Intelligent passenger guidance system is able to improve travel experience for passengers, and to enhance airport operating efficiency. In this paper, an airport arrival passengers trip chain generating method based on multiple spatiotemporal traveling segments is proposed. Trip chain is divided into two spatiotemporal segments: in and out of the airport terminal building. For the segment of in-door situation, the cost time is calculated with walking time of different infrastructures and waiting time in the transit. For the segment of out-door situation, carrying time on carriers has been estimated. BPR model and queue theory are applied to estimate the walking time, and BP neural network is adopted to evaluate the carrying time. A passenger guidance platform is built to verify the proposed method, and the real scenario test is conducted in Beijing Capital International Airport. It’s found that the method can accurately estimate time cost comparing with passenger historical data, and the optimized trip chain can notably reduce passenger travel time.

CICTP