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Short Term Prediction of Parking Area states Using Real Time Data and Machine Learning Techniques [article]

Jesper Provoost, Luc Wismans, Sander Van der Drift, Andreas Kamilaris, Maurice Van Keulen
2019 arXiv   pre-print
In this research, a real-time parking area state (occupancy, in- and outflux) prediction model (up to 60 minutes ahead) has been developed using publicly available historic and real time data sources.  ...  However, the performance of predicting in- and outflux is less sensitive to the prediction horizon.  ...  The end goal is the real-time predicton of parking area states in terms of occupancy, in-and out-flux, which can directly be used for traffic management by mobility service providers (e.g. traffic information  ... 
arXiv:1911.13178v1 fatcat:aavgt6vkazbchlxpoh25yns2um

Emerging Technologies for Smart Cities' Transportation: Geo-Information, Data Analytics and Machine Learning Approaches

Kenneth Li-Minn Ang, Jasmine Kah Phooi Seng, Ericmoore Ngharamike, Gerald K. Ijemaru
2022 ISPRS International Journal of Geo-Information  
This paper reviews the state-of-the-art for SC transportation techniques and approaches.  ...  The paper contains core discussions on the impacts of geo-information on SC transportation, data-driven transportation and big data technology, machine learning approaches for SC transportation, innovative  ...  Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/ijgi11020085 fatcat:bjkv6cu7zbfqbl7q7ezfhai5ya

Deep Learning-Based Mobile Application Design for Smart Parking

H. Canli, S. Toklu
2021 IEEE Access  
Within the application, a service has been developed based on deep learning with Long short-term memory (LSTM) to predict the parking space.  ...  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 [38] , a hybrid approach consisting of LSTM and genetic algorithm to predict parking areas in the short term was mentioned.  ... 
doi:10.1109/access.2021.3074887 doaj:d62e1a75d36d4c1c845c429f1702234b fatcat:bkfhmcy3fzdczjnpwrbytazgyq

A Review of Machine Learning and IoT in Smart Transportation

Fotios Zantalis, Grigorios Koulouras, Sotiris Karabetsos, Dionisis Kandris
2019 Future Internet  
As the volume of the collected data increases, Machine Learning (ML) techniques are applied to further enhance the intelligence and the capabilities of an application.  ...  The field of smart transportation has attracted many researchers and it has been approached with both ML and IoT techniques.  ...  Short-term along with long-term traffic predictions have been also challenged in [27] comparing the performance of four ML methods: RF, a baseline predictor, a regression tree, and an FF-NN.  ... 
doi:10.3390/fi11040094 fatcat:6xneyx7ynrgn7p2yl5efy76cee

Predicting Parking Occupancy via Machine Learning in the Web of Things

Jesper C. Provoost, Andreas Kamilaris, Luc J.J. Wismans, Sander J. van der Drift, Maurice van Keulen
2020 Internet of Things  
Machine learning (ML) is employed for AI analysis, using predictive models based on neural networks and random forests.  ...  This paper examines the impact of WoT and AI in smart cities, considering a real-world problem, the one of predicting parking availability.  ...  of Cyprus through the Directorate General for European Programmes, Coordination and Development.  ... 
doi:10.1016/j.iot.2020.100301 fatcat:oexyet4xxbfxrpqdvigzlxh63m

Sensing Occupancy through Software: Smart Parking Proof of Concept

Lea Dujić Rodić, Toni Perković, Tomislav Županović, Petar Šolić
2020 Electronics  
Consequently, given nodes have multiple components, adequate software is required for its control and state-machine to communicate its status to the receiver.  ...  In order to prove the concept, experimental setup based on LoRa-based parking sensors was used to gather occupancy/signal strength data.  ...  Using the data regarding the duration of free parking space and occupancy status, the researches in [27] have developed a short-term and long term parking availability prediction system based on Neural  ... 
doi:10.3390/electronics9122207 fatcat:i7ocqofs65cu5frjxbog4onh6i

Performance Evaluation of Machine Learning and Neural Network-Based Algorithms for Predicting Segment Availability in AIoT-Based Smart Parking

Issa Dia, Ehsan Ahvar, Gyu Myoung Lee
2022 Network  
In a smart parking application, Artificial Intelligence of Things (AIoT) can help drivers to save searching time and automotive fuel by predicting short-term parking place availability.  ...  However, performance of various Machine Learning and Neural Network-based (MLNN) algorithms for predicting parking segment availability can be different.  ...  In a smart parking, AIoT can help drivers to save searching time and automotive fuel by predicting short-term parking place availability.  ... 
doi:10.3390/network2020015 fatcat:biovxikiwndedmykrurnyzw6oq

Improving Parking Availability Information Using Deep Learning Techniques

Jamie Arjona, MªPaz Linares, Josep Casanovas-Garcia, Juan José Vázquez
2020 Transportation Research Procedia  
This research begins by looking at the state of the art in predictive methods based on machine learning for time series.  ...  Similar studies and proposed solutions for parking prediction are described in terms of the technology and current state-of-the-art predictive models.  ...  Acknowledgements Throughout this work, the authors have benefited from the support of the inLab FIB team at Universitat Politècnica de Catalunya and the company Worldsensing S.L.  ... 
doi:10.1016/j.trpro.2020.03.113 fatcat:rjzwsbuihjahlg6ofsmwkb3tva

