sis 18(17): e5

Research Article

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

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  • @ARTICLE{10.4108/eai.29-5-2018.154810,
        author={Alejandro Vera-Baquero and Ricardo Colomo-Palacios},
        title={A Big-Data based and process-oriented decision support system for traffic management},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={5},
        number={17},
        publisher={EAI},
        journal_a={SIS},
        year={2018},
        month={5},
        keywords={Traffic Management, Big Data, Cloud Computing, Big Data Analytics, Business Process, Process Monitoring, Event-Driven, Business Intelligence, Decision Support Systems.},
        doi={10.4108/eai.29-5-2018.154810}
    }
    
  • Alejandro Vera-Baquero
    Ricardo Colomo-Palacios
    Year: 2018
    A Big-Data based and process-oriented decision support system for traffic management
    SIS
    EAI
    DOI: 10.4108/eai.29-5-2018.154810
Alejandro Vera-Baquero1,*, Ricardo Colomo-Palacios2
  • 1: Universidad Carlos III de Madrid, Av. de la Universidad, 30, 28911 Leganés, Madrid, Spain.
  • 2: Ostfold University College, B R A Veien 4, 1783 Halden, Norway.
*Contact email: averabaq@gmail.com

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.