发表机构
Tsinghua University; Northwestern Polytechnical University（清华大学; 西北工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
BER-PEF是基于贝叶斯错误率估计的框架，可在无真实值时统一评估异构移动数据的可预测性估计器，实验显示其性能优于现有方法。
AI 中文摘要
人类移动可预测性关乎从给定目标和输入信息中可获得的最佳预测性能，但其真实值无法在真实移动数据上直接观测。本文提出BER-PEF，一种基于贝叶斯错误率（Bayes Error Rate, BER）的框架，该框架将BER估计转化为移动可预测性估计，并提供了一种无需可观测真实值即可比较估计器的统一协议。该框架将符号序列、数值轨迹、上下文特征及学习到的表示映射到共同的特征-标签空间，随后沿受控扰动曲线，通过测量共享可预测性参考区间下方、上方及跨区间的偏差，评估估计器的输出。在Foursquare NYC、TKY、GeoLife和T-Drive数据集上的实验表明，若干基于BER的估计器在符号序列和数值轨迹上的参考偏差低于现有可预测性方法，且其估计值能跟踪扰动下经验预测性能的变化。进一步分析显示，上下文输入和多种结构化表示可在同一协议下进行评估，且聚合多个扰动水平的证据相比仅依赖单一未扰动观测，能为估计器选择提供更可靠的依据。因此，当真实可预测性不可用时，BER-PEF为评估异构移动数据上的可预测性估计器提供了统一且可验证的路径。
英文摘要
Human mobility predictability concerns the best prediction performance attainable from a given target and input information, but its ground truth is not directly observable on real mobility data. We present BER-PEF, a Bayes-error-rate-based framework that converts BER estimation into mobility predictability estimation and provides a unified protocol for comparing estimators without observable ground truth. The framework maps symbolic sequences, numeric trajectories, contextual features, and learned representations into a common feature--label space, then evaluates estimator outputs along controlled perturbation curves against a shared predictability reference interval by measuring deviations below the interval, above the interval, and across the full interval. Experiments on Foursquare NYC and TKY, GeoLife, and T-Drive show that several BER-based estimators achieve lower reference discrepancy than existing predictability methods on symbolic sequences and numeric trajectories, while their estimates track changes in empirical prediction performance under perturbation. Additional analyses show that contextual inputs and multiple structured representations can be evaluated under the same protocol, and that aggregating evidence across multiple perturbation levels provides a more reliable basis for estimator selection than relying on a single unperturbed observation. BER-PEF therefore offers a unified and verifiable path for evaluating predictability estimators on heterogeneous mobility data when ground-truth predictability is unavailable.
