[
    {
        "id": "osp-22188",
        "type": "article-journal",
        "title": "Replication Failure and Trivial Baselines in Road-Level Crash Prediction",
        "author": [
            {
                "family": "Patel",
                "given": "Maurya"
            }
        ],
        "URL": "https://omanscience.com/en/articles/replication-failure-and-trivial-baselines-in-road-level-crash-prediction",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Graph neural networks are increasingly applied to road-level crash prediction, but the stability of their reported gains has received little scrutiny. We independently reconstruct the data pipeline of a recent uncertainty-aware model and evaluate eleven of its design decisions across three London boroughs under an expanding-window protocol. Four survive replication on a second borough; seven do not, and four of those reverse sign rather than attenuate. Multi-seed evaluation is decisive: one effect reverses sign between random seeds within a single borough, and the reference architecture exhibits per-borough seed spreads of up to 35.7 points against 4 points for ours. We further compare both networks against a parameter-free baseline that ranks segments by cumulative past crash count. At matched history depth our model is statistically indistinguishable from that baseline ($-0.90$ points, $p=0.61$), and the reference architecture loses to it on 18 of 18 held-out windows ($-17.37$, $p"
    }
]