## 2 Related Work  
In recent decades, cities have faced unexpected socioeconomic crises, such as the increase in the world's population, urban growth, and migration from rural areas to urban centers. More than 50% of the world's population lives in cities [5]. By 2050, the United Nations (UN) predicts this number to reach 70%. The rapid transition to a highly urbanized population raises the demand for new infrastructures for cities needed to provide essential services for citizens, i.e., healthcare, education, transport, safety, reduction of greenhouse gas emissions, and sustainable energy and water. The significant growth of the population in urbanized cities particularly increases the number of vehicle owners and hence impacts both the environment and the cost of transport. The transport sector is one of the largestcontributors to greenhouse gas (GHG) emissions and excessive consumption of energy resources [6]. According to the European Environment Agency [7], GHG emissions from road transport vehicles still account for around 93% of emissions from the transport sector. In addition, the world's total energy consumption will increase by almost 50% by 2050 according to the IEO2019 Reference case [8]. Energy consumption in the transport system generally accounts for a share of a country's total energy consumption. It will increase by almost 40% by 2050 [8]. In the pursuit of mitigating energy consumption and CO\({}_{2}\) emissions in the transport sector, specific attention has been directed to this endeavor [9]. Eco-driving emerges as a cost-effective and immediately implementable strategy to significantly curtail both fuel consumption and emissions [10]. Factors such as acceleration/deceleration, driving speed, route selection, and idling, which fall within the control of a driver during operation, play pivotal roles in influencing fuel consumption and emissions [11; 12]. Commonly employed methods to instill eco-driving skills encompass training programs and the utilization of in-vehicle feedback devices, and whereas immediate and substantial reductions in fuel consumption and CO\({}_{2}\) emissions have been observed, accompanied by a slight increase in travel time [13], the sustained impact of these methods may diminish over time due to ingrained driving habits developed over the years. This underscores the imperative need to formulate quantifiable eco-driving recommendations and integrate them into vehicle hardware to ensure consistent and uniform improvements. Vehicle routing problems (VRPs) and their various iterations constitute a distinguished category of network problems that have garnered significance over the years, particularly for their pragmatic approach to addressing logistical challenges [14]. The primary objectives within this domain revolve around minimizing operational time, cost, or both for vehicles en route to their designated destinations. Variations in the problem have been introduced over time through alterations in the formulation of the basic routing problem. Objectives range widely, including the minimization of distance, travel time, fuel consumption, pollution, and others, adapting based on the specific application [15]. Despite offering a direct competitive advantage, these algorithms often operate on generalized methodologies with limited situational awareness. Notably, the expertise, sentiments, and situational adaptability of drivers are frequently excluded from these approaches. Several strategies have been proposed to look over these problems from different approaches including traffic and energy demand management, improving vehicle technologies, and integrating Information and Communication Technologies (ICT) [16; 17; 18]. The application of machine learning and Artificial Intelligence (AI) approaches to the transport sector, i.e., ITS, has significantly contributed to enhancing safety, efficiency, comfort, and environmental impacts on this sector.