Research On Parking Users Oriented Information Recommendation Model

Along with the accelerating urbanization and increasing car ownership, urban traffic is becoming more and more crowded, and parking lots resources have become severity shortage. The problem that cannot find parking lot has become a serious problem in driver’s travel, which restrict the development of the economy. At the same time, along with a significant increase in the number and scale of the construction of the parking lots, parking demand of driver is also increasing day by day, and the problem of parking inefficient is becoming more and more serious. At present, the intellectualized degree of parking lot is generally not high, the existing parking guidance system provides only location guide, unable to accurately parking space guidance. Similarly, when the drivers returns to get the vehicle, they can’t find the car easily on account of the large scale of parking spaces and the similar scenes and markers. The parking lot operators also hope to help drivers find the area as soon as possible, accelerating the vehicle parking turnover, improving utilization and parking revenues.First of all, the research background and current situation of intelligent parking in the area of Intelligent Transportation System(ITS) are summarized in this paper. Combined with the actual application scenario, intelligent parking can be divided into three stages. In order to solve the problem that the drivers can’t find the optimal parking lot timely, a reservation based optimal parking lot recommendation model in Internet of Vehicle(IOV) environment is designed. Based on the users oriented parking information recommendation system, the model considers subjective demands of drivers comprehensively, makes a deeply analysis of the evaluation indicators. This recommendation model uses a phased selection method to calculate the optimal objective parking lot. The first stage is screening which based on the users’ subjective parking demands; the second stage is processing the candidate parking lots through multiple attribute decision making. Simulation experiments show that this model can effectively solve the problems encountered in the process of finding optimal parking lot, save the driver’s parking time and parking costs and also improve the overall utilization of parking facilities to ease the traffic congestion caused by vehicles parked patrol.Secondly, in order to deal with the issue of parking space choice in the parking lot, we design an intelligent terminal oriented optimal parking space recommendation model. Based on deeply analyzing users’ requirements, the model selects walking distance, driving distance, parking security, type of available parking space, status of available parking spaces and status of the lane as evaluation index. These evaluation indexes are regarded as several properties of each free parking space and used to calculate the optimal parking space. Considering the instantaneity of real-time road traffic status in the parking lot, the whole resources in the parking lot is integrated to dynamically recommend the optimal parking space. In order to describe the implementation model of parking spaces information recommendation and solve the problem of information exchange between systems and users, an optimal parking space recommendation system framework oriented intelligent terminals is designed. Instance validation and analysis shows that the model can dynamically recommend the optimal parking spaces, save the users’ parking time and improve the utilization of parking facilities.To deal with the reverse car-searching issue in large buildings and parking lots, a Quick Response Code(QR code) based reverse car-searching route recommendation model is designed. By scanning the deployed QR codes, a smartphone can pinpoint the host location and parking location efficiently. Based on the submitted location information, the central control system can finally return the recommended routes, which facilitates a host to reach the parking location effectively. In our model, the reverse car-searching route is divided into two parts: choosing the optimal exports(elevator) and computing the shortest walking distance route. Based on the optimal export selection algorithm and regional shortest path algorithm, our model can choose the prior exports(elevator) effectively, and then recommend the optimal walking route in the buildings and parking lots. The simulation shows that this low-cost system can effectively solve the reverse car-searching problem in large buildings and parking lots, save the driver’s car-searching time and improve the utilization rate of parking facilities.