Conference
From Static Logistics to Dynamic Ecosystems: Evaluating the Emergence of an Event Mobility Governance Framework in Dubai's Mega-Event Sector
Abstract
Mega-events compress months of ordinary travel demand into days, and they do so on top of a city
that must keep working. This white paper examines how Dubai has moved from event-specific,
largely reactive transport logistics towards a formalised Event Mobility Governance Framework
(EMGF) that treats surge mobility as a permanent institutional capability rather than a temporary
project. Drawing on the mobilities paradigm and on collaborative and adaptive governance theory,
we describe the framework as a four-layer architecture: a tripartite institutional layer (Roads and
Transport Authority, Dubai Police and venue authorities), a shared data and decision layer, a set
of supply, demand and network levers, and an outcomes layer. Two contributions are original to
this paper. The first is the Event Mobility Governance Maturity Index (EMGMI), a weighted five
dimension composite that scores how far a jurisdiction has institutionalised event mobility. The
second is the Surge Absorption Ratio (SAR), a simple published-data metric of how much of an
event's attendance the public transport system actually carries. We compare two embedded cases
within one jurisdiction, Expo 2020 (a supply-side, fixed-infrastructure baseline) and COP28 (a
demand-side, predictive model), against a pre-framework baseline. Because passenger-level
operational data are not public, the inferential analysis uses an illustrative event-day panel (n =
255) calibrated to published RTA magnitudes; it is presented as a reproducible analytic template
rather than as audited evidence. On that panel, one-way ANOVA shows large regime effects on
corridor delay (F(2, 252) = 53.3, η² = 0.30), crowd dispersal time and incident response, and
hierarchical OLS shows that public-transport share and digital integration jointly explain most of
the regime difference (adjusted R² rising from 0.05 to 0.47). The EMGMI rises from 30.5
(baseline) to 59.7 (Expo 2020) and 79.1 (COP28)
that must keep working. This white paper examines how Dubai has moved from event-specific,
largely reactive transport logistics towards a formalised Event Mobility Governance Framework
(EMGF) that treats surge mobility as a permanent institutional capability rather than a temporary
project. Drawing on the mobilities paradigm and on collaborative and adaptive governance theory,
we describe the framework as a four-layer architecture: a tripartite institutional layer (Roads and
Transport Authority, Dubai Police and venue authorities), a shared data and decision layer, a set
of supply, demand and network levers, and an outcomes layer. Two contributions are original to
this paper. The first is the Event Mobility Governance Maturity Index (EMGMI), a weighted five
dimension composite that scores how far a jurisdiction has institutionalised event mobility. The
second is the Surge Absorption Ratio (SAR), a simple published-data metric of how much of an
event's attendance the public transport system actually carries. We compare two embedded cases
within one jurisdiction, Expo 2020 (a supply-side, fixed-infrastructure baseline) and COP28 (a
demand-side, predictive model), against a pre-framework baseline. Because passenger-level
operational data are not public, the inferential analysis uses an illustrative event-day panel (n =
255) calibrated to published RTA magnitudes; it is presented as a reproducible analytic template
rather than as audited evidence. On that panel, one-way ANOVA shows large regime effects on
corridor delay (F(2, 252) = 53.3, η² = 0.30), crowd dispersal time and incident response, and
hierarchical OLS shows that public-transport share and digital integration jointly explain most of
the regime difference (adjusted R² rising from 0.05 to 0.47). The EMGMI rises from 30.5
(baseline) to 59.7 (Expo 2020) and 79.1 (COP28)
Keywords
event mobility governance
mega-events
smart cities
collaborative governance
intelligent transport systems
Dubai
Expo 2020
COP28
regression
ANOVA
