Bass + BL + seasonality forecasting method for demand trends in air rail integrated service

Abstract

The fast expansion of high speed train network acts as a double-edged sword for the development of air passenger transport all over the world. An air-rail integrated service (ARIS) has been regarded as a new trend for the air passenger transport. However, the launch of ARIS involves multiple stakeholders, mainly includes airport, regional railway bureau, airlines and passengers. Thus, the passenger demand forecasting of ARIS, directly impacts on the operations of both airports and airlines, further the development of both regional transport market and economics. This paper proposed a Bass + BL + Seasonality model, which combined Bass diffusion model, disaggregate choice model, and seasonal fluctuations to forecast the passenger demand and trend of ARIS. The ARIS of Shijiazhuang Airport in China was taken as an example to verify its performance. The results showed that compared with other typical methods, the proposed Bass + BL + Seasonality model could forecast the passenger demand trend of ARIS with higher accuracy.

KEYWORDS:

• Air passenger transport demand forecasting
• air-rail integrated service
• Bass + BL + seasonality

Acknowledgments

This work was supported by the Fundamental Research Funds for the Central Universities (2020RC010), the Key Project of National Social Science Fund of China (19VHQ012, 18VHQ005), National Natural Science Foundation of China (71621001, 91746201) and Key Project of National Natural Science Foundation of China (71431001).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 The ARIS of TSN has been upgraded four times. In the latest version of 4.0, TSN ARIS passengers have to pay for an HST ticket and accommodation themselves, which has changed the nature of TSN’s ARIS.

2 The SP data was from an air transport project, which was funded by the Liaoning Social Science Fund in 2013.

3 Considering the difficulty in collecting the personal information of passengers and the minimal impacts of personal factors on the utility of passengers, socio-demographic factors were omitted to simplify the model calculation.

Additional information

Funding

This work was supported by Fundamental Research Funds for the Central Universities: [Grant Number 2020RC010]; National Natural Science Foundation of China: [Grant Number 71621001,91746201]; Key Project of National Social Science Fund of China: [Grant Number 18VHQ005,19VHQ012].