Introduction to the Special Issue "Advances in Computational Intelligence Applications in the Mining Industry"

Rajive Ganguli, Sean Dessureault, Pratt Rogers
2022 Minerals  
This is an exciting time for the mining industry, as it is on the cusp of a change in efficiency as it gets better at leveraging data [...]  ...  The reinforcement model is trained based on a continuous real-time discrete event simulation (DES) model, which simulates short-term mine plans. Wilson et al.  ...  Many papers in this issue make excellent use of these techniques to advance the state of the industry.  ... 
doi:10.3390/min12010067 fatcat:lzm2oly4vnf4xjus7bqn4mhfou

Towards Efficient and Intelligent Internet of Things Search Engine

William Grant Hatcher, Cheng Qian, Weichao Gao, Fan Liang, Kun Hua, Wei Yu
2021 IEEE Access  
Given the now obvious advances in machine learning, the potential for deep learning-based prediction to improve resource use, and thus query retrieval, is clear.  ...  INDEX TERMS Machine learning, Internet of Things, search engine, edge intelligence, applications. 15778 This work is licensed under a Creative Commons Attribution 4.0 License.  ...  In terms of enabling intelligence in IoT search, we discuss the integration of state-of-the-art data mining and machine learning techniques. • LSTM-based Query Prediction: We conduct a case study to demonstrate  ... 
doi:10.1109/access.2021.3052759 fatcat:osnxv2spm5brfjlyswkxm5qm54

Short Term Wind Speed Forecasting using Hybrid ELM Approach

Mahaboob Shareef Syed, S. Sivanagaraju
2017 Indian Journal of Science and Technology  
It uses the features of both Persistent and Extreme Learning Machine algorithms.  ...  Findings: The forecasting is carried out for three areas Guntur, Vijayawada and Ongole of Andhra Pradesh state, India for winter, summer and rainy seasons.  ...  It is very simple way of forecasting and is used as a comparison tool over NWP methods. This naive approach is very much suitable for very short term and short term time horizon forecasts.  ... 
doi:10.17485/ijst/2017/v10i8/104479 fatcat:aihucg6kc5fnbjwd57rm6ltyky

Deep Intelligent Prediction Network: A Novel Deep Learning Based Prediction Model on Spatiotemporal Characteristics and Location Based Services for Big Data Driven Intelligent Transportation System

GPS data of the vehicle in the real time.  ...  Big data analytics helps to cope with large amount of storage and computing resources required to use mass traffic data effectively.  ...  Deep learning techniques predict the short-term travel demand as data has been exploited from electronic payment data sources and location traces in the real time.  ... 
doi:10.35940/ijitee.k1503.0981119 fatcat:wnyxlv4pvfgfvbti2tcprzqfwu

Traffic Stream Short-term State Prediction using Machine Learning Techniques

Mohammed Elhenawy, Hesham Rakha, Hao Chen
2016 Proceedings of the International Conference on Vehicle Technology and Intelligent Transport Systems  
The paper addresses the problem of stretch wide short-term prediction of traffic stream state.  ...  Two cutting-edge machine learning algorithms are used to predict the stretch-wide traffic stream traffic state up to 120 minutes in the future.  ...  CONCLUSIONS AND FUTURE WORK In this paper, two machine-learning techniques were used to predict the spatiotemporal evolution of traffic stream states.  ... 
doi:10.5220/0005895701240129 dblp:conf/vehits/ElhenawyRC16a fatcat:4lm3blvq7bceford65fubmui3m

Predicting vehicles parking behaviour in shared premises for aggregated EV electricity demand response programs [article]

Vinicius Monteiro de Lira, Fabiano Pallonetto, Lorenzo Gabrielli, Chiara Renso
2021 arXiv   pre-print
We formalize the prediction problem as a supervised machine learning task to predict the duration of the parking event before the car leaves the slot.  ...  We structure our experiments inspired by two research questions aiming to discover the accuracy of the proposed machine learning approach and the most relevant features for the prediction models.  ...  Acknowledgment The work is supported by the ERA-NET Smart Energy System, Sustainable Energy Authority Ireland and Italian Ministry of Research with project N. ENSGPLUSREGSYS18 00013.  ... 
arXiv:2109.09666v1 fatcat:4icx5cfymzeo3n7r3bb6z6y6be

Evaluation of Data Mining Techniques and Its Fusion with IoT Enabled Smart Technologies for Effective Prediction of Available Parking Space

Anchal Dahiya, Pooja Mittal
2021 International journal of electrical and computer engineering systems  
For this purpose, a comparative analysis of five data mining techniques such as the Support Vector Machine, K- Nearest approach, Decision Tree, Random Forest, and Ensemble learning approaches are applied  ...  After experiencing the hard times of pandemic situations we learned that if we could have a smart system that can help us in automatic parking of the vehicles then it could be a great help to society.  ...  To obtain better results for the prediction of vacant space, a lot of data is generated by IoT sensors that are further coupled with other IoT devices, and data mining techniques are applied to the real-time  ... 
doi:10.32985/ijeces.12.4.2 fatcat:ax5lyp7lgndxdbdpp2gotz2w5q
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