Project of an Intelligent Recommender System for Parking Vehicles in Smart Cities

Every trip by vehicle requires parking at the destination, so parking is an extremely important part of the transport systems of settlements. Parking space is usually one of the first impressions that people have when traveling to their destination. Convenient and affordable parking is considered a sign of welcome. Parking, which is difficult to find, inadequate, inconvenient or expensive, will disappoint users and may facilitate relocation. As a result, insufficient parking spaces can create problems for both motorists and ordinary city dwellers. Quite often when designing new buildings, shopping malls or certain other places, the question of the right amount of parking is not resolved correctly. This is due to a reluctance to address this issue, as well as a lack of information or a failed analysis of the same information. This is happening with a large number of projects in the modern world. With the right use of information, problem analysis and structural approach when planning parking, such problems could be avoided. The intellectual recommender system of public transport parking analyzes the area, processes certain user statistics and generates recommendations on the possibilities or needs of a project. To date, there is no system that can provide such information. Creating such a system and further improving it would greatly help in designing a more efficient city. Developed system analyzes a certain location, statistical data (for example, population, share of car ownership, location availability, features of work, etc.) giving the user advice on the required number of parking spaces in a particular region. Based on these data, you can build an expert assessment of the situation, work on further action to resolve this issue, such as increase or decrease the number of parking spaces in this location, plan further development of the region according to the parking requirements, redirect traffic or use certain alternative modes to reduce traffic.