SMART PARKING SYSTEM: OPTIMIZED ENSEMBLE DEEP LEARNING MODEL WITH INTERNET OF THINGS FOR SMART CITIES

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F23%3A50020885" target="_blank" >RIV/62690094:18450/23:50020885 - isvavai.cz</a>

Result on the web

<a href="https://www.scpe.org/index.php/scpe/article/view/2550" target="_blank" >https://www.scpe.org/index.php/scpe/article/view/2550</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.12694/scpe.v24i4.2550" target="_blank" >10.12694/scpe.v24i4.2550</a>

Result language

angličtina

Original language name

SMART PARKING SYSTEM: OPTIMIZED ENSEMBLE DEEP LEARNING MODEL WITH INTERNET OF THINGS FOR SMART CITIES

Original language description

In the recent era of smart city ecosystems and the Internet of Things (IoT), innovative, intelligent parking systems must make cities more sustainable. Every year, the increasing number of city vehicles requires more time to search for parking slots. In large cities, 10% of the traffic congestion occurs because of cruising; drivers spend almost 20 minutes searching for free space to park their vehicles. The passing time of waiting for parking in the traffic leads the issues such as energy, pollution, and stress. There needs to be more than the developed solutions. Therefore, the necessary to create a parking slot availability detection system that informs the drivers in advance about the free parking slot based on location. This paper introduces an enhanced ensemble Deep Learning (DL) model designed to forecast parking slot availability through the integration of IoT, cloud technology, and sensor networks. The devised model, known as Ensemble CNN-Boosted Graph LSTM (ECNN-BGLSTM), is optimized using a Genetic Algorithm (GA) framework. The model’s performance is rigorously evaluated using a dataset from Europe, and various metrics, including Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE), are employed for assessment. The experimental findings demonstrate the superior performance of the proposed model compared to existing state-of-the-art approaches. © 2023 SCPE.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Scalable Computing

ISSN

1895-1767

e-ISSN

1895-1767

Volume of the periodical

24

Issue of the periodical within the volume

4

Country of publishing house

RO - ROMANIA

Number of pages

11

Pages from-to

1191-1201

UT code for WoS article

001120913200046

EID of the result in the Scopus database

2-s2.0-85178226294