Investigating Airport Bus Scheduling Optimization: A Case Study of the Beijing Capital International Airport

Air has become popular in China, leading to higher requirements for airport landside transportation. The airport bus has become an important transportation mode. In this paper, we investigate an airport bus scheduling problem to better meet people's daily travel demand. By analyzing the data from car-hailing and taxis, potential passenger volume of starting a new airport bus line is predicted. Next, by applying the K-Means clustering analysis, appropriate sites are determined based on the distribution of the passenger volume, and the most suitable route is selected by comparing the time cost and bus cost of passengers among different routes with the local search method. A multi-objective operation and scheduling model is developed, and the departure interval of the airport bus is estimated by adopting the improved genetic algorithm. The model is validated by a case study using data from the Beijing capital international airport.