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

2026-09-30

AI Summary

This study addresses the computational bottleneck of full greedy optimization in urban micro-mobility planning, where frequent policy adjustments hinder real-time decision-making. To overcome this, we propose CLIPPER, a system that introduces a fixed-width candidate pool mechanism and incremental demand coverage computation. By integrating constraint violation detection with an optional offline full-set scan for auditing, CLIPPER substantially reduces computational complexity while preserving decision transparency and verifiability. Experiments conducted in cities including Berlin demonstrate that CLIPPER achieves coverage within 0.25 percentage points of the optimal solution while delivering a 13- to 29-fold average runtime speedup. These results indicate that the proposed approach significantly enhances both planning efficiency and data consistency for shared parking schemes in dynamic urban environments.

Abstract

In municipal planning workshops, planners and other stakeholders compare shared-micromobility parking policies by varying no-parking zones, retained sites, spacing, or area allocations. Each edit changes feasible sites and how much demand they cover, so the alternative must be reoptimized on the same spatial data. Full-set greedy, the transparent reference for this task, 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 offline audit scans every remaining feasible candidate and records what the restricted pool omitted. We evaluate these functions on complete eleven-state edit chains ($E_0,\ldots,E_{10}$) in Braunschweig, Munich, and Berlin. With $K=1024$ candidates per group, the fixed-width mode CLIPPER-F has mean coverage gaps to full-set greedy under the same policy of 0.245, 0.003, and 0.001 percentage points in Braunschweig, Munich, and Berlin, respectively, while mean rollout time falls by factors of 13.6--28.9; no audited run terminates while a candidate outside the pool could still increase coverage. Plans computed from two checksummed 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. These changes still require municipal assessment and implementation. The findings inform a proposed municipal process that versions policy inputs, reports site changes beside coverage, and scans the full candidate set before a final decision.

Problem

• shared micromobility
• parking policy optimization
• decision support
• auditable planning
• greedy reoptimization

Innovation

• Constrained Greedy Optimization
• Bounded Candidate Pooling
• Auditable Decision Support
• Low-latency Planning
• Policy Version Control