Binary search tree based hierarchical placement algorithm for IoT based smart parking applications

Kizilkaya, Burak; Caglar, Mehmet Umut; Al‐Turjman, Fadi; Ever, Enver

doi:10.1016/j.iot.2018.12.001

Cited by 24 publications

(4 citation statements)

References 12 publications

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“…Many research efforts have focused on improving parking search efficiency, reservation, and prediction for an available parking space. For example, Kizilkaya et al [19] used a hierarchical approach for predicting free parking spots using a binary search tree (BST). For the experiment, synthetic data is used with attributes such as parking distance, capacity, and availability status.…”

Section: Review Of the Scientific Literaturementioning

confidence: 99%

Multisource Data Integration and Comparative Analysis of Machine Learning Models for On-Street Parking Prediction

et al. 2022

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Searching for a free parking space can lead to traffic congestion, increasing fuel consumption, and greenhouse gas pollution in urban areas. With an efficient parking infrastructure, the cities can reduce carbon emissions caused by additional fuel combustion, waiting time, and traffic congestion while looking for a free parking slot. A potential solution to mitigating parking search is the provision of parking-related data and prediction. Previously many external data sources have been considered in prediction models; however, the underlying impact of contextual data points and prediction has not received due attention. In this work, we integrated parking occupancy, pedestrian, weather, and traffic data to analyze the impact of external factors on on-street parking prediction. A comparative analysis of well-known Machine (ML) Learning and Deep Learning (DL) techniques, including Multilayer Perceptron (MLP), Random Forest (RF), Decision Trees (DT), K-Nearest Neighbors (KNN), Gradient Boosting (GA), Adaptive Boosting (AB), and linear SVC for the prediction of OnStreet parking space availability has been conducted. The results show that RF outperformed other techniques evaluated with an average accuracy of 81% and an AUC of 0.18. The comparative analysis shows that less complex algorithms like RF, DT, and KNN outperform complex algorithms like MLP in terms of prediction accuracy. All four data sources have positively impacted the prediction, and the proposed solution can determine the best possible parking slot based on weather conditions, traffic flow, and pedestrian volume. The experiments on live prediction showed an ingest rate of 0.1 and throughput of 0.3 events per second, demonstrating a fast and reliable prediction approach for available slots within a 5–10 min time frame. The study is scalable for larger time frames and faster predictions that can be implemented for IoT-based big data-driven environments for on-street and off-street parking.

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Section: Review Of the Scientific Literaturementioning

confidence: 99%

Multisource Data Integration and Comparative Analysis of Machine Learning Models for On-Street Parking Prediction

et al. 2022

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“…Sustainable innovative solutions are needed to manage these limited resources [7]. With the increasing number of vehicles in cities and Internet of Things (IoT), innovative solutions are also required for limited parking spaces in smart cities [10] [11]. There are many studies in the literature carried out on the solution for parking space problems [12].…”

Section: Related Workmentioning

confidence: 99%

“…Park owners that provide services can advertise their services thorugh this system. In [11], the performance of the algorithms used in search for parking spaces was examined in terms of energy and time. The hierarchy-based BST algorithm was used and it was shown to be more efficient than non-hierarchical aspects.…”

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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“…HK.01.07/MENKES/382/2020. The smart parking system is considered beneficial for car parking operators, as well as in the preservation of the world's industrialization environment, the increase in the population, the city that is running slowly in its development, and the mismanagement of parking spaces in metropolitan urban areas [2]- [3]. The new IoT-based parking platform makes it possible to connect, analyze, and automate the data collected from the device and enable smart parking [4].…”

Section: Introductionmentioning

confidence: 99%

Smart Parking System Model Analysis with NodeMCU and IoT-Based RFID

Fernandez,

Erwadi,

Erlangga

2023

JUITA

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There is much conventional management parking nowadays. One of them is on campus 1 of Muhammadiyah University of Bengkulu. Many vehicles that do not belong to students or staff from the Muhammadiyah University of Bengkulu are parked in the inner area of campus. In addition, it causes vulnerable to direct physical contact with other people, while the government recommends not to have direct physical contact with other people in the era of the Covid-19 virus pandemic. So this study designs a smart parking system with Node MCU and IoT-based RFID. Parking systems that use RFID are able to filter incoming vehicles, increase security and efficiency in parking areas, and able to reduce the impact of the spread of the Covid-19 virus. The technology used is RFID as a user Id reader Sensor, Node MCU as a microcontroller, E-KTP as access to the parking area, servo as a parking portal driver, LCD as a sign whether access is accepted or not, and infrared as a distance sensor that will signal to a servo to close the portal. This system can be hoped to overcome the current problems.

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Binary search tree based hierarchical placement algorithm for IoT based smart parking applications

Cited by 24 publications

References 12 publications

Multisource Data Integration and Comparative Analysis of Machine Learning Models for On-Street Parking Prediction

Multisource Data Integration and Comparative Analysis of Machine Learning Models for On-Street Parking Prediction

Deep Learning-Based Mobile Application Design for Smart Parking

Smart Parking System Model Analysis with NodeMCU and IoT-Based RFID

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