| Title |
MOBDA: Microservice-Oriented Big Data Architecture for Smart City Transport Systems |
| ID_Doc |
40884 |
| Authors |
Asaithambi, SPR; Venkatraman, R; Venkatraman, S |
| Title |
MOBDA: Microservice-Oriented Big Data Architecture for Smart City Transport Systems |
| Year |
2020 |
| Published |
Big Data And Cognitive Computing, 4, 3 |
| DOI |
10.3390/bdcc4030017 |
| Abstract |
Highly populated cities depend highly on intelligent transportation systems (ITSs) for reliable and efficient resource utilization and traffic management. Current transportation systems struggle to meet different stakeholder expectations while trying their best to optimize resources in providing various transport services. This paper proposes a Microservice-Oriented Big Data Architecture (MOBDA) incorporating data processing techniques, such as predictive modelling for achieving smart transportation and analytics microservices required towards smart cities of the future. We postulate key transportation metrics applied on various sources of transportation data to serve this objective. A novel hybrid architecture is proposed to combine stream processing and batch processing of big data for a smart computation of microservice-oriented transportation metrics that can serve the different needs of stakeholders. Development of such an architecture for smart transportation and analytics will improve the predictability of transport supply for transport providers and transport authority as well as enhance consumer satisfaction during peak periods. |
| Author Keywords |
smart technologies; intelligent transportation systems; big data; microservice-oriented big data architecture; data analytics |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Emerging Sources Citation Index (ESCI) |
| EID |
WOS:000697677900002 |
| WoS Category |
Computer Science, Artificial Intelligence; Computer Science, Information Systems; Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
https://www.mdpi.com/2504-2289/4/3/17/pdf
|