## 4 Eco-Driving Strategy for Connected Vehicles
### Numerical Modelling
Despite these developments, obtaining precise and accurate future SPaT information remains challenging due to technological barriers and the dynamic operation of actuated, coordinated, and adaptive traffic signals. As ITS technology advances, new possibilities may open up to address this challenge. Last but not least, regarding the dynamic eco-driving control for platoons of vehicles at signalized intersections, the concept has attracted the interest of some researchers. A primary approach to address the impacts of platooning involves precise identification of the leading vehicles for each phase and providing slightly varied guidance to each vehicle within the platoon. For example, in [59], an algorithm was designed to account for real-time signal information and traffic conditions, as well as group vehicles into platoons based on their permutations, and the simulation results showed significantly reduced fuel consumption and emissions while also minimizing travel time and improving traffic flow. Similarly, Ref. [60] developed algorithms by characterizing the optimal speed profiles for platoon-based optimization and highlighted the importance of accurately estimating the vehicle's position and speed again, especially for platooning scenarios. In addition to the homogeneous CAV fleet, the heterogeneous traffic flow, including both CAVs and Human-Driven Vehicles (HDVs), is also a hot issue that needs to be addressed urgently. In [61], a suggestion-based control framework based on MPC was proposed to optimize fuel efficiency in heterogeneous urban traffic. The authors considered that the recommended velocity from CAVs are non-binding with HDVs, which means that the driver of an HDV can choose to follow or not to follow the suggested velocity. In the simulation, this assumption was expressed as a certain probability \(\beta\). At last, the simulation results showed the proposed control strategy's efficacy. Even though research on dynamic eco-driving for platoons has received limited attention so far, it provides a basis for significantly improving the energy-saving and emission-reduction potential of existing models. Here, we conclude the main elements for developing eco-driving models near signal-ized intersections:  
* Optimization problem formulation and methods: Most proposed eco-driving systems employ mathematical programming to estimate optimal speed profiles for energy and/or traffic efficiency objectives. These objectives include improving energy efficiency (minimizing vehicle tractive force/fuel consumption [52; 54; 62]), traffic efficiency (minimize idling time [44; 50; 63]), or a combination of safety, energy consumption, emissions, and traffic flow efficiency objectives [47; 58; 61]. Generally speaking, for models that incorporate a fuel consumption model, energy efficiency calculations are integrated with optimal problem solutions, while for others, speed trajectories are derived from simulation tools and input into fuel consumption and emissions models. Simultaneously, various optimization frameworks have been proposed for different objectives. These include Model Predictive Control (MPC) approaches focused on trip time and kinetic energy loss [50], fuel-optimal speed profile estimations based on a linear blend of traffic efficiency and emissions [53], and optimal controllers based on the formation of tight and fast-moving platoons for fuel efficiency optimization [62]. * Analysis boundary: The analysis boundary for a dynamic eco-driving system typically includes the area of the road network where the system can affect CAVs. This area comprises both the upstream road section leading to the signalized intersection and the downstream section where the benefits of eco-driving strategies are realized [48]. * Vehicle dynamic model and energy model: In many research works, constant acceleration [49; 52; 53] and non-linear acceleration models [47; 64] were taken into account to connect with eco-driving services and were adopted to estimate optimal velocity.