Green Light Optimal Speed Advisory Research Articles

Predicting the Future Signalization of Traffic-Actuated Signals Using Extreme Gradient Boosting

Stops, braking, and acceleration maneuvers at traffic signals cause high fuel consumption and emission levels. Green light optimized speed advisory (GLOSA) can help to reduce a proportion of such unnecessary vehicle maneuvers. For this, GLOSA systems require reliable switching time estimations as input. However, estimating the switching times of traffic-actuated signals can be challenging. The signalization of traffic-actuated lights is adjusted to match the current traffic. Based on green-time requests, the status of signals can change with almost no lead time. This paper presents an approach for predicting the signalization of traffic-actuated signals. We exploit the limited nature of the space for switched signal state combinations at intersections, and express combinations of motorized traffic-related signal states as one feature to depict the motorized traffic signalization state (MTSSt) at an intersection. Predicting MTSSt-switches allows later determination of the switching times of individual signals. To conduct the predictions, the machine learning method “extreme gradient boosting” was used. A three-step methodology—data preparation, tuning the prediction procedure, and testing the approach—was applied and evaluated on the historical data of four traffic-actuated signalized intersections. The results showed that within a period of the next 30 s, signal changes were predicted with an overall precision and sensitivity of about 95% and an average mean absolute error of less than 1.1 s. In this period, the sequence of switched signals was predicted accurately to the second, without any deviation, in, on average, over 82% of cases.

Adaptive Frequency Green Light Optimal Speed Advisory Based on Deep Reinforcement Learning

Adaptive Frequency Green Light Optimal Speed Advisory Based on Deep Reinforcement Learning

Enhanced Traffic Light Guidance for Safe and Energy-Efficient Driving: A Study on Multiple Traffic Light Advisor (MTLA) and 5G Integration

This paper presents Multiple Traffic Light Advisor (MTLA), a novel Green Light Optimal Speed Advisory (GLOSA) system that leverages 5G communication technology. GLOSA systems are emerging as a key component in intelligent transportation systems, thanks to the development of effective communication technologies. At its core, MTLA serves as a guidance system for drivers, providing real-time instructions to adjust vehicle speed to optimize the utilization of current and future states of traffic lights along their route.The work addresses several limitations in the current state-of-the-art approaches, including the use of an overly simplified velocity profile, the omission of potential grip and jerk in problem formulation, and the absence of a detailed description of the algorithm’s implementation aspects. Initially, we comprehensively present an optimization-free implementation of the overall control architecture based on an unconventional speed profile. Subsequently, MTLA is improved within a non-linear Model Predictive Control (MPC) framework which uses the latter nonoptimal solution as an initial guess and considers potential grip and jerk in the problem formulation. The developed systems are numerically tested and compared within a high-fidelity simulation environment using the IPG CarMaker simulator. The results demonstrate promising performance in terms of energy savings, with a significant reduction of 37% in energy usage, as well as improved overall comfort with respect to the case where no guidance is given to the driver. These findings suggest a high potential for future developments in this domain.

SpeedAdv: Enabling Green Light Optimized Speed Advisory for Diverse Traffic Lights

SpeedAdv: Enabling Green Light Optimized Speed Advisory for Diverse Traffic Lights

Controlling Traffic Congestion in a Residential Area via GLOSA Development

The phenomenon of traffic congestion started in the second half of the twentieth century. This arose because of our society’s constant increase in demand for mobility. The excessive traffic of vehicles attempting to use the same infrastructure at the same time is what causes congestion. The consequences are well-known: delays, air pollution, reduced speed, and dissatisfaction (which may lead to risky maneuvers, reducing pedestrian and other driver safety). Our objective is to simulate the change in traffic patterns brought about by app users in residential areas (using navigational tools like Google Maps and Apple Maps), where the majority of navigational tools provide shortcuts that go through residential areas. In addition to discouraging navigation apps from directing drivers through residential areas during peak hours to mitigate pollution levels, by developing an algorithm based on the technology of Green Light Optimized Speed Advisory (GLOSA) and implementing it in a simulated environment (VISSIM), we can see the effect of changing the duration of red lights while keeping green lights constant. Overall, this solution can be implemented to change the times of traffic lights without the need for supplies, additional equipment, or warning signs because most cities’ traffic lights are already remotely controlled. In addition, this procedure is temporary to provide some freedom and does not adhere to the speed specified for drivers who wish to pass through residential areas outside of rush hour.

