops at intersections. One way to reduce excessive stop-and-go driving on urban streets is to optimize signal timings. More recently, new methods in traffic signal optimization have incorporated changes in drivers' behavior to achieve optimum performance at signalized intersections. Connected vehicles technology provides a two-way wireless communication environment enabling vehicle-to-vehicle and vehicle-to-infrastructure communications, which can be used for a variety of mobility and safety applications. One such application is called the green light optimized speed advisory (GLOSA). This system uses timely and accurate information about traffic signal timing and traffic signal locations to guide drivers (through infrastructure-to-vehicle communication) with speed advice for a more uniform commute with less stopping time through traffic signals. A GLOSA implementation was evaluated for two types of traffic signal timing: predictable fixed-time signal timing and unpredictable actuated–coordinated signal timing. A two-intersection traffic network was modeled in VISSIM to achieve trustworthy results calibrated in the field. A comprehensive modal emission model was used to accurately estimate emissions. Experiments included various infrastructure-to-vehicle penetration rates and GLOSA activation frequencies. Results indicated that actuated–coordinated signal timings were not dependable for use in GLOSA systems. For fixed-time signals, higher penetration rates and more frequent GLOSA activations resulted in better traffic performance. GLOSA caused only minor improvements in fuel consumption, and average delay in vehicles stopped was improved significantly.

Cooperative Intelligent Transport Systems (C-ITS) aim to provide innovative solutions that can contribute to a better road management. Green Light Optimal Speed Advisory (GLOSA) is one of the traffic efficiency ITS services that can enhance traffic fluidity and fuel consumption economy. In this paper, we present driver reaction impact on GLOSA performance through a realistic simulation scenario taking into account the variation of penetration rate. In our work, we use vehicular network architecture ITS-G5 and we test our simple segment approach algorithm under a realistic traffic topology. Results show that driver reaction can significantly influence GLOSA performance in terms of fuel consumption and number of stopped vehicles.

The problem of how to adjust speed of vehicles so that they can arrive at the intersection when the light is green can be solved by means of Green Light Optimal Speed Advisory (GLOSA). The existing GLOSA approaches are single segment, that is, they consider traffic lights independently by providing vehicles with the optimal speed for the segment ahead of the nearest traffic lights. In this article we introduce a new approach-a multi segment GLOSA-according to which several lights in sequence on a vehicle's route are taken into account. The speed optimisation process is performed using a genetic algorithm. We assume that a vehicle has access to all traffic light phase schedules that it will encounter on its route. The route is composed of segments divided by traffic lights. The proposed GLOSA provides a driver with speed advisory for each segment according to selected preferences like minimisation of total traveling time or fuel consumption. We demonstrate, that in free-flow conditions such multi-segment GLOSA results in much better results when compared with single-segment approach.

Rapid expansion of 5G affects a number of sectors, including vehicular communications relying on cooperative intelligent transportation systems (C-ITS). More specifically, in the context of the Internet of Vehicles (IoV), a particular emphasis is placed on modern cellular V2X (C-V2X) technologies aiming to further improve road safety. This work originates from the detailed scope of the ongoing 5G-DRIVE research project promoting cooperation between the EU and China, with the aim of demonstrating IoV services that rely on vehicle-to-infrastructure (V2I) communications. With the C-V2X approach serving as a point of departure, we analyze and describe a specific green light-optimized speed advisory (GLOSA) use case, for which we provide a detailed descriptive framework, a proposed architectural framework for trials, as well as specific KPIs for the joint assessment of trials between the EU and China. We also discuss the context for performance test procedures to be conducted as part of the intended trials. GLOSA provides end-users with short-term information on upcoming traffic light status to optimize traffic flows, help prevent speed limit violations, improve fuel efficiency, and reduce pollution

An autonomous car is a self-driving car that is to keep the human being out of the car and to relieve them from the task of driving. An autonomous car can make more convenient, safer, and less energy intensive. In addition, Green Light Optimal Speed Advisory (GLOSA) systems reduce the travel time and CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emission. In this paper, we propose a novel GLOSA, called R-GLOSA system to support to autonomous cars. We assume that an autonomous car can access to all traffic light schedules that it will encounter on its route. The route is divided into each segment according to traffic lights. In each segment, an autonomous car can communicate to Road Side Units (RSUs) distributed among the road. An autonomous car collects the road information transmitted by an RSU and then, it optimizes speed in order to arrive at the intersection when the light is green. The R-GLOSA system provides an autonomous car with speed advisory for each RSU's coverage. The simulation results show that an autonomous car using R-GLOSA system outperforms in terms of travel time and waiting time compared to using single-segment and multi-segment GLOSA system.

Hardware-in-the-Loop and Road Testing of RLVW and GLOSA Connected Vehicle Applications

Vehicle speed and trajectories play an important role in influencing fuel consumption, gas emissions and travel time. ITS services are often proposed in order to reduce the impact of these issues on the environment and the comfort of drivers. Cooperative Intelligent Transport Systems (C‐ITS) introduced many services that target traffic efficiency management and road safety. Green Light Optimal Speed Advisory (GLOSA) is one of the traffic efficiency services that can significantly cut fuel consumption and decrease waiting time while crossing intersections. When approaching a signalized intersection, GLOSA informs the driver of an advisory speed to respect in order to reach the intersection while the traffic light is green. In this paper, we propose an Efficient Path Planning GLOSA‐based (EPP‐GLOSA) approach using Dijkstra greedy algorithm and GLOSA service. EPP‐GLOSA aims at proposing the fastest path for the driver to follow in order to reach its destination. The estimated duration to reach the destination takes into consideration the state of traffic lights along the path on top of Dijkstra algorithm. The proposed approach is evaluated under a large scale scenario in free flow and rush‐hour conditions. Our findings show that EPP‐GLOSA offers a balanced scale between travel time reduction and fuel saving especially in free flow conditions.