The increase in the number of vehicles on roads has compounded the difficulty in parking them when people go out for movies, shopping, theatre, etc. Thus, this paper proposes smart parking system. The reservation of the parking slots can be made using this proposed smart parking system. Unlike the methods proposed in the existing research, the system proposed in this paper divides the parking area into 3 parts. One for conventional parking, one for vehicles with reservation and the other for the vehicles with and without reservation. Learning automata is used in the proposed system to determine the percentage of the parking area for conventional parking. In general, the proportionate of the slots for reserved parking, conventional parking and common slots need to be assumed. Learning Automata helps in determining this proportionate optimally. The AES-256 encryption algorithm is used to provide security for the details provided by the user during reservation process. Goodput value is maintained for each vehicle which increases or decreases the chances of getting a reservation. The time limit for the reservation of the parking slot is also maintained after which the reservation gets cancelled automatically. Markov Model is used to represent the system. The performance of the proposed algorithm, Learning Automata and Reservation based Secure Smart Parking System (LA-RSSPS) is simulated and evaluated in terms of average waiting time, search time, the probability with which the vehicles do not get parking slot when they do not have reservation and the probability with which the vehicles do not get reservation and is compared with ProNet and iERS. Results have shown that our scheme preforms better than the ProNet and iERS competing schemes. The comparison of analytical and simulations results are also presented.

道路上车辆数量的增加加剧了人们出门看电影，购物，剧院等时停车的困难。因此，本文提出了一种智能停车系统。可以使用此建议的智能停车系统进行停车位的预订。与现有研究中提出的方法不同，本文提出的系统将停车区分为三个部分。一种用于常规停车，一种用于有预定的车辆，另一种用于有和没有预定的车辆。在建议的系统中使用学习自动机来确定常规停车位的停车面积百分比。通常，需要假定用于预留停车位，常规停车位和公共停车位的比例是成比例的。学习自动机有助于最佳地确定此比例。AES-256加密算法用于为用户在预订过程中提供的详细信息提供安全性。每辆车都保持有实际值，这增加或减少了获得预订的机会。保留停车位的时间限制也保持不变，之后该保留将自动取消。马尔可夫模型用于表示系统。仿真算法的性能，学习自动机和基于预订的安全智能停车系统（LA-RSSPS）的性能，并根据平均等待时间，搜索时间，车辆不停车时没有停车位的概率进行评估有保留，车辆没有得到保留的可能性，并与ProNet和iERS进行比较。结果表明，我们的计划比ProNet和iERS竞争计划更好地执行。还介绍了分析结果和模拟结果的比较。