Multi-objective coordinated control strategy for mixed traffic with partially connected and automated vehicles in urban corridors

In the urban corridor with a mixed traffic composition of connected and automated vehicles (CAVs) alongside human-driven vehicles (HDVs), vehicle operations are intricately influenced by both individual driving behaviors and the presence of signalized intersections. Therefore, the development of a coordinated control strategy that effectively accommodates these dual factors becomes imperative to enhance the overall quality of traffic flow. This study proposes a bi-level structure crafted to decouple the joint effects of the vehicular driving behaviors and corridor signal offsets setting. The objective of this structure is to optimize both the average travel time (ATT) and fuel consumption (AFC). At the lower-level, three types of car-following models while considering driving modes are presented to illustrate the desired driving behaviors of HDVs and CAVs. Moreover, a trigonometry function method combined with a rolling horizon scheme is proposed to generate the eco-trajectory of CAVs in the mixed traffic flow. At the upper-level, a multi-objective optimization model for corridor signal offsets is formulated to minimize ATT and AFC based on the lower-level simulation outputs. Additionally, a revised Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is adopted to identify the set of Pareto-optimal solutions for corridor signal offsets under different CAV penetration rates (CAV PRs). Numerical experiments are conducted within a corridor that encompasses three signalized intersections. The performance of our proposed eco-driving strategy is validated in comparison to the intelligent driver model (IDM) and green light optimal speed advisory (GLOSA) algorithm in single-vehicle simulation. Results show that our proposed strategy yields reduced travel time and fuel consumption to both IDM and GLOSA. Subsequently, the effectiveness of our proposed coordinated control strategy is validated across various CAV PRs. Results indicated that the optimal AFC can be reduced by 4.1%–32.2% with CAV PRs varying from 0.2 to 1, and the optimal ATT can be saved by 2.3% maximum. Furthermore, sensitivity analysis is conducted to evaluate the impact of CAV PRs and V/C ratios on the optimal ATT and AFC.

Modified dynamic programming algorithms for GLOSA systems with stochastic signal switching times

A discrete-time stochastic optimal control problem was recently proposed to address the GLOSA (Green Light Optimal Speed Advisory) problem in cases where the next signal switching time is decided in real time and is therefore uncertain in advance. The corresponding numerical solution via SDP (Stochastic Dynamic Programming) calls for substantial computation time, which excludes problem solution in the vehicle’s on-board computer in real time. In this context, the present paper concentrates on the challenge of developing numerical algorithms to solve efficiently the stochastic GLOSA problem. As a first attempt to overcome the computation time bottleneck, a modified version of Dynamic Programming, known as Discrete Differential Dynamic Programming (DDDP) was recently employed for the numerical solution of the stochastic optimal control problem and was demonstrated to achieve results equivalent to those obtained with the ordinary SDP algorithm, albeit with significantly reduced computation times. After an outline of the DDDP approach, the present work considers a second modified version of Dynamic Programming, known as Differential Dynamic Programming (DDP). For the stochastic GLOSA problem, it is demonstrated that DDP achieves quasi-instantaneous (extremely fast) solutions in terms of CPU times, which allows for the proposed approach to be readily executable online, in an MPC (Model Predictive Control) framework, in the vehicle’s on-board computer. The novel numerical approach is tested and demonstrated by use of realistic examples and is compared to the SDP and DDDP solutions. It should be noted that DDP does not require discretization of variables, hence the obtained solutions may be slightly superior to the standard SDP solutions.

