A Data-driven Region Generation Framework for Spatiotemporal
  Transportation Service Management
Liyue Chen Jiangyi Fang Zhe Yu Yongxin Tong Shaosheng Cao Leye Wang
MAUP (modifiable areal unit problem) is a fundamental problem for spatial
data management and analysis. As an instantiation of MAUP in online
transportation platforms, region generation (i.e., specifying the areal unit
for service operations) is the first and vital step for supporting
spatiotemporal transportation services such as ride-sharing and freight
transport. Most existing region generation methods are manually specified
(e.g., fixed-size grids), suffering from poor spatial semantic meaning and
inflexibility to meet service operation requirements. In this paper, we propose
RegionGen, a data-driven region generation framework that can specify regions
with key characteristics (e.g., good spatial semantic meaning and
predictability) by modeling region generation as a multi-objective optimization
problem. First, to obtain good spatial semantic meaning, RegionGen segments the
whole city into atomic spatial elements based on road networks and obstacles
(e.g., rivers). Then, it clusters the atomic spatial elements into regions by
maximizing various operation characteristics, which is formulated as a
multi-objective optimization problem. For this optimization problem, we propose
a multi-objective co-optimization algorithm. Extensive experiments verify that
RegionGen can generate more suitable regions than traditional methods for
spatiotemporal service management.