An Integrated Simulation Environment to test the effectiveness of GLOSA services under different working conditions

Coppola, Angelo; Costanzo, Luca Di; Pariota, Luigi; Santini, Stefania; Bifulco, Gennaro Nicola

doi:10.1016/j.trc.2021.103455

Cited by 27 publications

(11 citation statements)

References 21 publications

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“…They are equipped with an ITS RoadSide Unit (RSU) for providing the Green Light Optimal Speed Advisory (GLOSA) service to the vehicles, equipped with an ITS OnBoard Unit (OBU). The service allows the connected vehicle to reach the green with an appropriate speed profile [ 10 , 11 , 12 ], for improving the riding comfort [ 13 ], saving time [ 14 ], and energy [ 15 ].…”

Section: Autonomous Intersection Management: Preliminariesmentioning

confidence: 99%

Autonomous Intersection Management: Optimal Trajectories and Efficient Scheduling

Mualla

Gaud

Calvaresi

et al. 2023

Sensors

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Intersections are at the core of congestion in urban areas. After the end of the Second World War, the problem of intersection management has benefited from a growing body of advances to address the optimization of the traffic lights’ phase splits, timing, and offset. These contributions have significantly improved traffic safety and efficiency in urban areas. However, with the growth of transportation demand and motorization, traffic lights show their limits. At the end of the 1990s, the perspective of autonomous and connected driving systems motivated researchers to introduce a paradigm shift for controlling intersections. This new paradigm is well known today as autonomous intersection management (AIM). It harnesses the self-organization ability of future vehicles to provide more accurate control approaches that use the smallest available time window to reach unprecedented traffic performances. This is achieved by optimizing two main points of the interaction of connected and autonomous vehicles at intersections: the motion control of vehicles and the schedule of their accesses. Considering the great potential of AIM and the complexity of the problem, the proposed approaches are very different, starting from various assumptions. With the increasing popularity of AIM, this paper provides readers with a comprehensive vision of noticeable advances toward enhancing traffic efficiency. It shows that it is possible to tailor vehicles’ speed and schedule according to the traffic demand by using distributed particle swarm optimization. Moreover, it brings the most relevant contributions in the light of traffic engineering, where flow–speed diagrams are used to measure the impact of the proposed optimizations. Finally, this paper presents the current challenging issues to be addressed.

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Section: Autonomous Intersection Management: Preliminariesmentioning

confidence: 99%

Autonomous Intersection Management: Optimal Trajectories and Efficient Scheduling

Mualla

Gaud

Calvaresi

et al. 2023

Sensors

View full text Add to dashboard Cite

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“…This solution can have positive impacts with regard to the economic and environmental aspects. In Coppola et al [2022], microsimulation has shown that GLOSA allows to save consumption of 5% of both electric and not electric vehicles. According to Treiber et al [2000], minimizing acceleration and brakings can save about 30% of fuel consumption and combustion engine related emissions.…”

Section: R E L a T E Dw O R K Smentioning

confidence: 99%

5G-Enabled Automated Truck Platoons in Urban Areas

Hadjidimitriou

Willenbrock

2022

Preprint

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Transport and logistic sector is expected to obtain large benefits thanks to the implementation of 5G. This work proposes a framework based on 5G technology for Automated Truck Platoons (ATP) in urban areas to reduce energy consumption and Greenhouse Gas (GHG) emissions. More specifically, 5G features such as low latency communication (uRLLC), massive Machine Type Communication (mMTC) and enhanced mobile broadband (eMBB) are deployed with the Green Light Optimal Speed Advisory (GLOSA) to achieve the optimal speed of Automated Truck Platoons. The results of a preliminary test implemented in the Port of Hamburg show that 5G GLOSA ATP is promising in terms of energy consumption and GHG emissions reduction and increased truck platoon stability.

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“…This may cause sharp accelerations or hard braking, and unnecessary idling time, which, in turn, cause additional fuel consumption and emissions ( 12 , 13 ). Therefore, a great deal of research efforts have focused on optimizing such driving behavior using CV technology ( 8–10 , 12 , 14 , 15 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The current literature has demonstrated the efficiency of SAS vehicles using various algorithms. Coppola et al ( 10 ) developed a method to test the green light optimal speed advisory (i.e., GLOSA) system in an integrated simulation environment. The method enables one to test multiple factors in the GLOSA system, including the traffic signal and phase, communication distance, minimum speed, and electric engines.…”

Section: Literature Reviewmentioning

confidence: 99%

“…There exists a large body of literature on SAS modeling and its impact analysis. However, the primary focus of the literature is placed only on traffic delay, fuel consumption, and emission savings ( 1 , 7–10 ). For practical applications, several field-based experiments have been conducted in restricted road environments, yet their focus is also limited to energy and emission savings ( 6 , 11 , 12 ).…”

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confidence: 99%

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Safety Benefits of Automated Speed Advisory Systems at Signalized Intersections

Saunier

Miranda-Moreno

2022

Transportation Research Record: Journal of the Transportation R

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Human-driving behavior at signalized intersections may lack efficiency because drivers try to reach their desired speed without the upcoming traffic-signal information. This causes idling time, sharp accelerations, hard braking, traffic congestion, emissions, and energy consumption. Connected vehicles, for example those equipped with a speed advisory system (SAS), can provide prior information to drivers for optimizing their driving behavior while approaching signalized intersections. However, the current literature focuses only on the fuel consumption, emissions, and travel-delay reduction impacts of SASs. This paper evaluates the safety impact of SAS vehicles using the proposed approach that simulates mixed-traffic situations between SAS and human-driven vehicles (HDVs). HDVs in the model follow real vehicle trajectories based on car-following conditions. The study investigates various scenarios including the impact of the different ranks of SAS vehicles in the vehicle group, the lane-changing possibility, and market penetration rates (MPRs). The results suggest that SAS vehicles can reduce rear-end collision risks from 25% MPR. The minimum time to collision increases by 1.2 s and the deceleration rate to avoid crash declines by 0.3 [Formula: see text] on average for 100% MPR relative to 0%. The study demonstrated that this safety benefit is also strongly related to the rank of SAS vehicles within a vehicle group. In addition, the conflict locations in the approaching lane gradually move away from the intersection up to where the communication range starts as the MPR increases, which would reduce abrupt vehicle speed changes near pedestrian crosswalks.

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An Integrated Simulation Environment to test the effectiveness of GLOSA services under different working conditions

Cited by 27 publications

References 21 publications

Autonomous Intersection Management: Optimal Trajectories and Efficient Scheduling

Autonomous Intersection Management: Optimal Trajectories and Efficient Scheduling

5G-Enabled Automated Truck Platoons in Urban Areas

Safety Benefits of Automated Speed Advisory Systems at Signalized Intersections

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