Replication Failure and Trivial Baselines in Road-Level Crash Prediction

תקציר מקורי באנגליתarXiv:2609.35917v1 Announce Type: new 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 rank