🤖 AI Summary
This study addresses the multi-stage coordination bottlenecks arising from the coupling of passenger and vehicle flows during peak periods in airport landside operations. The authors develop a five-minute-resolution state evolution model that integrates passenger arrivals, vehicle supply, shuttle services, storage capacity, and roadway capacity. They introduce novel diagnostic dimensions—including a composite congestion severity index and shadow price-based levers—and, for the first time, apply QUBO (Quadratic Unconstrained Binary Optimization) modeling to landside scheduling. A hybrid approach combining finite-action model predictive control with a QUBO-inspired simulated annealing algorithm enables differentiated, bottleneck-targeted dynamic scheduling strategies. Evaluated under intense peak scenarios at Shanghai Pudong and Hangzhou Xiaoshan airports, the method reduces passenger queue lengths from 3,445 to 2,477 and from 2,053 to 1,482, respectively, while maintaining robust congestion mitigation under various disturbances.
📝 Abstract
Airport landside traffic centers connect terminal arrivals with taxis, ride-hailing vehicles, private cars, buses, metro services, parking facilities, and terminal-area roadways. Peak arrivals can create coupled congestion across passenger queues, vehicle queues, pickup berths, storage areas, and access roads. This study proposes a QUBO-inspired computational framework for bottleneck diagnosis and dynamic dispatch in this setting. Shanghai Pudong International Airport and Hangzhou Xiaoshan International Airport serve as case airports. A five-minute state model links passenger arrivals, vehicle supply, pickup berth service, vehicle storage, and road capacity. Bottleneck diagnosis uses service intensity, road demand saturation, bottleneck frequency, queue severity, shadow-price leverage, and a composite congestion severity index. Two dispatch schemes are tested under consistent demand inputs: finite-action model predictive control and quadratic-unconstrained-binary-optimization-inspired simulated annealing. In the strong-peak baseline scenario, the QUBO-inspired method reduces the final passenger queue from 3445 to 2477 passengers at Shanghai Pudong and from 2053 to 1482 passengers at Hangzhou Xiaoshan. Case results indicate different dominant bottlenecks. Shanghai Pudong is more affected by road saturation, whereas Hangzhou Xiaoshan is more affected by pickup berth service. Robustness tests under demand, supply, service, road-capacity, modal-share, and random-noise perturbations show retained queue-reduction benefits under the tested uncertainty levels.