A machine learning technique for detecting outdoor parking

Mago, Neeru; Kumar, Satish

doi:10.14419/ijet.v7i2.30.13460

Cited by 3 publications

(2 citation statements)

References 6 publications

(6 reference statements)

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“…The research considers both temporal and spatial patterns in parking slots. Another deep learning case study in Shoeibi and Shoeibi 23 and Mago and Kumar 24 using deep neural networks, a supervised learning technology, created an intelligent parking space that proposed hybrid robotic valets in a parking system that optimizes the use of parking spots.…”

Section: Related Workmentioning

confidence: 99%

Detection of car parking space by using Hybrid Deep DenseNet Optimization algorithm

Rajyalakshmi

Lakshmanna

2023

Int J Network Mgmt

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Internet of Things (IoT) and related applications have revolutionized most of our societal activities, enhancing the quality of human life. This study presents an IoT‐based model that enables optimized parking space utilization. The paper implements a Hybrid Deep DenseNet Optimization (HDDNO) algorithm for predicting parking spot availability involving Machine Learning (ML) and deep learning techniques. The HDDNO‐based ML model uses secondary data from the National Research Council Park (CNRPark) in Pisa, Italy. Different regression algorithms are employed to forecast parking lot availability for a given time as part of the prediction process. The DenseNet technique has generated promising results, whereas the HDDNO model yielded better accuracy. The use of five optimizers, namely, Adaptive Moment Estimation (Adam), Root Mean Squared Propagation (RMSprop), Adaptive Gradient (AdaGrad), AdaDelta, and Stochastic Gradient Descent (SGD), have played significant roles in minimizing the loss of the model. The part of Adam has enabled the HDDNO model to generate predictions with high accuracy 99.19% and low loss 0.0306%. This proposed methodology would significantly improve environmental safety and act as an initiative toward developing smart cities.

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Section: Related Workmentioning

confidence: 99%

Detection of car parking space by using Hybrid Deep DenseNet Optimization algorithm

Rajyalakshmi

Lakshmanna

2023

Int J Network Mgmt

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“…al. [26] proposed another model for designing a parking management system based on video processing techniques. The proposed method detects available parking lots in real-time using cameras and assign vehicles entering the area to specific places.…”

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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