Smart City Gnosys

Smart city article details

Title	Parking Availability Forecasting Model
ID_Doc	41305
Authors	Kuhail M.A.; Boorlu M.; Padarthi N.; Rottinghaus C.
Year	2019
Published	5th IEEE International Smart Cities Conference, ISC2 2019
DOI	http://dx.doi.org/10.1109/ISC246665.2019.9071688
Abstract	Parking is increasingly an issue in the world today especially in large and growing cities with contemporary urban mobility. The effort spent in searching for available parking spots results in significant loss of resources such as time, and fuel, as well as environmental pollution. Parking Availability can be influenced by many factors such as time of day, day of week, location, nearby events, weather and traffic conditions. Driven by the idea of predicting parking availability to help drivers plan ahead of time, we contribute a Parking Availability Forecasting Model, which uses a time-series analysis and machine-learning algorithms to predict the number of available parking spots at a certain location on a desired date and time. The forecasting model is trained on historical parking data from the cities of Kansas City, US and Melbourne, Australia. This paper also compares the accuracy of different time-series forecasting models, and how each of them fits our use-case scenario. Multivariate data analysis together with temperature and weather summary are used to cross-validate our forecasting model. © 2019 IEEE.
Author Keywords	Forecasting model; Parking prediction; Predictive modeling; Time Series Analysis

Similar Articles

Id	Similarity	Authors	Title	Published
36016	0.899	Soumana A.N.H.; Salah M.B.; Idbraim S.; Boulouz A.	Machine Learning Models In The Large-Scale Prediction Of Parking Space Availability For Sustainable Cities	EAI Endorsed Transactions on Internet of Things, 10 (2024)
15099	0.898	Soumana A.N.H.; Salah M.B.; Idbraim S.; Boulouz A.	Comparing Machine Learning Models For Large Scale Prediction Of Parking Space Availability	AIP Conference Proceedings, 2814, 1 (2023)
15957	0.898	Jelen G.; Babic J.; Podobnik V.	Contextual Prediction Of On-Street And Off-Street Parking Lot Utilisation: An Opening Gate Towards Sustainable Parking	2021 6th International Conference on Smart and Sustainable Technologies, SpliTech 2021 (2021)
30883	0.897	Arjona J.; Linares M.; Casanovas-Garcia J.; Vázquez J.J.	Improving Parking Availability Information Using Deep Learning Techniques	Transportation Research Procedia, 47 (2020)
38660	0.895	Inam S.; Mahmood A.; Khatoon S.; Alshamari M.; Nawaz N.	Multisource Data Integration And Comparative Analysis Of Machine Learning Models For On-Street Parking Prediction	Sustainability (Switzerland), 14, 12 (2022)
17277	0.894	Rodrigues B.; Fernandes C.; Vieira J.; Portela F.	Data Mining Models To Predict Parking Lot Availability	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 14116 LNAI (2023)
1340	0.891	Arjona J.; Linares M.P.; Casanovas J.	A Deep Learning Approach To Real-Time Parking Availability Prediction For Smart Cities	ACM International Conference Proceeding Series (2019)
9440	0.89	Guerrini G.; Romeo L.; Alessandrini D.; Frontoni E.	Analysis, Design And Implementation Of A Forecasting System For Parking Lots Occupation	DICPS 2021 - Proceedings of the ACM 1st Workshop on Data-Driven and Intelligent Cyber-Physical Systems, Part of CPS-IoT Week 2021 (2021)
17465	0.886	Kim K.; Koshizuka N.	Data-Driven Parking Decisions: Proposal Of Parking Availability Prediction Model	HONET-ICT 2019 - IEEE 16th International Conference on Smart Cities: Improving Quality of Life using ICT, IoT and AI (2019)
44321	0.885	Alghoniemy A.; Susko J.; Kahle D.; Saunders L.; Belsare P.; El-Tawab S.	Real-Time Cloud-Based Data Analysis Using Machine Learning For Smart Parking	2024 International Conference on Computer and Applications, ICCA 2024 (2024)