Predictive Analytics for Smart Parking: A Deep Learning Approach in Forecasting of IoT Data

Abstract

Nowadays, a sustainable and smart city focuses on energy efficiency and the reduction of polluting emissions through smart mobility projects and initiatives to “sensitize” infrastructure. Smart parking is one of the building blocks of intelligent mobility, innovative mobility that aims to be flexible, integrated, and sustainable and consequently integrated into a Smart City. By using the Internet of Things (IoT) sensors located in the parking areas or the underground car parks in combination with a mobile application, which indicates to citizens the free places in the different areas of the city and guides them toward the chosen parking, it is possible to reduce air pollution and fluidifying noise traffic. In this article, we present and discuss an innovative Deep Learning-based ensemble technique in forecasting the parking space occupancy to reduce the search time for parking and to optimize the flow of cars in particularly congested areas, with an overall positive impact on traffic in urban centres. A genetic algorithm has also been used to optimize predictors parameters. The main goal is to design an intelligent IoT-based service that can predict, in the next few hours, the parking spaces occupancy of a street. The proposed approach has been assessed on a real IoT dataset composed by over than 15M of collected sensor records. Obtained results demonstrate that our method outperforms both single predictors and the widely used strategy of the mean providing inherently robust predictions.

Deep learning: survey of environmental and camera impacts on internet of things images

“…The last model is GoogleNet model (Szegedy et al. 2015 ) which uses fewer parameters than others. The performance of VGG-16 and GoogleNet is better compared with the Caffe and VGG-CNN-S networks in noise.…”

“… 2014 ), VGG-16 model (Simonyan and Zisserman 2014 ) and GoogleNet model (Szegedy et al. 2015 ) for their experiments. To create the blur effects, the Gaussian kernel is used and the SD of the Gaussian is varied from 1 to 9.…”

DeepVATS: Deep Visual Analytics for Time Series

“…• Parking: Presented in [36], it contains the hourly parking occupancy rate in 6 streets of Caserta and Naples, Italy. From them, we take the data of one of the streets, namely Piazza Vanvitelli.…”

“…Based on the first synthetic dataset (S1 ), we trained a model in the deep learning module following a masking strategy with state (stateful ) and masking probability r = 0.4, with variable window in the range [36,72]. Once trained, we loaded the model in the visual analytics module to explore its content.…”

“…To demonstrate this hypothesis, we compared the results, for a segmentation task, between two MTSAE models trained on the same data set (S1), one trained with a variable window size in the range [36,72] (the same model as in 4.2.1) and the other with a fixed window size. Figure 14 shows the results of exploring the model trained using variable window size.…”