Clustering Passenger Trip Data for the Potential Passenger Investigation and Line Design of Customized Commuter Bus

Abstract

Customized commuter bus (CCB) is a kind of innovative public transit service launched starting in 2013 in many cities all over the world. It is designed to meet the direct travel demands of commuters who have similar origin and destination locations during their long-distance commuting trips at peak hours. To identify the origin and destination distribution of potential CCB passengers, this paper proposes a pair wise density-based spatial clustering algorithm. With the proposed algorithm, the method to extract the potential CCB passengers from regular bus passengers based on the bus smart card data is introduced. Meanwhile, the discovered hot locations of potential CCB passengers can be regarded as the candidate locations of CCB stops and can be used to set candidate CCB lines. Finally, the demand survey data collected from the actual registered CCB passengers are applied to verify the accuracy and feasibility of the clustering results obtained by the proposed algorithm. The related findings could guide bus operators to design CCB lines and allocate vehicle capacities on different lines.