Evolutionary Deep Learning for Car Park Occupancy Prediction in Smart Cities

Camero, Andrés; Toutouh, Jamal; Stolfi, Daniel H.; Alba, Enrique

doi:10.1007/978-3-030-05348-2_32

Cited by 78 publications

(60 citation statements)

References 25 publications

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“…Regarding the algorithms for the generation of predictions, here we have studied some techniques for time-series prediction based on regression. However, recently the recurring neural network technique has emerged as a good method for the generation of time-series predictions (LeCun et al, 2015, Camero et al, 2018. We plan to improve the fill-level predictions by using an algorithm based on recurring neural networks.…”

Section: Discussionmentioning

confidence: 99%

BIN-CT: Urban waste collection based on predicting the container fill level

Ferrer

Alba

2019

Biosystems

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The fast demographic growth, together with the population concentration in cities and the increasing amount of daily waste, are factors that are pushing to the limit the ability of waste assimilation by Nature. Therefore, we need technological means to optimally manage of the waste collection process, which represents 70% of the operational cost in waste treatment. In this article, we present a free intelligent software system called BIN-CT (BIN for the CiTy), based on computational learning algorithms, which plans the best routes for waste collection supported by past (historical) and future (predictions) data. The objective of the system is to reduction the cost of the waste collection service minimizing the distance traveled by a truck to collect the waste from a container, thereby reducing the fuel consumption. At the same time the quality of service for the citizen is increased, avoiding the annoying overflows of containers thanks to the accurate fill-level predictions given by BIN-CT. In this article we show the features of our software system, illustrating its operation with a real case study of a Spanish city. We conclude that the use of BIN-CT avoids unnecessary trips to containers, reduces the distance traveled to collect a container by 20%, and generates routes 33.2% shorter than the routes used by the company. Therefore it enables a considerable reduction of total costs and harmful emissions thrown up into the atmosphere.

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Section: Discussionmentioning

confidence: 99%

BIN-CT: Urban waste collection based on predicting the container fill level

Ferrer

Alba

2019

Biosystems

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“…In terms of predicting the parking occupancy rate, Zheng et al found that the Regression Tree method outperforms the other two algorithms they evaluated. Camero et al [13] presented a Recurrent Neural Network (RNN)-based approach to predict the number of free parking spaces. Their main aim was to improve the performance of the RNN.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction

Awan

Saleem

Minerva

et al. 2020

Sensors

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Machine/Deep Learning (ML/DL) techniques have been applied to large data sets in order to extract relevant information and for making predictions. The performance and the outcomes of different ML/DL algorithms may vary depending upon the data sets being used, as well as on the suitability of algorithms to the data and the application domain under consideration. Hence, determining which ML/DL algorithm is most suitable for a specific application domain and its related data sets would be a key advantage. To respond to this need, a comparative analysis of well-known ML/DL techniques, including Multilayer Perceptron, K-Nearest Neighbors, Decision Tree, Random Forest, and Voting Classifier (or the Ensemble Learning Approach) for the prediction of parking space availability has been conducted. This comparison utilized Santander’s parking data set, initiated while working on the H2020 WISE-IoT project. The data set was used in order to evaluate the considered algorithms and to determine the one offering the best prediction. The results of this analysis show that, regardless of the data set size, the less complex algorithms like Decision Tree, Random Forest, and KNN outperform complex algorithms such as Multilayer Perceptron, in terms of higher prediction accuracy, while providing comparable information for the prediction of parking space availability. In addition, in this paper, we are providing Top-K parking space recommendations on the basis of distance between current position of vehicles and free parking spots.

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“…In contrast, the use of the sensors has recently increased in various fields, such as anomaly detection, whose principles can be applied to DSD [4,34,35]. In the following sections, we discuss three deep learning algorithms used in sensing systems for anomaly detection [36][37][38].…”

Section: Deep Learning-based Sensorsmentioning

confidence: 99%

“…This residual error can be used to detect points of frequent anomalies. Experiments with actual IoT sensor data have indicated that abnormality detection using LSTM mainly detects the sudden change of a value efficiently [38].…”

Section: Long Short-term Memory (Lstm) Modelmentioning

confidence: 99%

An Automobile Environment Detection System Based on Deep Neural Network and its Implementation Using IoT-Enabled In-Vehicle Air Quality Sensors

Chung

Kim

2020

Sustainability

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This paper elucidates the development of a deep learning–based driver assistant that can prevent driving accidents arising from drowsiness. As a precursor to this assistant, the relationship between the sensation of sleep depravity among drivers during long journeys and CO2 concentrations in vehicles is established. Multimodal signals are collected by the assistant using five sensors that measure the levels of CO, CO2, and particulate matter (PM), as well as the temperature and humidity. These signals are then transmitted to a server via the Internet of Things, and a deep neural network utilizes this information to analyze the air quality in the vehicle. The deep network employs long short-term memory (LSTM), skip-generative adversarial network (GAN), and variational auto-encoder (VAE) models to build an air quality anomaly detection model. The deep learning models gather data via LSTM, while the semi-supervised deep learning models collect data via GANs and VAEs. The purpose of this assistant is to provide vehicle air quality information, such as PM alerts and sleep-deprived driving alerts, to drivers in real time and thereby prevent accidents.

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Evolutionary Deep Learning for Car Park Occupancy Prediction in Smart Cities

Cited by 78 publications

References 25 publications

BIN-CT: Urban waste collection based on predicting the container fill level

BIN-CT: Urban waste collection based on predicting the container fill level

A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction

An Automobile Environment Detection System Based on Deep Neural Network and its Implementation Using IoT-Enabled In-Vehicle Air Quality Sensors

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