CLIPPER Beyond Shortlisting: Auditable Decision Support for Changing Municipal Micromobility Policies

Abstract

In municipal planning workshops, planners and other stakeholders compare shared-micromobility parking policies by varying no-parking zones, locked sites, spacing, or area allocations. Each edit changes feasible candidates and how much demand they cover, so the alternative must be reoptimized on the same spatial data. Full-set greedy takes tens of seconds per alternative at city scale. We present Constraint-exact Low-latency Iterative Planning with Pooled Evaluation and Replay (CLIPPER), an optimizer with audit functions developed for requirements elicited with the City of Braunschweig. In each greedy round, it forms a deterministic candidate pool of bounded size, computes how much still-uncovered demand each candidate would add, and rejects candidates that violate an active constraint. An optional audit scans every remaining feasible candidate and records what the restricted pool omitted. We evaluate these functions on complete eleven-state edit chains (E0,…, E10) in Braunschweig, Munich, and Berlin. With K = 1024 candidates per group, the fixed-width mode CLIPPER-F stays on average within 0.245, 0.003, and 0.001 percentage points of full-set greedy coverage under the same policy in Braunschweig, Munich, and Berlin, respectively, with mean optimization speedups of 13.6-28.9×. At this pool size, no audited run is stopped by the pool limit. Plans computed from two checksummed dataset versions of Braunschweig's official no-parking-zone data differ in 30 of about 540 selected sites although coverage moves by only about 0.1 percentage points. The findings inform a proposed municipal process that versions policy inputs, reports site changes beside coverage, and audits the chosen plan before a final decision.