Urban multimodal public transport system with rail transit as the backbone and bus as the collaborative operation is an important carrier for the travel of urban residents. For improving efficiency of urban public transport services, it is important to optimize the feeder bus operation plans and promote efficient bridging. A quantitative method based on the data envelope analysis (DEA) algorithm was proposed to effectively evaluate the transfer efficiency of transit hubs and identify service bottlenecks, which used multi-source data including smart card transaction data, bus vehicle operation data and public transport trip chain data based on multimodal individual travel transaction data. Considering the passenger transfer and supply capacity of the feeder bus, and balancing between transfer and regular travel needs, a collaborative optimization model based on the branch and bound (BB) algorithm which can quickly obtain the absolute optimal solution of small-scale optimization problems is established to combinatorial optimize less efficient feeder bus line groups. The Tiantongyuan North transit hub and its feeder bus line in Beijing were selected as the case analysis. The results indicated that the main reasons for the failure of public transport to achieve DEA effectiveness are the departure frequency and the number of operating buses. With the collaborative optimization of the bus routing schedule, the average total transfer time for the transfer passengers is reduced by 19.9%, while the transfer time and waiting time are reduced by 22.8%, 16.1% respectively, which will help improve the efficiency and service level of multimodal public transport collaborative operation.
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