Abstract

Airports are more than aviation facilities; they function as critical nodes within modern urban mobility systems. In globally connected cities such as Dubai, airport performance directly influences tourism, trade, economic activity, and the overall efficiency of the transport network. At the same time, airports generate large volumes of detailed operational data on a daily basis. However, this information is often summarized into high-level statistics that do not fully capture temporal variations, congestion patterns, or operational reliability. This thesis investigates how flight-level operational data can be used to evaluate airport mobility performance in Dubai within a smart city context. The study focuses on Dubai International Airport (DXB) and DubaiWorld Central (DWC), two airports with distinct operational roles within the same metropolitan aviation system. Guided by four research questions, the research examines how airport performance can be evaluated using flight operations data, identifies temporal delay patterns, compares operational performance between DXB and DWC, and explores how these insights can support smarter urban mobility planning and data-driven governance. The research adopts a quantitative methodology based on secondary data analysis. Openaccess flight arrival and departure records obtained from the Dubai Pulse platform form the primary dataset. Each record contains scheduled and actual operational timestamps, enabling delay calculations at minute-level resolution. The dataset was cleaned and standardized before calculating key performance indicators including average delay, standard deviation, and On- Time Performance using the industry 15-minute threshold. Descriptive statistics, temporal analysis, and comparative evaluation were applied to examine airport efficiency, reliability, and congestion dynamics. The findings reveal clear temporal variations in airport performance, with identifiable peak congestion windows and recurring delay patterns throughout the day. Dubai International Airport, operating as a high-density international hub, demonstrates higher delay variability during synchronized arrival and departure banks. In contrast, DubaiWorld Central exhibits lower average delay levels but greater sensitivity to isolated disruptions due to its lower traffic density. These results demonstrate that airport mobility performance is strongly influenced by operational role, traffic intensity, and temporal demand cycles. The study concludes that open flight operations data represent a valuable yet underutilized resource for evaluating airport mobility within smart city environments. By framing airport operations as part of the broader urban mobility ecosystem, the research proposes a practical data-driven framework for monitoring airport performance and supporting evidence-based planning. The findings highlight the importance of integrating airport analytics into wider smart mobility systems to improve operational efficiency, reliability, and governance. Future research could expand this framework by incorporating additional variables such as weather conditions, passenger volumes, and predictive analytics techniques.

Publication Date

5-2026

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Ioannis Karamitsos

Campus

RIT Dubai

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