sis 19(23): e4

Research Article

The impact of Planned Special Events (PSEs) on urban traffic congestion

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  • @ARTICLE{10.4108/eai.13-7-2018.159624,
        author={Ruwangi Fernando},
        title={The impact of Planned Special Events (PSEs) on urban traffic congestion},
        journal={EAI Endorsed Transactions on Scalable Information Systems},
        volume={6},
        number={23},
        publisher={EAI},
        journal_a={SIS},
        year={2019},
        month={7},
        keywords={spatial-temporal big data mining, Planned Special Events (PSEs), urban traffic congestion},
        doi={10.4108/eai.13-7-2018.159624}
    }
    
  • Ruwangi Fernando
    Year: 2019
    The impact of Planned Special Events (PSEs) on urban traffic congestion
    SIS
    EAI
    DOI: 10.4108/eai.13-7-2018.159624
Ruwangi Fernando1,*
  • 1: Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, Australia
*Contact email: Ruwangi.fernando@vu.edu.au

Abstract

The transport infrastructure of many cities has not been able to keep up with the pace of growth in the motorization rate or to counteract the intensification of urban traffic. Rising in traffic congestion in cities not only impacts the productivity costing billions but also responsible for more than 40% of all CO2 emissions which results in global warming. While expansion and construction of new roads may be considered in some cases, in most, better management of existing infrastructure to lower traffic congestion is the only option. The current state of the art commercial solutions can predict the recurring traffic situations, as this behavior can be easily learned from historical data. The challenge is to predict the non-recurrent congestion caused by events such as accidents, adverse weather, construction zones and Planned Special Event (PSE). Past research has shown, PSEs such as concerts or sports games, festivals and conventions has a huge impact on everyday urban transportation. Therefore, the aim of the proposed research is to investigate the impact PSEs on urban traffic congestion. The proposed research is applying spatial-temporal big data mining methods to predict the impact of PSEs on the urban concession. Specifically, the proposed research will consider the characteristics of PSEs such as location, type, duration, audience and time and day of the event in the proposed analysis approach which enables to predict of urban congestion for future PSEs.