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

Green Light Optimized Speed Advisory (GLOSA) is an emerging smart transportation technology that guides drivers through signalized intersections during green light phases, showing promise in mobile source pollution control. However, there is still a lack of comprehensive understanding regarding its impact on driving behavior, fuel consumption, and CO2 emissions. In this study, we used heavy-duty diesel truck (HDDT) as an example and conducted a comprehensive path study from the changes in driving behavior to fuel consumption and CO2 emission reduction after the application of GLOSA, using a portable emission measurement system for real-world driving tests at two research scales: signal-controlled road section and signalized intersections. The study was innovatively conducted in three scenarios: Without GLOSA, With GLOSA, and GW GLOSA, which considers the simultaneous application of Green Wave (GW) traffic control system. Results show that applying GLOSA, the passing rate of vehicles at signalized intersections significantly improves, the driving cycle curve tends to be smoother, the cruise time ratio increases to 67.60%, and the travel time is reduced by 5%–13%. HDDT's average speed on the road segments (Seg) decreases by 25.15%, and the aggressive acceleration and deceleration behavior near signalized intersections are significantly reduced. The trip fuel consumption and CO2 emissions of HDDT decrease by 16.63%–23.51% and 16.06%–24.12%, respectively. These fuel savings and CO2 emission reductions mainly occur in the Seg and Signalized intersection-after sections. After the simultaneous application of GLOSA and GW, the trip average speed increases by 60.90%, and traffic efficiency is greatly improved. Although the fuel consumption and CO2 emissions are slightly higher compared to GLOSA alone due to the higher driving speed, GW GLOSA still offers relatively considerable fuel and environmental benefits. This study deepens people's understanding of GLOSA and GW and provides support for the formulation of transportation efficiency improvement and environmental protection strategies.

Energy-Saving and Punctuality Combined Velocity Planning for the Autonomous-Rail Rapid Tram with Enhanced Pseudospectral Method

Autonomous-rail rapid transit (ART) is a new medium-capacity rapid transportation system with punctuality, comfort and convenience, but low-cost construction. Combined velocity planning is a critical approach to meet the requirements of energy-saving and punctuality. An ART velocity pre-planning and re-planning strategy based on the combination of punctuality dynamic programming (PDP) and pseudospectral (PS) method is proposed in this paper. Firstly, the longitudinal dynamics model of ART is established by a multi-particle model. Secondly, the PDP algorithm with global optimal characteristics is adopted as the pre-planning strategy. A model for determining the number of collocation points of the real-time PS method is proposed to improve the energy-saving effect while ensuring computation efficiency. Then the enhanced PS method is utilized to design the velocity re-planning strategy. Finally, simulations are conducted in the typical scenario with sloping roads, traffic lights, and intrusion of the pedestrian. The simulation results indicate that the ART with the proposed velocity trajectory optimization strategy can meet the punctuality requirement, and obtain better economy efficiency compared with the punctuality green light optimal speed advisory (PGLOSA).

Optimization of Vehicle Trajectories Considering Uncertainty in Actuated Traffic Signal Timings

This paper introduces a robust green light optimal speed advisory (GLOSA) system for fixed and actuated traffic signals considering a probability distribution. These distributions represent the domain of possible switching times from the signal phasing and timing (SPaT) messages. The system finds the least-cost (minimum fuel consumption) vehicle trajectory using a computationally efficient A <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$^\ast$</tex-math> </inline-formula> algorithm incorporated within a dynamic programming (DP) procedure to minimize the vehicle’s total fuel consumption. Constraints are introduced to ensure that vehicles do not collide with other vehicles, run red indications, or exceed a maximum vehicular jerk for passenger comfort. Results of simulation scenarios are evaluated against empirical comparable trajectories of uninformed drivers to compute fuel consumption savings. The proposed approach produced significant fuel savings compared to an uninformed driver behavior amounting to 37% on average for deterministic SPaT and 30% for stochastic SPaT data. A sensitivity analysis was performed to understand how the degree of uncertainty in SPaT predictions affects the optimal trajectory’s fuel consumption. The results present the required levels of confidence in these predictions to achieve savings in fuel consumption. Specifically, the study demonstrates that the proposed system can be within 85% of the maximum savings if the timing error is ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\pm$</tex-math> </inline-formula> 3.3 seconds) at a 95% confidence level. Results also emphasize the importance of more reliable SPaT predictions as the time to green decreases relative to the time the vehicle is expected to reach the intersection given its current speed.

