We present a semantic-structural atlas of transportation research built from 120{,}323 papers across 34 peer-reviewed journals published between 1967 and 2025, roughly an order of magnitude larger than and a decade beyond Sun and Rahwan's~(2017) coauthorship study. We use OpenAlex and Crossref as open, CC0-licensed data sources, resolve author identity through OpenAlex author IDs, ORCID records, and manual alias resolution, and embed every paper with SPECTER2 with Arora-style whitening concatenated with concept TF--IDF and venue linear-discriminant projections. On this substrate we report three findings. First, Leiden on the author-level semantic k-nearest-neighbor graph yields 23 topic communities that agree only weakly with the 172 coauthor communities (normalized mutual information $0.23$), opening room for a predictive layer that neither source encodes alone. Second, a multiplex Leiden partition combining both edge types recovers 181 communities and localizes where collaboration and topic structure decouple. Third -- the paper's core methodological contribution -- we define \emph{phantom collaborators}, pairs of authors who are top-$K$ semantic neighbors yet $\geq 3$ hops apart in the coauthor graph, and show via a temporal hold-out (training cutoff 2019) that phantom pairs become real coauthors in 2020--2025 at a rate $16$ to $33$ times above random, popularity-weighted, and same-venue baselines, with a $68$-fold monotone gradient between the highest- and lowest-similarity buckets. All artifacts are released as a live, reproducible web atlas at https://choi-seongjin.github.io/transport-atlas/.

翻译：我们构建了一幅交通研究的语义结构图谱，其基础是1967年至2025年间发表在34本同行评审期刊上的120，323篇论文，规模比Sun和Rahwan（2017）的合著研究大约一个数量级，时间跨度也超出十年。我们使用OpenAlex和Crossref作为开放的CC0许可数据源，通过OpenAlex作者ID、ORCID记录和手动别名消解来识别作者身份，并利用SPECTER2结合Arora风格白化、概念TF–IDF以及期刊线性判别投影对每篇论文进行嵌入。在此框架上，我们报告三项发现：首先，对作者级别的语义k近邻图进行Leiden聚类，得到23个主题社群，这些社群与172个合著社群仅有弱一致性（归一化互信息为0.23），这为单独任一来源无法编码的预测层留下了空间。其次，结合两种边类型的多重Leiden划分恢复出181个社群，并定位了合作与主题结构分离的位置。第三——本文的核心方法论贡献——我们定义了“幽灵合作者”，即一对在语义上互为top-K近邻、但在合著图中相距至少3跳的作者，并通过时间预留验证（训练截止于2019年）表明，幽灵对在2020–2025年间成为真实合著者的比率比随机、流行度加权和同期刊基线高出16至33倍，且在最高与最低相似度区间之间存在68倍的单调梯度。所有成果均以可复现的实时网络图谱形式发布，网址为https://choi-seongjin.github.io/transport-atlas/。