An Efficient Approach for Identifying Potential Bus Passenger Demand Based on Multisource Data

Big data provide massive samples and resources for exploring the operating rules of public transportation. This article proposes a method that combines multiple data sources to identify potential bus passenger flows, aiming to address the issue of insufficient identification accuracy with a single data source. First, the spatially weighted K-means algorithm and improved DBSCAN algorithm are designed to partition traffic zones and residents’ travel flow OD is extracted based on mobile phone signaling data. Second, using bus IC card data and vehicle trajectory data, a method for identifying bus passenger boarding and alighting stops based on spatiotemporal clustering is proposed and the bus passenger flow OD for each traffic zone is calculated. By comparing the resident travel flow OD with the bus passenger flow OD, we set a threshold for the potential bus passenger demand proportion. Finally, the analysis is conducted using actual data from a city in central China. The city is divided into 43 traffic zones, with the maximum bus passenger flow proportion between zones being 14.9%, the minimum being 5.0%, and the average being 7.2%. The initial threshold for the potential bus passenger demand proportion is thus set to 7.2%, and a sensitivity analysis is conducted by gradually decreasing the threshold in increments of 0.5% to 6.7%, 6.2%, 5.7%, and 5.2%. The corresponding potential bus passenger demand OD pairs between traffic zones are identified as 419, 358, 245, 151, and 51. Urban managers should focus on the 51 pairs with relatively large potential flows to gradually optimize and balance the development of the bus network based on actual conditions. The method proposed provides important theoretical and practical support for effectively optimizing urban bus networks. However, there are limited indicators for identifying potential passenger flows; in the future, more multidimensional indicators will be taken into consideration.