Designing a C-ITS Communication Infrastructure for Traffic Signal Priority of Public Transport

Looking ahead: transforming conventional public transport prioritization into C-ITS G5 services. The city of Frankfurt aims to digitize its public transport prioritization system in order to fulfill the requirements of future public transport communication standards and, moreover, to build on this very infrastructure for the development of imminent C-ITS services. Therefore, the communication systems of the mobility and transport provider VGF (Verkehrsgesellschaft Frankfurt am Main mbH) are being revised fundamentally by implementing new technologies for Car2X C-ITS G5 communication. The hardware components of the C-ITS system are strategically positioned with the help of a newly developed planning tool that identifies and determines the range of communication. For highly significant sites and locations of the hardware components, the calculated data are validated by utilizing measurements within a mobile setup. The operational stability and the development of previously unused potential are then carried out via the combination of the C-ITS services TSP (Traffic Signal Priority) and GLOSA (Green Light Optimized Speed Advisory). The overlay of the C-ITS services results in a high level of operational stability. As a result, potentials can be adequately employed through the sensible shifting of waiting times to the stops and a smooth flow of traffic through information on optimal speed and remaining times of the traffic light potentials. This paper presents a new methodology with which it is now possible to plan and evaluate C-ITS with regard to service distribution and radio propagation.

Assessing Connected Vehicle’s Response to Green Light Optimal Speed Advisory From Field Operational Test and Scaling Up

What are the main factors related to road or service configuration influencing the response behaviour of connected vehicles? How does it evolve with respect to the Market Penetration Rate (MPR) of connected vehicles? Here are some questions raised by this paper with a focus made on Green Light Optimal Speed Advisory (GLOSA) strategy. Such a system, based on V2I communication, aims at providing speed advice/recommendations when approaching an intersection to adjust speed and enhance fuel consumption. The message is displayed on the Human Machine Interface (HMI) of the connected vehicles and a response is expected from the driver. This paper derives its interest in the response behaviour of the driver to HMI. It develops a two-stage methodology based on (i) Field Operational Test to collect realistic inputs (e.g., response rate, delay, deceleration profile, etc.) and (ii) a simulated environment used for extending the findings to non-observed cases (e.g. higher MPR). Besides, the methodology that is well-fitted for generic evaluation and comparison of pilots sites&#x2019; conclusions, one further contribution lies in the process to select the explaining factors. Factors are targeted among features of (i) the road configuration (e.g. number of lanes), (ii) the service configuration (e.g. activation distance), or (iii) the individual route choice and traffic conditions. Among others, it is highlighted that the activation distance plays a significant role in the response behaviour and, depending on the cycle duration, a short activation distance might be completely inefficient, while a true environmental impact requires high MPR.

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

Green Light Optimized Speed Advisory (GLOSA) is being rapidly extended worldwide as an innovative technology for intelligent transportation. However, the energy consumption, pollutant emissions and the corresponding driving behaviors changes for plug-in hybrid vehicle (PHEV) before and after applying GLOSA have not been adequately studied. Here, we quantitatively analyze for the first time the environmental and energy benefits of PHEVs after GLOSA application by combining acquired real-road driving cycles (DCs) and chassis dynamometer tests. We found that GLOSA significantly reduced the frequency of PHEV engine starts and throttle openings by reducing the number of stops and starts, the intensity of acceleration and the ratio of acceleration to idle time, and shifted more engine and motor operating points into the efficiency zone. The improved powertrain operating conditions significantly reduced the energy consumption and pollutant emissions of PHEV. GLOSA helps to reduce energy consumption by 23.50%, 26.01% and 27.94% when the initial state of charge (SOC) of the PHEV is 20%, 50% and 100% respectively. When the initial SOC of the PHEV is 20%, GLOSA contributed to a reduction in CO2, CO, total hydrocarbons (THC), NOx and particle number (PN) emissions of 17.37%, 22.58%, 36.07%, 30.39% and 16.38%. Our study aims to highlight the key role of GLOSA in reducing the pollutant emissions and energy consumption of PHEVs, while demonstrating that GLOSA is a valuable option for future transportation retrofitting.

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

Green Light Optimized Speed Advisory (GLOSA) is a cutting-edge technology for intelligent transportation and is being increasingly applied on the ground worldwide. However, evaluating the environmental benefits of vehicles using GLOSA has become a challenge owing to the complexity and unpredictability of real-traffic. Here, a standardized environmental benefit evaluation framework for GLOSA is first proposed, which combined standard driving cycle (DC) database and high-precision emission models. The DC database was constructed using Markov chain approach. The database has certain randomness and diversity while considering the real driving characteristics. The plug-in hybrid electric vehicle (PHEV) CO2 and NOx emission models were constructed based on machine learning approach. These models have good generalization ability and prediction accuracy, with 0.87 and 0.80 R2 on the test set, respectively. In the constructed model, vehicle specific power (VSP) and state of charge (SOC) were identified as the most significant features affecting CO2 and NOx emissions. According to the evaluation results, the use of GLOSA reduces the emission factors of CO2 and NOx by 16.3% and 18.4%, respectively, when the initial SOC of PHEV is 20%. When the initial SOC is 50%, the emission factors of CO2 and NOx of the PHEV with GLOSA are reduced by 22.1% and 26.6%, respectively. The generality of this framework solves the different evaluation scales problem due to the randomness traffic. The framework serves the construction of intelligent and clean transportation while also assisting in the promotion, optimization, and recognition of GLOSA's limits.

