There are so many vehicles in the world and the number of vehicles is increasing rapidly. To alleviate the parking problems caused by that, the smart parking system has been developed. The parking planning is one of the most important parts of it. An effective parking planning strategy makes the better use of parking resources possible. In this paper, we present a feasible method to do parking planning. We transform the parking planning problem into a kind of linear assignment problem. We take vehicles as jobs and parking spaces as agents. We take distances between vehicles and parking spaces as costs for agents doing jobs. Then we design an algorithm for this particular assignment problem and solve the parking planning problem. The method proposed can give timely and efficient guide information to vehicles for a real time smart parking system. Finally, we show the effectiveness of the method with experiments over some data, which can simulate the situation of doing parking planning in the real world.
T. Litman @cite_4 presents a comprehensive implementation guide on parking management, including parking planning practices. The study of M. Idris @cite_5 reviews the evolution of parking space occupancy detection. The detection technology makes the status of the parking space available for the parking management system. S. Zeitman @cite_3 invents a communication system for the parking management system. They provide a method to establish communication between municipality, driver and parking spaces. S. Shaheen @cite_2 documents the research and feasibility analysis for the design and implementation of parking management field test. Their report gives a description of the parking field test and its technology in detail. R. Lu @cite_0 provides a solution for smart parking scheme through taking advantage of communication between vehicules. Our method is different since we study the problem of processing parking planning on a real time smart parking system, then sending the guide information to vehicles.
@cite_4 @cite_3 @cite_0 @cite_2 @cite_5
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Searching for a vacant parking space in a congested area or a large parking lot and preventing auto theft are major concerns to our daily lives. In this paper, we propose a new smart parking scheme for large parking lots through vehicular communication. The proposed scheme can provide the drivers with real-time parking navigation service, intelligent anti- theft protection, and friendly parking information dissemination. Performance analysis via extensive simulations demonstrates its efficiency and practicality. Keywords— Vehicular communications; smart parking; navi- gation; anti-theft; information dissemination; security & privacy In almost every major city in the U.S. and internationally, parking problems are ubiquitous. It is well known that the limited availability of parking contributes to roadway congestion, air pollution, and driver frustration and that the cost of expanding traditional parking capacity is frequently prohibitive. However, less research has addressed the effect of insufficient parking at transit stations on transit use. In the San Francisco Bay Area, parking has recently been at or near capacity at many of the 31 Bay Area Rapid Transit (BART) District stations with parking facilities. Smart parking management technologies may provide a cost-effective tool to address near-term parking constraints at BART transit stations. This report presents early findings from an application of advanced parking technologies to maximize existing parking capacity at the Rockridge BART station, which was launched in December 2004 in the East San Francisco Bay Area. The smart parking system includes traffic sensors that count the number of vehicles entering and exiting the parking lots at the station. A reservation system allows travelers to reserve spaces by Internet, personal digital assistant (PDA), phone, and cell phone. The real-time information obtained from the sensors and the reservation system is displayed on variable message signs (VMS) (on Highway 24 leading to the station) to alert drivers of parking space availability. Before and after surveys and focus groups will be used to evaluate the travel effects, economic potential, and system technology of the field test. This report consists of three major sections: * A literature review in which the effectiveness of different types and applications of smart parking management systems are evaluated; * A feasibility analysis, including focus groups, surveys, and observational analyses, which guides the development and initial evaluation of the smart parking field test; and * A smart parking project description, which includes the applied demonstration design and technology. We propose an effective CNN architecture for visual parking occupancy detection.The CNN architecture is small enough to run on smart cameras.The proposed solution performs and generalizes better than other SotA approaches.We provide a new training validation dataset for parking occupancy detection. A smart camera is a vision system capable of extracting application-specific information from the captured images. The paper proposes a decentralized and efficient solution for visual parking lot occupancy detection based on a deep Convolutional Neural Network (CNN) specifically designed for smart cameras. This solution is compared with state-of-the-art approaches using two visual datasets: PKLot, already existing in literature, and CNRPark-EXT. The former is an existing dataset, that allowed us to exhaustively compare with previous works. The latter dataset has been created in the context of this research, accumulating data across various seasons of the year, to test our approach in particularly challenging situations, exhibiting occlusions, and diverse and difficult viewpoints. This dataset is public available to the scientific community and is another contribution of our research. Our experiments show that our solution outperforms and generalizes the best performing approaches on both datasets. The performance of our proposed CNN architecture on the parking lot occupancy detection task, is comparable to the well-known AlexNet, which is three orders of magnitude larger. Thanks to the development of Mobile mapping systems (MMS), street object recognition, classification, modelling and related studies have become hot topics recently. There has been increasing interest in detecting changes between mobile laser scanning (MLS) point clouds in complex urban areas. A method based on the consistency between the occupancies of space computed from different datasets is proposed. First occupancy of scan rays (empty, occupied, unknown) are defined while considering the accuracy of measurement and registration. Then the occupancy of scan rays are fused using the Weighted Dempster‐Shafer theory (WDST). Finally, the consistency between different datasets is obtained by comparing the occupancy at points from one dataset with the fused occupancy of neighbouring rays from the other dataset. Change detection results are compared with a conventional point to triangle (PTT) distance method. Changes at point level are detected fully automatically. The proposed approach allows to detect changes at large scales in urban scenes with fine detail and more importantly, distinguish real changes from occlusions.