Parking Guidance Information System Research Articles

Case-Based Reasoning Application for Parking Guidance Systems

Parking activities are one of the major problems in modern cities' transportation management systems. It is a complex and expensive process, and its price is determined not only by service tariffs, but also by resources invested in finding and traveling to vacancy spaces. Parking problems in larger cities can lead to increased air pollution, noise, driver time losses, stress and congestion. Parking guidance information systems (PGIS) have been successfully used to improve services and reduce harmful effects. Using case-based reasoning methodology in parking management and guidance systems could improve services and reduce required resources. Data collected from the existing drivers’ experience can be used by PGIS to generate solutions in free parking space search.

Short-Term Prediction of Available Parking Space Based on Machine Learning Approaches

Reliable short-term prediction of available parking space (APS) is the basic theory of parking guidance information system (PGIS). Based on the Intelligent parking system at the Eastern New Town, Yinzhou District, Ningbo, China, this study collected the data of parking availability in the on-street parking areas. The variation characteristics of APS were investigated and analyzed at different spatial-temporal levels. Then the APS prediction models based on Gradient Boosting Decision Tree (GBDT) and Wavelet Neural Network (WNN) were proposed. Furthermore, an improved WNN algorithm with (WA) decomposition and Particle Swarm Optimization (PSO) were presented. The original time series was decomposed and reconstructed by wavelet analysis, and the WNN algorithm found the optimal threshold of initial weight through PSO. The result of GBDT (weekday: MSE = 27.37, SMSE = 0, TIME = 35min, weekend: MSE = 9.9, SMSE = 0, TIME = 35min) and WA-PSO-WNN (weekday: MSE = 14.93, SMSE = 1.88, TIME = 160.32s, weekend: MSE = 12.33, SMSE = 10.23, TIME = 160.95s) approximated the true value. But the prediction time of GBDT was too long to be applicable to the short-term prediction of APS in this paper. Compared with the methods of GBDT, WNN, and PSO-WNN, the WA-PSO-WNN algorithm performs much better. The average differences in MSE between WA-PSO-WNN and GBDT for weekday and weekend data are 45.45% and 58.76%, respectively, indicating that WA-PSO-WNN can increase the prediction accuracy of weekday and weekend data by an average of 45.45% and 58.76% compared with the GBDT model. Finally, the application prospects of short-term APS forecasting were also discussed in reducing cruising parking behavior, reducing illegal parking behavior and adjusting dynamic parking rates to verify the importance of APS short-term forecasting.

Smart QR Based Parking System

Today parking management is a big issue in our society because of rapid growth in a number of vehicles day by day so in order to manage this problem, there has to be a solution, which helps getting rid of problems arising due to the lack of a proper parking management system. Hence finding a parking space has become an unpleasant experience in many metropolitan cities. There are many parking management systems like PGIS (Parking Guidance Information Systems), GIS (Geographic Information System), etc. These systems provide the driver with a view of the near real-time parking situation of his destination and allow the driver to reserve a space for a vehicle. In this present paper, we have represented an alternate solution based on Smart QR Technology for Parking Management using Android Application. This system will collectively alleviate traffic congestion, reduce fuel consumption and provide a user-friendly interface to access parking. Keywords: Android application, internet of things, parking management, protocols, QR code technology, web map server Cite this Article: Siddahant Jain, Shivam Kumar Mishra, Madhur Bihani, Palash Goyal. Smart QR Based Parking System. Journal of Computer Technology & Applications. 2019; 10(3): 12–18p.

MDP: Minimum delay hot-spot parking

Hot-spot parking is becoming the Achilles' heel of the tourism industry. The more tourists that are attracted to the scenic site, the more often they will encounter a hassle of congestion to find a parking place; while those existing facilities for daily traffic are not supposed to support the excessive volume outburst. In this paper, we present a new parking guidance information system (PGI). By taking advantage of the technical advances of today in wireless communication of vehicular ad-hoc network, each vehicle will request and obtain a relatively fair opportunity to park. The competition and the corresponding allocation on the available slots emerging along the time scale are considered, in order to ensure that no vehicle enters a state of starvation. This is the first attempt to solve the spatiotemporal problem of resource assignment based on our extensive work on the Hungarian algorithm. The contribution as one part of the sustainable development of big historic cities is to minimize the idle driving and waiting, without increasing the parking supply, which could be costly and unnecessary to build in those urban areas. Both analytical and experimental results demonstrate the success of our effort, in terms of the average cruising/waiting time in each individual parking case and its upper bound. The data is compared with the best results known to date and shows a new direction to improve the resource assignment.

