### Related Works  
In the literature, various methods have been employed to solve the problem of locating parking lots. These methods can be broadly categorized into optimization, metaheuristics, machine learning, and game theory. Optimization models are the most popular method used for EV parking lot allocation problems. [PERSON] et al. [11] addressed smart city charging station allocation for electric vehicles using analytic hierarchy process and multi-objective goal programming. The primary objective is to assign electric vehicles to the most suitable charging stations, taking into account four objectives: maximizing the energy requested by electric vehicles, minimizing total response time, reducing charging costs, and minimizing battery degradation. Several factors, such as travel distance, state of charge, grid-to-vehicle energy trading, time, road traffic, driver priorities, and charging station capacity, are taken into account to address this problem. [PERSON] et al. [12] study a multi-agent system for electric vehicle charging scheduling in parking lots. [PERSON], [PERSON], and [PERSON] [13] propose a decentralized optimization approach to the power management of electric vehicle parking lots. Metaheuristics are the second commonly reported method to address the parking lot allocation and sizing problem. A model for multi-objective site selection of electric vehicle charging stations based on the Non-Dominated Sorting Genetic Algorithm (NSGA)-II is proposed by [PERSON] and [PERSON] [14]. A multi-objective neural network algorithm (MONNA) for the optimal location of electric vehicle charging infrastructure in Tunis city is presented in [PERSON] et al. [15]. In [PERSON] and [PERSON] [16], a multi-objective algorithm based on the artificial bee colony (ABC) and firefly algorithms is proposed to optimally determine the number of parking lots to be allocated for plug-in hybrid electric vehicles (PHEVs) in a distribution system is proposed. In [PERSON] and [PERSON] [17], Gray Wolf Optimizer for optimal sizing and siting of energy storage systems in electric distribution networks is investigated. [PERSON] and [PERSON] [18] present a method based on calculating the probability of electric vehicles (EVs) entering each parking lot for the long-term planning of EV parking lots. [PERSON] et al. [19] perform optimal allocation of EV parking lots and DG in a micro grid using a two-stage genetic algorithm and particle swarm optimization (GA-PSO). [PERSON] et al. [20] developed a multi-objective optimization approach for the allocation of electric vehicle parking lots and smart charging with distributed energy resources. [PERSON] et al. [21] performs hybrid genetic algorithm-simulated annealing-based electric vehicle charging station placement for optimizing distribution network resilience. [PERSON] et al. [22] proposes an improved metaheuristic method based on fire hawks for the optimization of electric vehicle parking lots in a distribution network. [PERSON] and [PERSON] [23] studies the reliability-constraint allocation of EV parking lots is a radial distribution network. A genetic algorithm is utilized to achieve the best allocation of parking lots. The numerical results show that distribution feeders with no residential and commercial customers are not feasible to allocate parking lots. [PERSON] et al. [24] performs optimal electric vehicle charging and discharging scheduling using metaheuristic algorithms and proposes a V2G approach for cost reduction and grid support. Machine learning models for parking lot allocation have also gained traction in recent years. [PERSON], [PERSON], and [PERSON] [25] perform optimal management of parking lots using big data for electric vehicles via internet of things and Long Short-term Memory. [PERSON] et al. [26] address charging scheduling in a workplace parking lot and develop bi-objective optimization approaches through predictive analytics of electric vehicle users' charging behavior. [PERSON] et al. [27] provide a review on machine learning approaches for EV charging behavior.