English(EN) SeoulMMOD: A Large-Scale Multimodal Origin-Destination Flow Benchmark

新的SeoulMMOD数据集为多模态城市出行预测设立基准

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-12 04:00

研究人员推出了SeoulMMOD，一个用于预测首尔城市出行的综合基准数据集。该数据集包含三年内跨六种交通方式的小时起讫流量数据，覆盖25个区和426个子区。它还纳入了旅行时间、距离、天气和兴趣点等补充数据。对14个预测模型的初步基准测试显示，虽然一些时空模型在子区级别预测方面遇到困难，但朴素的多模式联合训练并不总能带来更高的准确性。 AI

影响为城市规模的多模态、多年期OD预测建立了一个可复现的测试平台，可能推动人工智能在城市规划中的应用。

排序理由该条目描述了一个新的学术数据集和城市出行预测基准。[lever_c_demoted from research: ic=1 ai=0.4]

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arXiv cs.LG TIER_1 English(EN) · Taeyoung Yu, Seonbin Jo, Jiwon Kim, Junyoung Byun · 2026-10-12 04:00

SeoulMMOD：一个大规模多模态出行需求基准

arXiv:2610.13730v1 Announce Type: new Abstract: Origin-destination (OD) flow forecasting supports urban mobility decisions by predicting the volume and patterns of human movement across a city. Most existing public OD benchmarks provide only a partial view of citywide mobility, c…

本文如何被排名

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SeoulMMOD：一个大规模多模态出行需求基准

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