Baggage Towing Tractor Demand Formation Within a Three-Hour Arrival Time Window at Soekarno-Hatta Terminal 3
DOI:
https://doi.org/10.55324/ijoms.v5i9.1301Keywords:
actual time arrival, aircraft movement, airport ground handling, baggage towing tractor, demand formationAbstract
Efficient airport ground handling is essential for maintaining aircraft turnaround performance while reducing operational costs and environmental impacts. Among Ground Support Equipment (GSE), the Baggage Towing Tractor (BTT) plays a critical role in transporting baggage between aircraft parking stands and baggage breakdown areas. However, BTT demand is often estimated directly from aircraft movements, leading to inaccurate workload assessments because not all arrivals generate baggage transport tasks. This study aimed to develop a structured demand formation framework for BTT operations within the busiest three-hour arrival time window at Terminal 3 of Soekarno-Hatta International Airport. A quantitative operational data-processing approach was employed using 2025 aircraft movement records, baggage weight data, aircraft type, parking stand information, route classification, and BTT operational parameters. The methodology incorporated multi-stage data filtering, service-scope identification, baggage-cart conversion, BTT trip estimation, and spatial node assignment to produce structured routing inputs. The results showed that 57,615 annual aircraft movements were reduced to 27,514 operator-scope movements after filtering, including 13,614 arrivals. The peak arrival period of 12:00–14:59 contained 3,318 arrivals, of which 3,198 represented active baggage-handling demands. These demands generated 8,511,498 kg of baggage, corresponding to 7,031 baggage carts and 3,315 estimated BTT trips. A representative daily scenario produced 13 active demands, 30 baggage carts, and 13 BTT trips. The proposed framework provides a robust and transferable method for transforming aircraft movement data into structured BTT demand records, establishing a reliable foundation for subsequent Vehicle Routing Problem with Time Windows (VRPTW)-based route optimization, fuel consumption analysis, and carbon emission assessment.
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