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Accurately predicting taxi-in times for arrival flights is crucial for efficient ground handling resource allocation, impacting flight departure timeliness. This study investigates terminal layout characteristics, specifically decentralised layouts, to predict and analyse arrival flight taxi-in times. We develop a surface traffic flow calculation method considering arrival and departure flights, eliminating fixed thresholds. We introduce runway-crossing operations for decentralised airports, creating new prediction variables. We consider factors like runway, aircraft type, airline, taxi distance, and time periods. Gradient Boosting Regression Tree predicts taxi-in times, while Lasso analyses factor impact. Our approach yields highly accurate predictions for decentralised airports, with Surface traffic flow and Runway-crossing variables significantly influencing taxi-in times. This research informs airport managers in decentralised layouts, enabling tailored management strategies.