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CLIPPER introduces a novel approach to municipal micromobility planning that efficiently handles complex constraints and policy edits by utilizing a pooled evaluation and replay mechanism. This method significantly reduces rollout times鈥攂y up to 28.9 times鈥攚hile maintaining coverage accuracy within 0.245 percentage points of traditional greedy methods across multiple cities. The framework allows for rapid and repeatable assessments of planning states, ensuring compliance with operational requirements in urban environments.
CLIPPER slashes planning rollout times by up to 28.9 times while keeping coverage nearly identical to traditional methods, revolutionizing urban micromobility strategies.
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.
