Estimation Passenger Transfer Demand Multimodal Split in a High-Speed Railway Hub Based on Multi-Source Data Fusion

Abstract:

In order to accurately identify travel mode split rate of transfer passenger flow in high-speed rail hubs， a travel mode choice model is proposed based on a generalized trip chain model based on multimodal public transportation big data. Through the correlation and fusion of different public transportation modes in the transfer phase， the individual generalized trip chain with the high-speed rail hub as the origin is extracted. Then， the temporal-spatial distribution characteristics of the transfer flow in high-speed rail hubs are analyzed. Comprehensively considering the influence of individual economic and social attributes， and subjective psychological factors in combination with individual travel characteristics on transfer mode choice behavior， a transfer mode choice model for passengers in high-speed rail hubs is proposed based on multiple indicators and multiple causes （MIMIC） and multi-nominal logit（MNL） model. Taking the actual data of Beijing South Railway Station as the input， the estimated travel mode split rate of the commuting passenger flow is obtained. A comparison and analysis of ground true data indicates that the estimation error is within an acceptable range.