Fernando, RPF (2021) The impact of Planed Special Events (PSE's) on urban traffic congestion. PhD thesis, Victoria University.

Abstract

Traffic congestion has been on the rise in much of the world, and all indications are that it will continue to worsen, posing a danger to the quality of life in the city. Its main manifestation is the progressive reduction of traffic speeds, which translates into increases in travel times, less reliable travel times, increased fuel consumption, and other operating costs and air pollution. There are two types of congestion: recurrent congestion and non-recurrent congestion. Non-recurring congestion refers to a type of congestion that occurs irregularly, usually associated with events such as traffic accidents, road repairs, bad driving practices, etc. In general, these events reduce the capacity of the transport system and happen independently of the increase in demand associated with peak hours (recurrent congestion). Unlike recurrent congestion, the characteristics of non-recurrent congestion, that is, frequency, duration and severity, have not been addressed in the local environment, and therefore there is not enough background to estimate the cost of these externalities in the operation of the transport system. Given the above, it is possible to reason that there will be certain thresholds from which it is possible to propose incident management plans such that the cost of their implementation is lower than the savings obtained from the operation of such plans, and in this way justify traffic management plans that can mitigate the effects of non-recurring congestion. Past research has shown that PSEs such as concerts or sports games, festivals, and conventions significantly impact everyday urban transportation. Therefore, the doctoral thesis investigates the impact of PSEs on urban traffic congestion. This research utilises event characterise, mobility behaviour and urban traffic accidents to examine the impact of PSEs on urban road traffic congestion by applying spatial-temporal data mining methods. This research will contribute to the gap in the existing literature on event-driven traffic predictions, allowing more precise planning of urban mobility services in the presence of PSEs.

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