To tackle the challenges in small-sample POP, we propose an approach named Adaptation and Learning to Learn (ALL) by adopting the capability of advanced deep learning and federated learning. However, an issue hinders its mass deployment: many parking facilities cannot collect enough data to feed data-hungry machine learning models. Parking occupancy prediction (POP) plays a vital role in many parking-related smart services for better parking management.
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Comparative analysis has been done with the existing time series a model including other LSTM models, and it is found that our proposed CNN-LSTM-based models provide better results in terms of different performance measurement parameters. With the use of the Internet of Things network, cloud server, and sensors used in various smart parking places, we generate real-time information to estimate the hybrid CNN-LSTM model and the results have been discussed in detail. As such, in this paper, parking occupancy percentage is forecasted for an indoor parking system for any type of vehicle using a modified long short-term memory model (LSTM) as a CNN-LSTM hybrid model, from which parking occupancy percentage can be predicted using the prior parking information during specific dates and hours. Even though many researchers have tried to solve these problems previously using various methods of deep learning, there are still some shortcomings when it comes to estimating parking space occupancy levels. Parking occupancy percentage forecasting is an optimum problem.
In this way, parking spaces could be found more quickly during traffic searches. This information should be available to any type of vehicle in an indoor parking area.
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An intelligent decision support system can help to provide advance information to the drivers about the occupancy percentage of a parking area.
Nowadays due to the advancement of the smart parking solutions, the drivers are eager to know that how much vacancy will be there in a particular parking place in specific day or in a particular hour of a day. The performance of the ML models for the prediction of parking occupancy is better than the state of the art work in the problem under study, scoring an MSE of 7.18 at a time horizon of 60 minutes. Machine learning (ML) is employed for AI analysis, using predictive models based on neural networks and random forests.
Traffic cameras are used as WoT sensors, together with weather forecasting Web services. This paper examines the impact of WoT and AI in smart cities, considering a real-world problem, the one of predicting parking availability. Via AI, data produced by WoT-enabled sensory observations can be analyzed and transformed into meaningful information, which describes and predicts current and future situations in time and space. Besides WoT, an essential aspect of understanding dynamic urban systems is artificial intelligence (AI). The Web of Things (WoT) enables information gathered by sensors deployed in urban environments to be easily shared utilizing open Web standards and semantic technologies, creating easier integration with other Web-based information, towards advanced knowledge.
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