Performance of the location prediction models.

Research on maximum predictability for next location prediction aims to derive the theoretical maximum accuracy that an ideal prediction model could achieve, which is crucial for analyzing travel regularity and evaluating prediction models. However, three problems remain: 1) The spatiotemporal information used in existing predictability measures is incomplete; 2) quantifying predictability across diverse spatiotemporal information is challenging due to the limitations of entropic measures; and 3) applications of predictability lack further analysis of individual regularity. In this work, we first summarized spatiotemporal information and categorized it into four types of spatiotemporal knowledge. Next, to better quantify predictability, we proposed a refined maximum predictability based on fusion knowledge and Shannon entropy. Finally, we leveraged individual spatiotemporal knowledge preferences based on the refined maximum predictability to analyze travel regularity and evaluate prediction models. Our experimental results showed that the proposed predictability achieved the best results in both the simulation dataset and actual datasets, with a simulation dataset’s mean absolute error (MAE) of 0.06. Furthermore, the evaluation results of prediction models indicated that personalized selection and full utilization of spatiotemporal knowledge are crucial for effective location prediction. This work provides insights into the design and improvement of location prediction models. Codes are available at https://github.com/hlh7/A-refined-maximum-predictability.

面向下一位置预测（next location prediction）的最大可预测性研究，旨在推导理想预测模型所能达到的理论最高精度，这对于分析出行规律性以及评估预测模型而言至关重要。然而当前仍存在三类待解决问题：1）现有可预测性度量方法所采用的时空信息并不完整；2）受限于熵类度量手段，针对多样化时空信息的可预测性量化极具挑战；3）可预测性的应用场景尚未针对个体出行规律性展开深入分析。本研究首先对时空信息进行了系统性梳理，并将其划分为四类时空知识。随后，为实现更精准的可预测性量化，我们提出了一种基于融合知识与香农熵（Shannon entropy）的精细化最大可预测性方法。最后，我们基于该精细化最大可预测性，结合个体时空知识偏好特性，开展了出行规律性分析与预测模型评估工作。实验结果表明，所提出的可预测性方法在模拟数据集与真实数据集上均取得了最优表现，其中模拟数据集的平均绝对误差（mean absolute error，MAE）仅为0.06。进一步的预测模型评估结果显示，个性化选择并充分利用时空知识，对于实现高效的位置预测至关重要。本研究为下一位置预测模型的设计与优化提供了关键理论参考。相关代码已开源至：https://github.com/hlh7/A-refined-maximum-predictability。