发表机构
Independent Researcher（独立研究者）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
CALM提出一种可复现的混合框架，集成LLM活动规划器与校准随机选择，在纽约市调查数据上显著降低模式散度，并通过消融和压力测试实现可解释的出行者仿真。
AI 中文摘要
我们提出了CALM，一个可复现的混合框架，它集成了一个可选的大语言模型（LLM）活动规划器，并包含校准的随机选择、共享网络反馈、记忆与习惯、类型化可行性检查以及确定性离线回放。与出行方式分类器或仅生成日记的生成器不同，CALM执行一个封闭的出行者日循环，并针对经验性的、可复现的基线评估每个生成模块。在2024年纽约市全市出行调查（CMS）中，110,691次七模式出行按受访者被分为78,487次训练出行和32,204次保留出行。仅训练用的备选方案特定常数校准将十次随机种子下的保留模式詹森-香农散度从0.15599降至0.00394。随后，一项匹配的实时LLM消融研究量化了总体拟合、时间拟合、行为持久性和可行性之间的权衡，而冻结的提示-响应对支持下游模拟的确定性回放。受控的天气、延误、票价和停车阶梯进一步展示了干预下一致且可解释的响应。CALM通过人员不相交校准、匹配模块消融、受控压力测试和端到端可追溯性，为在出行者仿真中集成和评估生成式规划器贡献了一个可复现的协议。
英文摘要
We present CALM, a reproducible hybrid framework that integrates an optional large language model (LLM) activity planner with calibrated stochastic choice, shared network feedback, memory and habit, typed feasibility checks, and deterministic offline replay. Unlike trip-mode classifiers or diary-only generators, CALM executes a closed traveler-day loop and evaluates each generative module against an empirical, reproducible baseline. On the 2024 New York City Citywide Mobility Survey (CMS), 110,691 seven-mode trips are split by respondent into 78,487 training and 32,204 holdout trips. Training-only alternative-specific constant calibration reduces mean holdout mode Jensen-Shannon divergence from 0.15599 to 0.00394 across ten seeds. A matched live-LLM ablation then quantifies trade-offs among aggregate fit, temporal fit, behavioral persistence, and feasibility, while frozen prompt-response pairs support deterministic replay of downstream simulation. Controlled weather, delay, fare, and parking ladders further demonstrate consistent and interpretable responses under intervention. CALM contributes a reproducible protocol for integrating and evaluating generative planners in traveler simulation through person-disjoint calibration, matched module ablation, controlled stress testing, and end-to-end traceability.
