# A Data-driven Region Generation Framework for Spatiotemporal Transportation Service Management

arXiv: https://arxiv.org/abs/2306.02806 · HTML: https://mlsanity.com/paper/2306.02806/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 74 references that are in the map.
- First posted on arXiv 5 Jun 2023.

## Builds on (most cited first, 19 of 19)

- [Graph WaveNet for Deep Spatial-Temporal Graph Modeling](https://mlsanity.com/paper/1906.00121/) (2019)
- [GMAN: A Graph Multi-Attention Network for Traffic Prediction](https://mlsanity.com/paper/1911.08415/) (2019)
- [Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction](https://mlsanity.com/paper/1610.00081/) (2016)
- [Spatial-Temporal Graph ODE Networks for Traffic Flow Forecasting](https://mlsanity.com/paper/2106.12931/) (2021)
- [Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecasting](https://mlsanity.com/paper/2206.09112/) (2022)
- [Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting](https://mlsanity.com/paper/2206.09113/) (2022)
- [Quantifying Uncertainty in Deep Spatiotemporal Forecasting](https://mlsanity.com/paper/2105.11982/) (2021)
- [Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach](https://mlsanity.com/paper/1706.06279/) (2017)
- [Preserving Dynamic Attention for Long-Term Spatial-Temporal Prediction](https://mlsanity.com/paper/2006.08849/) (2020)
- [Deep Spatially and Temporally Aware Similarity Computation for Road Network Constrained Trajectories](https://mlsanity.com/paper/2112.09339/) (2021)
- [Recent Advances in Graph Partitioning](https://mlsanity.com/paper/1311.3144/) (2013)
- [Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data](https://mlsanity.com/paper/2006.12715/) (2020)
- [Fluid Communities: A Competitive, Scalable and Diverse Community Detection Algorithm](https://mlsanity.com/paper/1703.09307/) (2017)
- [Exploring the Generalizability of Spatio-Temporal Traffic Prediction: Meta-Modeling and an Analytic Framework](https://mlsanity.com/paper/2009.09379/) (2020)
- [Modeling Network-level Traffic Flow Transitions on Sparse Data](https://mlsanity.com/paper/2208.06646/) (2022)
- [BusTr: Predicting Bus Travel Times from Real-Time Traffic](https://mlsanity.com/paper/2007.00882/) (2020)
- [Polestar: An Intelligent, Efficient and National-Wide Public Transportation Routing Engine](https://mlsanity.com/paper/2007.07195/) (2020)
- [Real-World Trajectory Sharing with Local Differential Privacy](https://mlsanity.com/paper/2108.02084/) (2021)
- [Public Transport Planning: When Transit Network Connectivity Meets Commuting Demand](https://mlsanity.com/paper/2103.16084/) (2021)

## Built on by (most cited first, 6 of 6)

- [A Unified Model for Spatio-Temporal Prediction Queries with Arbitrary Modifiable Areal Units](https://mlsanity.com/paper/2403.07022/) (2024)
- [Multimodal joint prediction of traffic spatial-temporal data with graph sparse attention mechanism and bidirectional temporal convolutional network](https://mlsanity.com/paper/2412.19842/) (2024)
- [Sea-cret Agents: Maritime Abduction for Region Generation to Expose Dark Vessel Trajectories](https://mlsanity.com/paper/2502.01503/) (2025)
- [Revisiting Day-ahead Electricity Price: Simple Model Save Millions](https://mlsanity.com/paper/2405.14893/) (2024)
- [DRL4AOI: A DRL Framework for Semantic-aware AOI Segmentation in Location-Based Services](https://mlsanity.com/paper/2412.05437/) (2024)
- [UCTB: An Urban Computing Tool Box for Building Spatiotemporal Prediction Services](https://mlsanity.com/paper/2306.04144/) (2023)

## Abstract (arXiv)

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.