GridTuner：重新研究时空预测模型的网格大小选择[技术报告]
GridTuner: Reinvestigate Grid Size Selection for Spatiotemporal Prediction Models [Technical Report]
摘要：
随着交通预测技术的发展，时空预测模型越来越受到学术界和工业界的关注。然而，现有的研究大多集中在减少模型的预测误差，而忽略了空间事件在一个区域内的分布不均匀所造成的误差。在本文中，我们研究了一个区域划分问题，即最优网格大小选择问题（OGSS），其目的是通过选择最优的网格大小，以最小化时空预测模型的实际误差。为了求解OGSS，我们分析了时空预测模型的实际误差的上界，并通过最小化实际误差的上界来最小化实际误差。通过深入分析发现，当模型网格数从1增加到最大允许值时，实际误差的上界先减小后增大。然后，我们提出了两种算法，即三元搜索和迭代法，自动找到最佳的网格大小。最后，通过实验验证了预测误差与其上界的变化趋势一致，而实际误差上界随模型网格数的增加先减小后增大。同时，在一个案例研究中，通过选择最佳的网格大小，一个国家的最先进的基于预测的算法的订单调度结果可以提高到13.6%，这表明我们的方法调整区域划分的时空预测模型的有效性。
英文摘要：
With the development of traffic prediction technology, spatiotemporal prediction models have attracted more and more attention from academia communities and industry. However, most existing researches focus on reducing model's prediction error but ignore the error caused by the uneven distribution of spatial events within a region. In this paper, we study a region partitioning problem, namely optimal grid size selection problem (OGSS), which aims to minimize the real error of spatiotemporal prediction models by selecting the optimal grid size. In order to solve OGSS, we analyze the upper bound of real error of spatiotemporal prediction models and minimize the real error by minimizing its upper bound. Through in-depth analysis, we find that the upper bound of real error will decrease then increase when the number of model grids increase from 1 to the maximum allowed value. Then, we propose two algorithms, namely Ternary Search and Iterative Method, to automatically find the optimal grid size. Finally, the experiments verify that the error of prediction has the same trend as its upper bound, and the change trend of the upper bound of real error with respect to the increase of the number of model grids will decrease then increase. Meanwhile, in a case study, by selecting the optimal grid size, the order dispatching results of a state-of-the-art prediction-based algorithm can be improved up to 13.6%, which shows the effectiveness of our methods on tuning the region partition for spatiotemporal prediction models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
