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

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Leye WangAuthors Info & Claims

KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Pages 3842 - 3854

https://doi.org/10.1145/3580305.3599760

Published: 04 August 2023 Publication History

Abstract

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.

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Cited By

Fang JChen LChai DHong YXie XChen LWang L(2024)UCTB: An Urban Computing Tool Box for Building Spatiotemporal Prediction Services2024 IEEE International Conference on Software Services Engineering (SSE)10.1109/SSE62657.2024.00020(54-65)Online publication date: 7-Jul-2024
https://doi.org/10.1109/SSE62657.2024.00020
Chen LFang JLiu TCao SWang L(2024)A Unified Model for Spatio-Temporal Prediction Queries with Arbitrary Modifiable Areal Units2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00111(1352-1365)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00111
Zhang DYan JPolat KAlhudhaif ALi J(2024)Multimodal joint prediction of traffic spatial-temporal data with graph sparse attention mechanism and bidirectional temporal convolutional networkAdvanced Engineering Informatics10.1016/j.aei.2024.10253362(102533)Online publication date: Oct-2024
https://doi.org/10.1016/j.aei.2024.102533

Index Terms

A Data-driven Region Generation Framework for Spatiotemporal Transportation Service Management
1. Applied computing
  1. Operations research
    1. Transportation
2. Information systems
  1. Information systems applications
    1. Spatial-temporal systems

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cover image ACM Conferences

KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 2023

5996 pages

ISBN:9798400701030

DOI:10.1145/3580305

General Chairs:
Ambuj Singh
UC Santa Barbara, USA
,
Yizhou Sun
UC Los Angeles, USA
,
Program Chairs:
Leman Akoglu
Carnegie Mellon University, USA
,
Dimitrios Gunopulos
University of Athens, Greece
,
Xifeng Yan
UC Santa Barbara, USA
,
Ravi Kumar
Google, USA
,
Fatma Ozcan
Google, USA
,
Jieping Ye
Alibaba DAMO Academy

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Published: 04 August 2023

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KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 6 - 10, 2023

CA, Long Beach, USA

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Cited By

Fang JChen LChai DHong YXie XChen LWang L(2024)UCTB: An Urban Computing Tool Box for Building Spatiotemporal Prediction Services2024 IEEE International Conference on Software Services Engineering (SSE)10.1109/SSE62657.2024.00020(54-65)Online publication date: 7-Jul-2024
https://doi.org/10.1109/SSE62657.2024.00020
Chen LFang JLiu TCao SWang L(2024)A Unified Model for Spatio-Temporal Prediction Queries with Arbitrary Modifiable Areal Units2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00111(1352-1365)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00111
Zhang DYan JPolat KAlhudhaif ALi J(2024)Multimodal joint prediction of traffic spatial-temporal data with graph sparse attention mechanism and bidirectional temporal convolutional networkAdvanced Engineering Informatics10.1016/j.aei.2024.10253362(102533)Online publication date: Oct-2024
https://doi.org/10.1016/j.aei.2024.102533

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