Performance Analysis of Proximity and Light Sensors for Smart Parking

Bachani, Mamta; Qureshi, Umair Mujtaba; Shaikh, Faisal Karim

doi:10.1016/j.procs.2016.04.200

Cited by 34 publications

(10 citation statements)

References 23 publications

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“…It gives high accuracy rate even in closed parking spaces. In [21], the performances of the sensors that control whether the parking spaces in the car parks are occupied or not were examined. These sensors exhibit different performances depending on the angle of the light and the distance.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Mobile Application Design for Smart Parking

Canlı

Toklu

2021

IEEE Access

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In the era of Internet of Things (IoT) and smart city ecosystems, there is a need for innovative smart parking systems for more sustainable cities. With the increasing number of vehicles in the cities every year, it takes more time to find parking spaces. The solution methods developed are no longer sufficient. The time that passes while waiting for a parking space in traffic carries with it problems such as energy, environmental pollution and stress. In this study, a deep learning and cloud-based new mobile smart parking application was developed to minimize the problem of searching for parking spaces. Within the application, a service has been developed based on deep learning with Long short-term memory (LSTM) to predict the parking space. Here, dynamic access is provided to the LSTM-based model previously created through the mobile device of the user, and the process of displaying the occupancy rates of the parks at the desired place is accomplished on the mobile device by entering the relevant parameters. By this means, both energy and time savings have been achieved. With the real-time car parking data collected in the city of Istanbul in Turkey, high accuracy results were obtained. In order to demonstrate the effectiveness of the model proposed, it was compared with the Support Vector Machine, Random Forest and ARIMA methods. The results have confirmed the high accuracy and reliability that was promised.

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Section: Related Workmentioning

confidence: 99%

Deep Learning-Based Mobile Application Design for Smart Parking

Canlı

Toklu

2021

IEEE Access

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“…For example, GPP and PGS2 systems adopt ultrasonic waves, while Siemens systems use parking sensors placed on the ground [6]. Monitoring vehicles by infrared sensors is one of the most sensible methods while considering that the system is mainly aimed at large-and medium-sized parking lots, and ultrasonic sensors are very sensitive to temperature changes and extreme air [7]. Therefore the system adopts infrared photoelectric switches.…”

Section: Parking Garage Information Guidance Managementmentioning

confidence: 99%

Design and Implementation of the Optimization Algorithm in the Layout of Parking Lot Guidance

Liu

Yang

et al. 2021

Wireless Communications and Mobile Computing

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The information guidance system for parking spaces in large- and medium-sized parking lots is not efficient at present. It tends to be difficult to find an empty parking space in parking lots in big cities. One of the problems is the large amount of calculation in the traditional Dijkstra algorithm. In this paper, an improved Dijkstra algorithm is presented and optimized to find the best parking path with the purpose of looking for the nearest free parking space based on the layout model in parking lot parking guidance. The experiments show that the improved Dijkstra algorithm can find the optimal parking space and the optimal parking path and improve the parking efficiency.

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“…Data collected from sensors were sent to a single server and centrally processed. In [21], a comprehensive analysis of the most relevant aspects of the design of a smart parking system has been presented. The article focuses mainly on the selection of adequate sensors and how to deploy them for accurately detecting state changes in the parking lot.…”

Section: Related Workmentioning

confidence: 99%

Forecasting Parking Lots Availability: Analysis from a Real-World Deployment

Barraco

Bicocchi

Mamei

et al. 2021

2021 IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events (PerCom Work

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Smart parking technologies are rapidly being deployed in cities and public/private places around the world for the sake of enabling users to know in real time the occupancy of parking lots and offer applications and services on top of that information. In this work, we detail a real-world deployment of a full-stack smart parking system based on industrial-grade components. We also propose innovative forecasting models (based on CNN-LSTM) to analyze and predict parking occupancy ahead of time. Experimental results show that our model can predict the number of available parking lots in a ±3% range with about 80% accuracy over the next 1-8 hours. Finally, we describe novel applications and services that can be developed given such forecasts and associated analysis.

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Performance Analysis of Proximity and Light Sensors for Smart Parking

Cited by 34 publications

References 23 publications

Deep Learning-Based Mobile Application Design for Smart Parking

Deep Learning-Based Mobile Application Design for Smart Parking

Design and Implementation of the Optimization Algorithm in the Layout of Parking Lot Guidance

Forecasting Parking Lots Availability: Analysis from a Real-World Deployment

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