Discrete-event simulation and queuing analysis of landside taxi dispatch logistics at KLIA Terminal 1

Abstract

Ground transportation systems are critical for managing fluctuating passenger volumes at major international airports such as Kuala Lumpur International Airport (KLIA) Terminal 1. During peak flight periods, sudden surges in passenger arrivals may create bottlenecks at taxi loading bays, resulting in long passenger waiting times and queue growth. This study evaluates taxi queueing dynamics at KLIA Terminal 1 under assumed demand and service conditions and examines potential operational strategies for reducing congestion. A hybrid approach combining an analytical M/M/12 queueing model and discrete-event simulation was employed. The simulation model was developed in Python using the SimPy framework. For the simulated peak-demand scenario, the assumed arrival rate was 300 passenger groups per hour, with 12 loading bays and an average service rate of 20 groups per hour per bay. This produced a traffic intensity of ρ = 1.25, indicating that demand exceeded service capacity under these assumptions. In the two-hour baseline simulation, the maximum queue length reached 75 passenger groups, while the maximum waiting time was 17.37 minutes. When the assumed service time was reduced to 2.5 minutes per group, equivalent to μ = 24 groups per hour per bay, the simulated maximum waiting time fell to 2.75 minutes and the maximum queue length fell to 15 groups. However, traffic intensity remained above one (ρ = 1.0417), so this scenario did not achieve steady-state stability. These findings suggest that faster dispatch and loading processes may reduce congestion during a finite peak period, but further evaluation using observed operational data is needed before drawing conclusions about long-term queue stability or recommending a specific intervention.