Short‐term forecasting of available parking space using wavelet neural network model

The technique to forecast available parking spaces (APSs) is the foundation theory of parking guidance information systems (PGISs). This study utilises data collected on parking availability at several off-street parking garages in Newcastle upon Tyne, England, to investigate the changing characteristics of APS. Using these baseline data the research reported here aims to build up a short-term APS forecasting model and applies the wavelet neural network (WNN) method to the PGIS problem. After selecting optimal preferences, including training set size, delay time and embedding dimension, the APS short-term forecasting model based on WNN is built and tested using the real-world dataset. By experimental tests conducted using the same dataset, WNN's prediction performance is compared with the largest Lyapunov exponents (LEs) method in the aspects of accuracy, efficiency and robustness. It is found that WNN prevails through a more efficient structure and employs, barely half of the computational cost compared to the largest LEs method, which could be significant if applied to real-time operation. Moreover, WNN enjoys a more accurate performance, for its prediction average mean square error (MSE) is 6.4 ± 3.1 (in a parking building with 492 parking lots) for workdays and 8.5 ± 6.2 for weekends, compared to the MSE of largest LEs method, 18.7 and 29.0, respectively.

Designing Parking Guidance Information System Based on Internet of Things

Parking problem is being more and more difficult with increasing of total vehicles and lack of parking facilities, it is necessary to guide driver in order to cut down useless driving in the process of searching parking space. This paper designs an Parking Guidance Information System (PGIS) based on ITO technology, Zigbee technology is used to perceive vacancy parking space information and send to parking management system through wireless network, Parking space release system control and ensure the accurate release of information, thus the function of parking guidance is realized, besides this system has the function of reservation through mobile operating platform. The application of the PGIS will contribute to improving the order and efficiency of parking.

Design of Wireless Sensor Network for Parking Guidance Information System

Design of Wireless Sensor Network for Parking Guidance Information System

주차안내정보시스템의 USN기술 적용을 위한 연구

Parking guidance and information systems have been introduced to response the inefficient and inconvenience use of parking facilities. While advanced countries have reached effective use of parking guidance information system, the Republic of Korea has yet suffered from the lack of full use of advanced technologies. This study proposes an algorithm designed for a systems utilizing the USN technologies. A field test was performed. The test results showed that information from the proposed system is much reliable when compared to the one from the existing.

Optimization of Dynamic Parking Guidance Information for Special Events

Planned special events attract thousands of attendees from nearby cities or suburbia by car and transit. In most cases, the majority of attendees use personal automobiles, and a high parking demand results in a short time, with a consequent parking shortage. Parking guidance information systems can solve the problem by displaying information on parking lot availability to dynamically divert vehicles. This study focused on optimizing dynamic parking guidance information for automobile drivers at special events. An original multimode traffic network was converted to a novel network by considering parking lots as dummy links; therefore the shortest path and traffic assignment could be implemented in this extended network. A bilevel programming model based on quasi-dynamic route choice and linear programming was proposed to optimize the dynamic parking guidance information. On the basis of travelers' reaction to the guidance, stochastic dynamic user optimal route choice was employed within the lower-level model. The upper-level model was a linear program aimed at minimizing network total travel time. The solutions of the bilevel programming model were based on discrete particle swarm optimization and the method of successive average algorithms. Results of a case study implemented with a hypothetical network indicated that the optimization model could reduce the system total travel time by 4%.

A Study on Discrete Choice Modeling with Parking Guidance Information System

This paper aims to develop valid analysis frame for interaction between advanced information system and travel behavior. We examine the shopping destination choice model with parking guidance information. Pre-trip information system is assumed in this study. First, we develop a methodology to describe relation between information and travel behavior based on the likelihood matrix. Especially, the role of accuracy parameter of information system was well studied. Second, the several processes to evaluate the accuracy were compared. The results show the validity and rationality of our models. Finally, the further topics to be studied are discussed.