Prototype of Parking Finder Application for Intelligent Parking System

This paper explains the design and fabrication of an intelligent, user-friendly parking system developed in the United Arab Emirates. The need for a smart parking system was assessed by conducting a survey on the current parking issues. This paper elaborates on the hardware and software realization of the proposed parking system developed for motorists to locate vacant parking using mobile application. The various types of vehicle detection sensors available in the market have been evaluated for the implementation of the system. The main objective of this paper is to build a prototype intelligent parking system with maximum accuracy. The goal of the project was to control the detection modules wirelessly through a customized mobile application, allowing ease of operation and maintenance. While the users can enjoy better comfort and safety, the focus is to create a self-reliable, and ecologically sustainable system while reducing searching time, fuel wastage resulting in lower carbon footprint. The mobile application has been developed using Android Studio, and the results are presented in this paper

Prototype of parking finder application for intelligent parking system

<p class="IJASEITAbtract"><span lang="EN-GB">This paper explains the design and fabrication of an intelligent, user-friendly parking system developed in the United Arab Emirates. The need for a smart parking system was assessed by conducting a survey on the current parking issues. This paper elaborates on the hardware and software realization of the proposed parking system developed for motorists to locate vacant parking using mobile application. The various types of vehicle detection sensors available in the market have been evaluated for the implementation of the system. The main objective of this paper is to build a prototype intelligent parking system with maximum accuracy. The goal of the project was to control the detection modules wirelessly through a customized mobile application, allowing ease of operation and maintenance. While the users can enjoy better comfort and safety, the focus is to create a self-reliable, and ecologically sustainable system while reducing searching time, fuel wastage resulting in lower carbon footprint. The mobile application has been developed using Android Studio, and the results are presented in this paper.</span></p

Data analytics for smart parking applications

We consider real-life smart parking systems where parking lot occupancy data are collected from field sensor devices and sent to backend servers for further processing and usage for applications. Our objective is to make these data useful to end users, such as parking managers, and, ultimately, to citizens. To this end, we concoct and validate an automated classification algorithm having two objectives: (1) outlier detection: to detect sensors with anomalous behavioral patterns, i.e., outliers; and (2) clustering: to group the parking sensors exhibiting similar patterns into distinct clusters. We first analyze the statistics of real parking data, obtaining suitable simulation models for parking traces. We then consider a simple classification algorithm based on the empirical complementary distribution function of occupancy times and show its limitations. Hence, we design a more sophisticated algorithm exploiting unsupervised learning techniques (self-organizing maps). These are tuned following a supervised approach using our trace generator and are compared against other clustering schemes, namely expectation maximization, k-means clustering and DBSCAN, considering six months of data from a real sensor deployment. Our approach is found to be superior in terms of classification accuracy, while also being capable of identifying all of the outliers in the dataset