Browsing by Author "Alotaibi, Muteb Khodar"

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Managing congestion and service location at the Hajj using GIS and spatial modelling
(University of Leeds, 2023) Alotaibi, Muteb Khodar; Nick, Malleson; Graham, Clarke
Crowd management techniques have become a major concern for decision makers at major events. In this research, the application of a Location-Allocation Model and an Agent Based Model is used to locate services in an optimal fashion at the religious festival in Mina and to explore whether or not these service locations have any implications for pilgrims’ movement across the Mina road network. The first focus is on producing different demand layers within the location-allocation model (LAM), ultimately producing a dynamic layer which can represent pilgrims’ movement across Mina networks more realistically, in turn contributing to the allocation of different services such as health services and civil defence centres at different times of the day. It is argued that taking time into account is important for planning services more effectively throughout the festival. Second, we well focus specifically on the use of an agent-based model ( ABM) to look at the dynamics of crowd movements along the most congested routes and to explore how much congestion is ‘acceptable’. To define the ‘acceptable’ term here, we will use Fruin’s LOS category breakdown, which has become a standard tool for interpreting crowd congestion. Different scenarios in the analysis using both LAM and ABM will be explored to optimise services provision taking into account the safety of crowd moving around these services. From a planning point of view, the use of ABM is vital to support optimisation using LAM. ABM is a very important tool for investigating the appropriateness of a specific location for the centres or the roads themselves The results indicate how, decision makers should take both the results from LAM and ABM, in an integrated fashion, to improve pilgrims’ accessibility and safety while they are performing their ritual in Mina city and Mashaer .
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