CLIPPER: Replayable Shortlisted Optimization for Repeated Spatial Coverage Planning

2026-08-27

AI Summary

为解决城市微移动规划中因政策变化需频繁重新规划的问题，提出CLIPPER算法，通过构建候选池并实时检查约束条件，显著减少规划时间同时保证覆盖效果。

Abstract

Operational requirements developed with the City of Braunschweig frame municipal micromobility planning under geofenced exclusions, mandatory retained sites, spacing rules, and area-level caps. Each policy edit requires a new feasible plan; full-set greedy takes tens of seconds per alternative at city scale. We present CLIPPER (Constraint-exact Low-latency Iterative Planning with Pooled Evaluation and Replay). It forms bounded candidate pools but recomputes exact current gains and checks every active constraint before selection. Coverage from each candidate alone sets the initial order. Offline full-set scans measure gains omitted by the pool; online, a conservative bound triggers expansion or audit. CLIPPER-F gives each proposal group the same number of candidate slots. Across Braunschweig, Munich, and Berlin, its mean coverage over complete chains stays within 0.245 percentage points of full-set greedy under the same policy, with 13.6--28.9 times lower mean rollout time. CLIPPER-A instead distributes one shared candidate budget across the groups. Under its coverage-prioritized policy, it uses 9--15% of full-set greedy's rollout time under the same policy, with mean gaps of 1.82 percentage points in Braunschweig, 0.12 in Munich, and 0.27 in Berlin. Together, CLIPPER enables rapid, replayable comparison of recorded city-scale planning states while enforcing every encoded model constraint.

Problem

• repeated spatial coverage planning
• geofenced exclusions
• mandatory retained sites
• spacing rules
• area-level caps

Innovation

• Constraint-exact
• Low-latency Planning
• Pooled Evaluation and Replay
• Coverage Optimization
• Spatial Coverage Planning