CLIPPER 超越候选列表缩减：面向市政微出行政策变更的可审计决策支持
CLIPPER Beyond Shortlisting: Auditable Decision Support for Changing Municipal Micromobility Policies
- Clausthal University of Technology（克劳斯塔尔工业大学）
- Leibniz University Hannover（莱布尼茨汉诺威大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对市政微出行停车政策调整需反复重优化的问题，提出带审计功能的CLIPPER优化器，通过有界候选池和离线审计实现低延迟决策支持，在三个城市中覆盖率差距极小且速度大幅提升。
AI中文摘要：
在市级的规划工作坊中，规划人员及其他利益相关者通过调整禁停区、保留站点、间距或区域分配来比较共享微出行停车政策。每一次编辑都会改变可行站点及其覆盖的需求量，因此必须在相同的空间数据上对备选方案重新优化。全集合贪心算法作为该任务的透明参考方法，在城市规模下每个备选方案需要数十秒。我们提出了约束精确的低延迟迭代规划与池化评估及重放（CLIPPER），这是一种带有审计功能的优化器，其需求是与布伦瑞克市共同提炼的。在每一轮贪心迭代中，它形成一个有界大小的确定性候选池，计算每个候选者将增加的未覆盖需求量，并拒绝违反活动约束的候选者。可选的离线审计会扫描所有剩余的可行候选者，并记录受限池遗漏的内容。我们在布伦瑞克、慕尼黑和柏林的完整十一状态编辑链（$E_0,\ldots,E_{10}$）上评估这些功能。在每组$K=1024$个候选者的情况下，固定宽度模式CLIPPER-F在相同政策下与全集合贪心算法的平均覆盖率差距分别为0.245、0.003和0.001个百分点（布伦瑞克、慕尼黑、柏林），而平均展开时间减少了13.6至28.9倍；没有任何一次审计运行在池外候选者仍可能增加覆盖率时终止。基于布伦瑞克官方禁停区数据的两个校验和版本计算出的计划在约540个选定站点中有30个不同，尽管覆盖率仅变动约0.1个百分点。这些变化仍需市政评估和实施。研究结果为拟议的市政流程提供了信息，该流程对政策输入进行版本管理，在覆盖率旁边报告站点变化，并在最终决策前扫描完整候选集。
英文摘要：
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.
