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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Scalable Computing

ISSN

1895-1767

e-ISSN

1895-1767

Svazek periodika

24

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

RO - Rumunsko

Počet stran výsledku

11

Strana od-do

1191-1201

Kód UT WoS článku

001120913200046

EID výsledku v databázi Scopus

2-s2.0-85178226294