A dynamic line generation and vehicle scheduling method for airport bus line based on multi-source big travel data

Abstract

Airport bus is an important public transportation mode for large international airport. To improve the bus station coverage, passenger demand compatibility and the scheduling flexibility of Beijing International Airport bus line, a dynamic line generation and vehicle scheduling method is proposed in this paper. Firstly, based on multi-source big data from the airport (including data from taxi, ride-hailing service, subway, regular bus, airport bus, etc.), we accurately extract candidate stations, which are very popular with passengers and convenient for parking and transfer, through public transportation demand level calculation, iterative clustering and POI matching. Then, the candidate stations need to be partitioned appropriately by selecting suitable features and calculating the similarity of candidate stations, so as to make the stations within each group a moderate size and have a consistent spatial orientation. Finally, a line generation and vehicle scheduling algorithm, which is compatible with multi-vehicle, high success rate of ride-sharing matching and low cost, is designed to realize accurate and rapid operation scheduling within each group according to the situation of passengers booking tickets. We have carried out experiments in Wangjing and Yayuncun, and the results show that our method can satisfy passenger demand fast and accurately.