SPaT/MAP V2X communication between traffic light and vehicles and a realization with digital twin

The present paper’s focus is the V2X (Vehicle-to-Everything) communication between traffic light controller and road vehicles. After a brief review on the state-of-the-art V2X communication technologies and their feasibility for practical implementation, a brief description of their potential is discussed providing a working solution for SPaT/MAP (Signal Phase and Timing and MAP as intersection geometry) standard based communication, specifically for automotive proving ground usage. The main outcome of the proposed solution is a full y flexible traffic control system applying industrial PLC while ensuring a standardized V2X protocol. As a demonstrative HiL (Hardware-in-the-Loop) example, exploiting the elements of the traffic management system at the ZalaZONE Automotive Proving Ground, a GLOSA (Green Light Optimal Speed Advisory) scenario is presented with the developed SPaT/MAP V2X communication realizing a global optimum traffic control solution. The more, the whole framework is designed in a way that it is capable of using mixed reality components for testing purposes and realizing a digital twin for traffic lights. The method of virtualization for V2X communication is introduced using a co-simulation framework of three common software tools (SUMO, Veins, and OMNeT++).

Heads-Up Green in Connected Traffic Signals

Information-sharing between traffic signals and connected vehicles can enhance the traffic conditions in signalized intersections. We propose a straightforward application, heads-up green, with the aim to inform queuing connected vehicles about a switch from red to green. In this way, the drivers’ reaction time is expected to be reduced and the number of vehicles that can pass the intersection during a cycle is thereby increased. Unlike the applications Green Light Optimal Speed Advisory (GLOSA), time-to-green and time-to-red, there is no need for predictions of future traffic signal states. Therefore, heads-up green can be used in traffic actuated and adaptive control strategies in signalized intersections without loss in precision. A simulation-based evaluation of a typical Swedish intersection with a traffic actuated controller is performed to evaluate the traffic performance. The main goal as been to show on the potential to use heads-up green to increase traffic efficiency in signalized intersections. Additionally, different reaction times to the information from heads-up green has been investigated. The results show that with heads-up green it is possible to improve the travel time by up to 15% at high demand levels when queues are frequently observed. The improvements are largest with 100% connected vehicles, but significant improvements of 5-15% are observed also for lower shares of connected vehicles. At high demand levels, travel times are decreased already at small reductions in the reaction time. Hence, there is a potential to improve traffic efficiency already when small changes in reaction time can be achieved with heads-up green.

Unified network tRaffic management frAmework for fully conNected and electric vehicles energy cOnsumption optimization (URANO)

Cooperative control in the presence of connected and automated vehicles has attracted substantial attention due to its pronounced benefits on the network compared with human-driven vehicles. They make possible a significant reduction of travel time/waiting time, energy consumption and emissions. In this context of new emerging technologies, traffic lights are still recognized as one of the most effective strategies in terms of energy and environmental benefits, which can be further improved by considering the integration with greener powertrains. The paper proposes a cooperative network traffic management framework for Electric Vehicles (EVs) based on a multi-objective optimization aimed at minimizing the total time spent (TTS) and energy consumption (EC) of EVs. Such framework is composed of i) a traffic control model that incorporates traffic lights design, ii) a traffic flow model to estimate TTS as a network performance indicator, and iii) an EVs model to estimate EC at the intersections. The EC function has been derived from a VT-CPEM model to simulate consumptions and thoroughly calibrated based on real-world individual trajectories. The optimization framework was implemented on a nine-node network and the results of the multi-criteria optimization (aiming at minimizing the TTS and EC of EV) are compared with results of the benchmark mono - criterion optimization (aiming at minimizing the TTS) and the mono-criterion optimization combined with the speed advisory (GLOSA; Green Light Optimized Speed Advisory). All the proposed analyses were carried out for different powertrain vehicle categories; ICEVs, and EVs.

