Spatio-temporal Prediction of Fine-Grained Origin-Destination Matrices with Applications in Ridesharing

Accurate spatial-temporal prediction of network-based travelers’ requests is crucial for the effective policy design of ridesharing platforms. Having knowledge of the total demand between various locations in the upcoming time slots enables platforms to proactively prepare adequate supplies, thereby increasing the likelihood of fulfilling travelers’ requests and redistributing idle drivers to areas with high potential demand to optimize the global supply-demand equilibrium. This paper delves into the prediction of Origin-Destination (OD) demands at a fine-grained spatial level, especially when confronted with an expansive set of local regions. While this task holds immense practical value, it remains relatively unexplored within the research community. To fill this gap, we introduce a novel prediction model called OD-CED, which comprises an unsupervised space coarsening technique to alleviate data sparsity and an encoder-decoder architecture to capture both semantic and geographic dependencies. Through practical experimentation, OD-CED has demonstrated remarkable results. It achieved an impressive reduction of up to 45% reduction in root-mean-square error and 60% in weighted mean absolute percentage error over traditional statistical methods when dealing with OD matrices exhibiting a sparsity exceeding 90%.基于交通网络的出行者需求的精准时空预测，对于拼车平台的有效政策制定至关重要。掌握未来时段内各点位间的总需求情况，可帮助平台主动筹备充足运力，进而提升出行者需求的满足率，并将闲置司机调度至潜在需求旺盛的区域，以优化全局供需均衡。 本文聚焦于细粒度空间尺度下的起讫点（Origin-Destination, OD）需求预测任务，尤其是在本地区域覆盖范围广泛的场景中。尽管该任务具备极高的实用价值，但目前学术界对此的研究仍相对匮乏。 为填补这一研究空白，本文提出一种名为OD-CED的新型预测模型，该模型包含用于缓解数据稀疏问题的无监督空间粗化技术，以及用于捕捉语义与地理依赖关系的编码器-解码器架构。 通过实际实验验证，OD-CED模型展现出了优异的性能。在处理稀疏度超过90%的OD矩阵时，相较于传统统计方法，该模型的均方根误差（root-mean-square error）最高可降低45%，加权平均绝对百分比误差（weighted mean absolute percentage error）最高可降低60%。