Prediction of Parking Spaces and Recommendation of Parking Area in Urban Complex

Zhu, Deyuan; Song, Shuaifei; Zhang, Hengjing; Shi, Zhao; Wei, Zheng; Liu, Hengchang

doi:10.1145/3421537.3421540

Proceedings of the 2020 4th International Conference on Big Data and Internet of Things 2020

DOI: 10.1145/3421537.3421540

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Prediction of Parking Spaces and Recommendation of Parking Area in Urban Complex

Deyuan Zhu

Shuaifei Song

Hengjing Zhang

et al.

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2024

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Cited by 2 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

View full text Add to dashboard Cite

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Understanding Drivers’ Behavioral attitudes and Intentions to Use Guidance Systems in Urban Complex Parking Lots Based on the C-TAM-TPB Framework

Yang,

Caiati,

Rasouli

et al. 2024

Transportation Research Record: Journal of the Transportation R

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Multi-function services characteristic of complexes, complicated spatial structure, and inaccurate parking guidance in complex parking lots create many issues for drivers, including orientation problems, excessive parking time, and difficultly finding a parking space close to the desired destination. Complex parking lot guidance systems (CPLGSs), if accurately designed, can potentially make a substantial contribution to alleviate these problems and reduce drivers’ travel time while improving the parking experience. However, the extent of this contribution depends on drivers’ acceptance of this technology. This paper reports on the intention of complex visitors to use CPLGSs. Specifically, an integrated model combining the technology acceptance model and the theory of planned behavior was developed and tested to analyze the factors that can affect drivers’ behavioral attitudes and intentions for CPLGSs. Perceived risk is included as an antecedent on behavioral attitude. Data were collected through an online questionnaire, and 810 valid questionnaires were obtained. Results from a structural equation model show that perceived usefulness, subjective norm, behavioral attitude, and perceived behavioral control are positively connected with behavioral intention, while perceived risk is negatively related to behavioral attitude. Findings on mediators are also discussed. In addition, multi-group differences are analyzed, revealing that the use intention of the age ≤ 40 group is more positively influenced by subjective norms compared with the age > 40 group. Moreover, perceived ease of use has a strong influence on behavioral attitude, especially among individuals who travel to a complex frequently. The theoretical implications in this paper are intended to guide the service improvement of CPLGSs and other parking guidance systems.

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Prediction of Parking Spaces and Recommendation of Parking Area in Urban Complex

Cited by 2 publications

References 10 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

Understanding Drivers’ Behavioral attitudes and Intentions to Use Guidance Systems in Urban Complex Parking Lots Based on the C-TAM-TPB Framework

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