Abstract
Data analysis and monitoring of road networks in terms of reliability and performance are valuable but hard to achieve, especially when the analytical information has to be available to decision makers on time. The gathering and analysis of the observable facts can be used to infer knowledge about traffic congestion over time and gain insights into the roads safety. However, the continuous monitoring of live traffic information produces a vast amount of data that makes it difficult for business intelligence (BI) tools to generate metrics and key performance indicators (KPI) in nearly real-time. In order to overcome these limitations, we propose the application of a big-data based and process-centric approach that integrates with operational traffic information systems to give insights into the road network's efficiency. This paper demonstrates how the adoption of an existent process-oriented DSS solution with big-data support can be leveraged to monitor and analyse live traffic data on an acceptable response time basis.
Highlights
As McKinsey forecasted in a report in 2014, the top priority objective of IT organizations at nowadays is generating greater value from data [1]
In order to overcome these limitations, we propose the application of a big-data based and process-centric approach that integrates with operational traffic information systems to give insights into the road network's efficiency
We propose the adoption of a former work in the area of business process analytics to monitor and analyse the vehicles flows on roads for traffic management and control
Summary
As McKinsey forecasted in a report in 2014, the top priority objective of IT organizations at nowadays is generating greater value from data [1]. Whatever the case may be, research based on big data can be conducted in various ways, its basic purpose lies in handling huge amounts of data from technological, sociological, and economic systems to discover some hidden patterns [6]. Smart cities and their infrastructures are one of the main producers of data and, as a consequence of this, Big Data efforts with regards to urban or traffic data are one of the most important arenas for research. We present a big data based technological approach aimed to bring operational decision support technology to the traffic managers in order to help them drive traffic strategies on road networks
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