Scalable System for Smart Urban Transport Management
Efficient management of smart transport systems requires the integration of various sensing technologies, as well as fast processing of a high volume of heterogeneous data, in order to perform smart analytics of urban networks in real time. However, dynamic response that relies on intelligent demand...
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Series: | Journal of Advanced Transportation |
Online Access: | http://dx.doi.org/10.1155/2020/8894705 |
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doaj-3662703a7c37443199a51d45fe60f4192020-11-25T03:39:54ZengHindawi-WileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/88947058894705Scalable System for Smart Urban Transport ManagementNauman Ahmad Khan0Jean-Christophe Nebel1Souheil Khaddaj2Vesna Brujic-Okretic3School of Computer Science & Mathematics, Kingston University London, Kingston KT1 2EE, UKSchool of Computer Science & Mathematics, Kingston University London, Kingston KT1 2EE, UKSchool of Computer Science & Mathematics, Kingston University London, Kingston KT1 2EE, UKSchool of Computer Science & Mathematics, Kingston University London, Kingston KT1 2EE, UKEfficient management of smart transport systems requires the integration of various sensing technologies, as well as fast processing of a high volume of heterogeneous data, in order to perform smart analytics of urban networks in real time. However, dynamic response that relies on intelligent demand-side transport management is particularly challenging due to the increasing flow of transmitted sensor data. In this work, a novel smart service-driven, adaptable middleware architecture is proposed to acquire, store, manipulate, and integrate information from heterogeneous data sources in order to deliver smart analytics aimed at supporting strategic decision-making. The architecture offers adaptive and scalable data integration services for acquiring and processing dynamic data, delivering fast response time, and offering data mining and machine learning models for real-time prediction, combined with advanced visualisation techniques. The proposed solution has been implemented and validated, demonstrating its ability to provide real-time performance on the existing, operational, and large-scale bus network of a European capital city.http://dx.doi.org/10.1155/2020/8894705 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Nauman Ahmad Khan Jean-Christophe Nebel Souheil Khaddaj Vesna Brujic-Okretic |
spellingShingle |
Nauman Ahmad Khan Jean-Christophe Nebel Souheil Khaddaj Vesna Brujic-Okretic Scalable System for Smart Urban Transport Management Journal of Advanced Transportation |
author_facet |
Nauman Ahmad Khan Jean-Christophe Nebel Souheil Khaddaj Vesna Brujic-Okretic |
author_sort |
Nauman Ahmad Khan |
title |
Scalable System for Smart Urban Transport Management |
title_short |
Scalable System for Smart Urban Transport Management |
title_full |
Scalable System for Smart Urban Transport Management |
title_fullStr |
Scalable System for Smart Urban Transport Management |
title_full_unstemmed |
Scalable System for Smart Urban Transport Management |
title_sort |
scalable system for smart urban transport management |
publisher |
Hindawi-Wiley |
series |
Journal of Advanced Transportation |
issn |
0197-6729 2042-3195 |
publishDate |
2020-01-01 |
description |
Efficient management of smart transport systems requires the integration of various sensing technologies, as well as fast processing of a high volume of heterogeneous data, in order to perform smart analytics of urban networks in real time. However, dynamic response that relies on intelligent demand-side transport management is particularly challenging due to the increasing flow of transmitted sensor data. In this work, a novel smart service-driven, adaptable middleware architecture is proposed to acquire, store, manipulate, and integrate information from heterogeneous data sources in order to deliver smart analytics aimed at supporting strategic decision-making. The architecture offers adaptive and scalable data integration services for acquiring and processing dynamic data, delivering fast response time, and offering data mining and machine learning models for real-time prediction, combined with advanced visualisation techniques. The proposed solution has been implemented and validated, demonstrating its ability to provide real-time performance on the existing, operational, and large-scale bus network of a European capital city. |
url |
http://dx.doi.org/10.1155/2020/8894705 |
work_keys_str_mv |
AT naumanahmadkhan scalablesystemforsmarturbantransportmanagement AT jeanchristophenebel scalablesystemforsmarturbantransportmanagement AT souheilkhaddaj scalablesystemforsmarturbantransportmanagement AT vesnabrujicokretic scalablesystemforsmarturbantransportmanagement |
_version_ |
1715153266177736704 |