arXiv cs.CY· Ruiya Zhou, Zhanzhan Zhao·· 2 天前AI 评分20
COMPASS：跨城市迁移学校学区划分知识
Learning to Transfer School Attendance Zoning across Cities
AI 导读
COMPASS 是一个参数到政策的框架，通过监督预测将既往学区划分搜索知识迁移到未见过的城市，用搜索得到的参数配置作为离线监督，学习从城市表征和划分难度信号预测可解释的学区控制参数，再由下游混合整数线性规划转化为明确划分方案。
正文 · 原文
Abstract:Designing multi-school attendance zones requires selecting constraint settings that balance educational equity, accessibility, and school capacity. Repeating this process for each new city can require substantial local calibration and computational search. We introduce COMPASS, a parameter-to-policy framework that transfers knowledge from previous zoning searches to unseen cities through supervised prediction. Using search-derived parameter configurations as offline supervision, COMPASS learns to predict interpretable zoning-control parameters from urban representations and zoning-difficulty signals. A downstream mixed-integer linear program converts these parameters into explicit zoning plans, with conditional parameter repair when the initial solve is unsuccessful. We evaluate parameter prediction, zoning-outcome recovery, and computational efficiency through five-fold cross-validation across 3,000 US cities, complemented by a case study of Atlanta. COMPASS achieves a 99.80% solver-feasible success rate and a 49.14-fold median deployment-time speedup relative to repeated city-specific parameter search. Further analysis shows that transfer performance varies across urban contexts and policy dimensions: structurally difficult cities receive the largest feasibility gains, while enrollment balance remains the hardest outcome to recover. These findings demonstrate how cross-city learning can reduce repeated search while retaining explicit zoning controls and spatial plans for local inspection and adaptation.
| Subjects: | Computers and Society (cs.CY) |
| Cite as: | arXiv:2610.21924 [cs.CY] |
| (or arXiv:2610.21924v1 [cs.CY] for this version) | |
| https://doi.org/10.48550/arXiv.2610.21924 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Zhanzhan Zhao [view email]
[v1]
Thu, 15 Oct 2026 14:06:37 UTC (17,867 KB)
来源：arXiv cs.CY · arxiv.org