Modeling and Simulation of Departure Passenger’s Behavior Based on an Improved Social Force Approach: A Case Study on an Airport Terminal in China

Abstract

The unprecedented growth of passenger throughput in large airport terminals highlights the importance of analyzing passengers’ movement to achieve airport terminal’s elaborate management. Based on the theory of original social force model, video data from a departure hall of a large airport terminal in China were analyzed to summarize passengers’ path planning characteristics. Then, a double-level model was established to describe passengers’ path planning behaviors. At the decision level of the proposed model, the avoiding force model including common avoiding force and additional horizontal avoiding force was established on the basis of setting time and space limitations for taking avoiding action and was used to describe passengers’ path planning in close-range space. At the tactical level of the proposed model, the route and node choice models were established to describe passengers’ path planning in long-range space. In the route choice model, a distribution of intermediate destination areas was proposed, with detouring distance, pedestrian density, speed difference, and pedestrian distribution considered in choosing an intermediate destination area. In the node choice model, the walking distance, the quantity of people waiting, and luggage were considered in choosing a check-in counter or security check channel. The main parameters of the proposed model were confirmed according to video data. Simulation results show that the proposed model can simulate departure passengers’ path planning behaviors at an acceptable accuracy level.

Based on Hierarchical Reinforcement Learning for Large-scale Pedestrian Trajectory Prediction

“…Zhang Yaping et al [3] analyzed the passenger behavior in the departure hall through video data analysis of an airport in China, and established a departure passenger path model by improving the social force model, which simulated the path planning behavior of passengers in the public pedestrian space and service facility selection area of the departure hall at an acceptable accuracy level.…”

Towards simulation optimization of subway station considering refined passenger behaviors

“…Soltani et al [ 9 ] used two software programs, Aimsun and Path Finder, to examine the service levels of pedestrian pathways and corridors at the Sadeghiyeh urban train station in Tehran, indicated the service levels of the main hall, north entrance, and south corridor of the station. Additionally, numerous scholars have made multiple achievements in areas such as simulating pedestrian behavior in subway stations [ 10 , 11 ], airport terminals [ 12 ], stairs [ 13 ], irregular channels (L [ 14 ], T [ 15 ], Y [ 16 ], Z [ 17 ]), and evacuation conditions [ 11 , 18 , 19 ].…”

Simulation and optimization of inbound passenger flow line in large-scale railway station

“…It is found that passenger flow density can be changed by taking economic or administrative measures. Zhang et al [8] built a improved social force model to simulate passengers' path-choice behaviors in departure halls, emphasizing that airport congestion caused by increasingly throughput and insufficient space capacity should be solved with flow path optimization.…”

“…Notably, Social force model [21] and cellular automata model [22] are frequently adopted in microscopic simulation of pedestrian traffic, and the main differences between the two models are shown in Tab. Ⅱ [8]. Seeing that the flow line simulation model in transportation hubs requires high fidelity of pedestrian activities, the social force model is preferred in this paper [23].…”