Our paper on Understanding Operation Behaviors of Taxicabs in Cities by Matrix Factorization has been published on Computers, Environment and Urban Systems.


Taxicabs play significant roles in public transport systems in large cities. To meet repetitive demands of daily intra-urban travels, cabdrivers acquire self-organized habitual operation behaviors in space and time, largely with assistance of their longitudinal operating experience. Recognizing those collective operation behavior patterns of taxicabs will enable us to better design and implement public transport services and urban development plan. In this paper, we systematically study patterns of the spatial supply of 6,000+ taxicabs in Wuhan, China based on their monthly digital traces and the non-negative matrix factorization method. We successfully identify a set of high-level statistical features of the spatial operation behaviors of taxicabs in Wuhan, providing valuable insights to our knowledge of the demand and supply of taxicabs in (similar) large cities. First, we decouple several spatially cohesive regions with intensive internal taxicab travels (termed as demand regions), which intuitively reveal the well-known multi-sectored urban configuration of Wuhan. Second, by applying the non-negative matrix factorization to taxicab’s longitudinal traces, we uncover remarkably self-organized operation patterns of cabdrivers in space (termed as supply regions) as reactions to the sectored distribution of daily travel behaviors. We find that a large proportion of cabdrivers frequently operate within specific single service area and a small proportion of taxicabs works as shifting tools between different service areas. Last, we focus on performances of taxicabs with distinct spatial operation behaviors and unveil their statistical characteristics in terms of frequency, duration and distance with passenger on board. Our work demonstrates the great potential to understand and improve urban mobility and public transport system from cabdrivers’ collective intelligence.

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