发表机构
Marine Engineering Faculty, Chabahar Maritime University（恰巴哈尔海事大学海洋工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出泄漏感知评估协议，通过船舶、时间和区域不相交划分审计AIS轨迹预测模型，发现数据划分对误差影响远超架构改进，并公开代码与划分。
AI 中文摘要
从自动识别系统（AIS）数据中获得的船舶轨迹预测性能提升被归功于新架构，但评估协议很少被衡量为误差减少的来源。我们构建了一个泄漏感知协议，采用船舶、时间和区域不相交的数据划分，并将其应用于两个交通状况不同的语料库：31天的丹麦全国AIS交通数据和30天的美国墨西哥湾休斯顿及加尔维斯顿近海交通数据。在这两个语料库上，我们审计了TrAISformer、GATransformer以及受AISFormer启发的受控重建模型。两个语料库中均出现三种协议效应。第一，TrAISformer的最佳16选1预言机解码相对于贪心解码将误差降低了2.1-3.2倍。第二，共享船舶的划分使其贪心解码在1小时处的误差降低了23-25%，而一个紧凑的0.43百万参数编码器仅降低2%或更少。第三，区域不相交划分使TrAISformer在美国语料库上的1小时误差从2.2公里升至24.6公里，因为99.9%的测试上下文落在训练中从未见过的经度区间内；基于局部偏移的编码器不受此影响。架构机制的重要性较小：GATransformer的图注意力在两个语料库上均无显著收益，而其水道特征价值为12-22%。时间不相交划分的效果在语料库间不稳定（13%对比2%）。我们发布了数据划分和代码。
英文摘要
Reported gains in vessel-trajectory prediction from Automatic Identification System (AIS) data are credited to new architectures, but the evaluation protocol is rarely measured as a source of error reduction. We build a leakage-aware protocol with vessel-, time- and region-disjoint splits and apply it to two corpora with different traffic: 31 days of Danish national AIS traffic and 30 days of US Gulf coast traffic off Houston and Galveston. On both, we audit TrAISformer, GATransformer, and controlled AISFormer-inspired reconstructions. Three protocol effects appear in both corpora. First, TrAISformer's best-of-16 oracle decoder lowers error by a factor of 2.1-3.2 relative to greedy decoding. Second, a split that shares vessels lowers its greedy error by 23-25% at one hour, against 2% or less for a compact 0.43 M-parameter encoder. Third, a region-disjoint split raises TrAISformer's one-hour error from 2.2 to 24.6 km on the US corpus, because 99.9% of the test contexts fall in longitude bins never seen in training; the encoder built on local offsets is unaffected by this. Architectural mechanisms matter less: GATransformer's graph attention gives no measurable benefit on either corpus, while its waterway feature is worth 12-22%. The effect of a time-disjoint split is not stable across corpora (13% versus 2%). We release the splits and code.
