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.

翻译：可修改面积单元问题（MAUP）是空间数据管理与分析中的基本问题。作为在线交通平台中MAUP的具体实例，区域生成（即指定服务运营的面积单元）是支撑拼车、货运等时空交通服务的首要关键步骤。现有区域生成方法多采用人工指定方式（如固定尺寸网格），存在空间语义性差、难以灵活满足服务运营需求等问题。本文提出RegionGen数据驱动区域生成框架，通过将区域生成建模为多目标优化问题，能够生成兼具空间语义性与可预测性等关键特性的区域。首先，为获得良好的空间语义性，RegionGen基于道路网络与障碍物（如河流）将城市分割为原子空间单元；继而将这些原子空间单元聚类为区域，通过最大化多种运营特性指标构建多目标优化问题。针对该优化问题，我们提出多目标协同优化算法。大量实验证明，相较于传统方法，RegionGen能为时空服务管理生成更适配的区域。