Modeling Analysis of Automated and Connected Cars in Signalized Intersections

It is acknowledged that many of the problems related to urban congestion can be solved through the diffusion of automated vehicles capable not only of replacing drivers, but also of receiving information from the infrastructure. In this article, the effects of driverless cars (level 3–4 of automation) and of the Green Light Optimal Speed Advisory (GLOSA) system, a particular kind of Cooperative – Intelligent Transport System (C-ITS), will be evaluated at an urban signalized intersection through a set of micro-simulations. The aim of the paper is to analyze the two system as stand-alone before evaluating their jointed implementation, so to obtain their impacts and to analyze if and how they synergize for different levels of market penetration. The results of these simulations demonstrate that automated and connected cars should bring global benefits at intersections and also result in a first set of recommendations and best practices for the implementation of the systems in the short-medium term. Particular focus is given to the interaction between the equipped vehicles and traditional traffic, to frame the negative effects on the overall crossing both in Traffic Efficiency and Environment. Finally, the evaluation of a real crossing in Milan is performed and the results of the overall node are provided for different scenarios and time horizon.

Developing and Field Testing a Green Light Optimal Speed Advisory System for Buses

In this study, a Green Light Optimal Speed Advisory (GLOSA) system for buses (B-GLOSA) was developed. The proposed B-GLOSA system was implemented on diesel buses, and field tested to validate and quantify the potential real-world benefits. The developed system includes a simple and easy-to-calibrate fuel consumption model that computes instantaneous diesel bus fuel consumption rates. The bus fuel consumption model, a vehicle dynamics model, the traffic signal timings, and the relationship between vehicle speed and distance to the intersection are used to construct an optimization problem. A moving-horizon dynamic programming problem solved using the A-star algorithm is used to compute the energy-optimized vehicle trajectory through signalized intersections. The Virginia Smart Road test facility was used to conduct the field test on 30 participants. Each participant drove three scenarios, including a base case uninformed drive, an informed drive with signal timing information communicated to the driver, and an informed drive with the recommended speed computed by the B-GLOSA system. The field test investigated the performance of using the developed B-GLOSA system considering different impact factors, including road grades and red indication offsets, using a split-split-plot experimental design. The test results demonstrated that the proposed B-GLOSA system can produce smoother bus trajectories through signalized intersections, thus producing fuel consumption and travel time savings. Specifically, compared to the uninformed drive, the B-GLOSA system produces fuel and travel time savings of 22.1% and 6.1%, on average, respectively.

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

The Green Light Optimal Speed Advisory (GLOSA) system is a Cooperative Intelligent Transportation System (C-ITS) that is supposed to reduce the energy consumption and travel time associated with a vehicle trip by providing an optimal speed profile to avoid unnecessary stops at an intersection, based on Traffic Light Signals (TLSs) information. It is expected that GLOSA solutions will be widely deployed on transportation networks, thanks to the rapid spread of increasingly effective and pervasive communication technologies. Thus, appropriate methodologies and tools should be adopted to test sustainability objectives and evaluate the effects of such systems on the performance of traffic networks. In this perspective, this paper proposes an enhanced testing approach of GLOSA services based on an Integrated Simulation Environment that combines custom models developed in Matlab/Simulink with the SUMO traffic simulation environment, allowing both the traffic environment and vehicle dynamics to be represented with a suitable level of detail. The proposed approach can be used to cover several aspects which, in the existing technical literature, are: (i) not considered (traffic signal phase condition); (ii) rarely considered (electric engine); (iii) considered in a non-integrated way (traffic condition, TLS cycle duration, communication distance and minimum speed). Indeed, the considered factors pertain to different subsystems of the mobility environment (network and vehicles), and the proposed testing framework allows an equally detailed simulation of all, thus enhancing the accuracy of the results. To show the validity of the proposed approach, this study considers one controlled vehicle, equipped with a GLOSA system, travelling along a single route through a city centre including many TLSs. The simulation analysis deals with different levels of considered factors, assessed through Key Performance Indicators (KPIs) related to mobility and environmental indicators. Numerical results confirm that the proposed Integrated Simulation Environment can give valuable insights and suggestions to design a GLOSA system aiming to enhance both mobility and environmental performance. They also show that the factors in question affect system performance differently.