Smart parking systems technologies, tools, and challenges for implementing in a smart city environment: a survey based on IoT &amp; ML perspective

Raj, Aparna; Shetty, Sujala D.

doi:10.1007/s13042-023-02056-5

Cited by 3 publications

References 90 publications

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Machine Learning‐Based Prediction of Parking Space Availability in IoT‐Enabled Smart Parking Management Systems

Dahiya,

Mittal,

Sharma

et al. 2024

Journal of Advanced Transportation

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Parking space management has become a critical challenge in urban areas due to increasing vehicle numbers and limited parking infrastructure. This paper presents a comprehensive study of machine learning (ML) models in IoT‐enabled environments focusing on proposing an ML‐based model for predicting available parking space. The study evaluates the performance of various models including K‐nearest neighbors (KNNs), support vector machines (SVMs), random forest (RF), decision tree (DT), logistic regression (LR), and Naïve Bayes (NB) based on “precision, recall, accuracy, and F1‐score performance metrics”. The results obtained by implementing ML models on the data with 65% and 85% threshold values are compared to draw meaningful conclusions regarding their performance in predicting parking space availability. Among the evaluated models, random forest (RF) demonstrates superior performance with high precision, recall, accuracy, and F1‐score values. It showcases its effectiveness in accurately predicting parking space availability in the IoT‐enabled environment. On the other hand, models such as K‐nearest neighbors (KNNs), decision tree (DT), logistic regression (LR), and Naïve Bayes (NB) show relatively lower performance in complex parking scenarios. The paper concludes that the use of advanced predictive models, particularly random forest, significantly enhances the accuracy and reliability of IoT‐enabled parking management systems and also reduces the waiting time of the vehicles, leading to more efficient resource utilization, reduced traffic congestion in real‐time scenarios, and better user satisfaction in the IoT‐enabled environment.

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Machine Learning‐Based Prediction of Parking Space Availability in IoT‐Enabled Smart Parking Management Systems

Dahiya,

Mittal,

Sharma

et al. 2024

Journal of Advanced Transportation

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Advancements in Embedded Systems and IoT for Automatic Heavy and Light Vehicle Parking: A Comprehensive Review

Rahul R. Gupta,

Shubham M. Udage,

Roshan J. Gupta

et al. 2024

IJARSCT

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This research paper comprehensively reviews automatic heavy and light vehicle parking systems, focusing on technological advancements in Embedded Systems and the Internet of Things (IoT). The escalating challenges in urban parking demand innovative solutions, and this paper assesses various literature surveys to explore the efficacy of these technologies in addressing the unique requirements of heavy and light vehicle parking. The investigation scrutinizes embedded systems as a foundational element, examining their hardware and software integrationto develop automated parking solutions. Additionally, the study explores the role of IoT in enhancing parking efficiency, leveraging sensors and communication technologies for real-time data acquisition and intelligent decision-making. Through an analysis of diverse literature surveys, this paper elucidates the strengths and limitations of different technological approaches, providing a comprehensive understanding of the state-of-the-art in automatic heavy and light vehicle parking. The synthesized insights are valuable for researchers, practitioners, and policymakers seeking to implement effective and scalable parking solutions in urban environments

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Enhancing Smart Parking Management through Machine Learning and AI Integration in IoT Environments

Knights,

Petrovska,

Prchkovska

2024

Navigating the Internet of Things in the 22nd Century - Concepts, Applications, and Innovations [Working Title]

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The integration of Internet of Things (IoT) technology has profoundly transformed urban life, particularly in the realm of parking management. Smart parking systems harness the capabilities of IoT to optimize parking space utilization, alleviate congestion, and elevate user experience. This chapter delves into the intricate process of data collection within IoT-enabled smart parking environments, with a specific emphasis on the seamless integration of machine learning and artificial intelligence (AI) techniques. By conducting a comprehensive analysis of various data sources, machine learning algorithms, and AI technologies, this chapter elucidates how smart parking systems leverage intelligent data collection and analysis to enhance operational efficiency and effectiveness. Through the convergence of IoT, machine learning, and AI, smart parking systems are poised to revolutionize urban mobility and drive sustainable urban development.

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Smart parking systems technologies, tools, and challenges for implementing in a smart city environment: a survey based on IoT & ML perspective

Cited by 3 publications

References 90 publications

Machine Learning‐Based Prediction of Parking Space Availability in IoT‐Enabled Smart Parking Management Systems

Machine Learning‐Based Prediction of Parking Space Availability in IoT‐Enabled Smart Parking Management Systems

Advancements in Embedded Systems and IoT for Automatic Heavy and Light Vehicle Parking: A Comprehensive Review

Enhancing Smart Parking Management through Machine Learning and AI Integration in IoT Environments

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