MambaLSTM, a Mamba-plus-LSTM spatiotemporal network, reports F1-score gains of roughly 2–5 points over baselines for next-step traffic accident risk prediction on NYC, Chicago, and US-Accidents data.
Neural Networks 25, 70 --83 (2012)
1 Pith paper cite this work, alongside 90 external citations. Polarity classification is still indexing.
1
Pith paper citing it
90
external citations · OpenAlex
fields
cs.LG 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
MambaLSTM: A Spatio-Temporal Framework for Enhanced Traffic Accident Risk Prediction
MambaLSTM, a Mamba-plus-LSTM spatiotemporal network, reports F1-score gains of roughly 2–5 points over baselines for next-step traffic accident risk prediction on NYC, Chicago, and US-Accidents data.