发表机构
University of Wisconsin–Madison; Columbia University; Tsinghua University（威斯康星大学麦迪逊分校; 哥伦比亚大学; 清华大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对仅刷卡进站公交智能卡数据无法构建OD矩阵的问题，本研究提出分层贝叶斯潜在目的地（HBLD）模型，结合多类数据推断出行目的地，经评估其表现优于基线，可生成不确定性感知的OD矩阵用于公交相关管理工作。
AI 中文摘要
仅进站自动收费系统仅记录上车记录，不记录下车记录，无法直接构建起讫点（OD）矩阵。本研究开发了分层贝叶斯潜在目的地（HBLD）模型，该模型将站点-小时上车量、推断的下车需求与乘客卡历史相结合，将出行链目的地视为带有可靠性参数的噪声证据，使目的地不确定性能传播到OD流中。该模型应用于2025年5月在常州收集的838305条公交刷卡进站数据，并与站点网络和小时天气数据关联，利用网络、时段、天气及平滑后的历史需求效应，估计可行的下游及反向方向经停站点的目的地分布。贝叶斯个性化层使用先验卡出行数据，当无历史数据时则回归到共享出行层面分布。通过随机变分推断拟合模型，并在最后一周进行评估，结果显示HBLD模型的表现优于最强基线。观测到的上车模式始终能提升预测效果，尤其在无卡历史时；而推断的下车模式仅在出行链证据被高度信任时才有帮助。该模型捕捉到与经站点骑行、公交辅助道路过街一致的出行，并为确定性链无法解析的出行估计目的地。由于无真实下车数据，得分衡量的是与出行链输出的一致性，而非实际目的地准确性。HBLD为服务管理、规划、调度及资源分配提供了感知不确定性的目的地预测与OD矩阵。
英文摘要
Entry-only automatic fare collection systems record boardings but not alightings, preventing direct construction of origin-destination (OD) matrices. This study develops a Hierarchical Bayesian Latent-Destination (HBLD) model that combines station-hour boarding and inferred alighting demand with passenger card histories. Trip-chain destinations are treated as noisy evidence with a reliability parameter, allowing destination uncertainty to propagate into OD flows. The model was applied to 838,305 bus tap-ins collected in Changzhou in May 2025 and linked to stop-network and hourly weather data. It estimates destination distributions over feasible downstream and reverse-direction through-terminal stops using network, time-of-day, weather, and smoothed historical demand effects. A Bayesian personalization layer uses prior card trips and reverts to the shared trip-level distribution when history is unavailable. Fitted by stochastic variational inference and evaluated on the final week, HBLD outperformed the strongest baseline. Observed boarding patterns consistently improved prediction, especially without card history, while inferred alighting patterns helped only when trip-chain evidence was strongly trusted. The model captured travel consistent with through-terminal riding and bus-assisted road crossing and estimated destinations for trips unresolved by deterministic chaining. Because true alightings were unavailable, scores measure agreement with trip-chain outputs rather than actual destination accuracy. HBLD provides uncertainty-aware destination predictions and OD matrices for service management, planning, scheduling, and resource allocation.
