提出新模型精准预测拼车平台细粒度出行需求，提升供需匹配效率。
Spatio-temporal Prediction of Fine-Grained Origin-Destination Matrices with Applications in Ridesharing
- 通过无监督空间粗化缓解高维出行矩阵的数据稀疏问题。
- 在90%以上稀疏度下，误差降低45%（RMSE）和60%（WMAPE）。
- 适合拼车平台调度优化与城市交通需求预测场景使用。
准确预测网络化出行需求的时空分布对拼车平台的有效政策设计至关重要。掌握未来时段内各区域间的总需求量，有助于平台提前调配运力，提高乘客请求满足率，并将闲置司机重新分配至高需求区域，从而优化全局供需平衡。本文聚焦于细粒度出发地-目的地（OD）需求的时空预测，尤其针对大量局部区域的情形。尽管该任务具有重要应用价值，但在学术界仍鲜有研究。为此，本文提出一种新型预测模型OD-CED，包含无监督空间粗化技术以缓解数据稀疏性，以及编码器-解码器架构以捕捉语义与地理依赖关系。实验证明，当处理稀疏度超过90%的OD矩阵时，相较于传统统计方法，该模型在根均方误差（RMSE）上最高降低45%，加权平均绝对百分比误差（WMAPE）降低60%。
原文摘要 · Abstract (English)
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%.
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