ArticleScientific reports2022
Smart-parking management algorithms in smart city.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A Deep Reinforcement Learning and Graph Convolution Approach to On-Street Parking Search Navigation.Sensors (Basel, Switzerland) · 2025Article
- Constraint Optimization Model for Dynamic Parking Space Allocation.Sensors (Basel, Switzerland) · 2024Article
- Effects of the interplay between topology and function of an integrated urban development on patterns of user movement.Scientific reports · 2024Article
- CRAWLING: a crowdsourcing algorithm on wheels for smart parking.Scientific reports · 2023Article
- Multilevel Simultaneous Lighting-Imaging System.ACS omega · 2023Article
- Novel randomization and iterative based algorithms for the transactions assignment in blockchain problem.PloS one · 2023Article
- Architecture and enhanced-algorithms to manage servers-processes into network: a management system.PeerJ. Computer science · 2023Article
- Novel variable neighborhood search heuristics for truck management in distribution warehouses problem.PeerJ. Computer science · 2023Article
- A secure solution based on load-balancing algorithms between regions in the cloud environment.PeerJ. Computer science · 2023Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, various advanced technologies have been employed to build smart cities. Smart cities aim at improving the quality of life through the delivery of better services. One of the current services that are essential for any smart city, is the availability of enough parking spaces to ensure smooth and easy traffic flow. This research proposes a new framework for solving the problem of parking lot allocation, which emphasizes the equitable allocation of people based on the overall count of people in each parking space. The allocation process is performed while considering the available parking lots in each parking space. To accomplish the desired goal, this research will develop a set of seven algorithms to reduce the gap in the number of people between parking spaces. Many experiments carried out on 2430 different cases to cover several aspects such as the execution time and the gap calculations, were used to explore the performance of the developed algorithm. Analyzing the obtained results indicates a good performance behavior of the developed algorithms. Also, it shows that the developed algorithms can solve the studied problem in terms of gap and time calculations. The MR algorithm gained excellent performance results compared to one of the best algorithms in the literature. The MR algorithm has a percentage of 96.1 %, an average gap of 0.02, and a good execution time of 0.007 s.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.