Urban vehicle emissions are one of the main contributors to air pollution since most vehicles still rely on fossil fuels, despite the growing popularity of alternative options such as hybrids and electric cars. Recently, Artificial Intelligence (AI) and automation-based controllers have gained attention for their potential use in adaptive traffic signal control. Many studies have been conducted on the application of Deep Reinforcement Learning (DRL) models to reduce travel time in adaptive traffic signal control. However, limited research has been done on adapting traffic signal control to reduce CO2 emissions and fuel consumption in urban vehicles. As such, this work proposes a digital-twin-based adaptive traffic signal control approach that relies on a digital twin of urban traffic network and uses the DRL Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to optimise for reduced fuel consumption and CO2 emission. The system is designed to simulate different traffic scenarios and control strategies, enabling for adaptation in traffic signal adjustments. To assess the effectiveness and applicability of the proposed approach, a quantitative simulation is performed using synthetic and real-world traffic datasets from a multi-intersection network in a neighbourhood in Amman, Jordan, during peak hours. The findings suggest that the DRL approach based on digital twins on synthetic networks can reduce CO2 emissions and fuel consumption even when using a basic reward function based on stopped vehicles.

Design-time evaluation is essential to build the initial software architecture to be deployed. However, experts’ assumptions made at design-time are unlikely to remain true indefinitely in systems that are characterized by scale, hyperconnectivity, dynamism, and uncertainty in operations (e.g. IoT). Therefore, experts’ design-time decisions can be challenged at run-time. A continuous architecture evaluation that systematically assesses and intertwines design-time and run-time decisions is thus necessary. This paper proposes the first proactive approach to continuous architecture evaluation of the system leveraging the support of simulation. The approach evaluates software architectures by not only tracking their performance over time, but also forecasting their likely future performance through machine learning of simulated instances of the architecture. This enables architects to make cost-effective informed decisions on potential changes to the architecture. We perform an IoT case study to show how machine learning on simulated instances of architecture can fundamentally guide the continuous evaluation process and influence the outcome of architecture decisions. A series of experiments is conducted to demonstrate the applicability and effectiveness of the approach. We also provide the architect with recommendations on how to best benefit from the approach through choice of learners and input parameters, grounded on experimentation and evidence.

Context: Evaluating software architectures in uncertain environments raises new challenges, which require continuous approaches. We define continuous evaluation as multiple evaluations of the software architecture that begins at the early stages of the development and is periodically and repeatedly performed throughout the lifetime of the software system. Numerous approaches have been developed for continuous evaluation; to handle dynamics and uncertainties at run-time, over the past years, these approaches are still very few, limited, and lack maturity. Objective: This review surveys efforts on architecture evaluation and provides a unified terminology and perspective on the subject. Method: We conducted a systematic literature review to identify and analyse architecture evaluation approaches for uncertainty including continuous and non-continuous, covering work published between 1990–2020. We examined each approach and provided a classification framework for this field. We present an analysis of the results and provide insights regarding open challenges. Major results and conclusions: The survey reveals that most of the existing architecture evaluation approaches typically lack an explicit linkage between design-time and run-time. Additionally, there is a general lack of systematic approaches on how continuous architecture evaluation can be realised or conducted. To remedy this lack, we present a set of necessary requirements for continuous evaluation and describe some examples.