A Big-Data based and process-oriented decision support system for traffic management

TL;DR

This paper presents a big-data and process-oriented decision support system that enables real-time monitoring and analysis of traffic data, improving decision-making for traffic management.

Contribution

It introduces a novel integration of big-data and process-centric approaches into traffic management DSS for real-time insights.

Findings

Enhanced real-time traffic monitoring capabilities

Improved KPI generation from large traffic datasets

Successful integration with operational traffic systems

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.