A Predict–Optimize–Evaluate Framework for Sustainable Traffic Safety Resource Allocation: LSTM Forecasting with Triangulated Enforcement Elasticity in Saudi Arabia

Road traffic crashes remain a global public health burden and a persistent resource allocation problem that undermines progress toward the sustainable development of safe, equitable mobility systems. Saudi Arabia’s Vision 2030 targets fewer than 10 fatalities per 100,000 population, a goal aligned with United Nations Sustainable Development Goal 3.6 (halving road traffic deaths) and SDG 11.2 (safe and sustainable transport), yet a gap persists between crash prediction research and how agencies deploy enforcement resources. This paper builds a closed-loop predict–optimize–evaluate framework connecting Long Short-Term Memory (LSTM) neural networks to a goal-distance gap metric and constrained optimization, feeding forecast outputs directly into enforcement scheduling decisions. Using monthly casualty data from official Saudi sources covering the entire kingdom (all 13 administrative regions) from 2010 through 2024 (N = 42,856 fatal and serious injuries across 180 monthly observations), we validate LSTM forecasting against five benchmarks plus a GRU and a Transformer baseline, apply gap analysis as a standardized goal-distance metric, optimize enforcement allocation with triangulated elasticity estimates, and evaluate past policy reforms through multi-method counterfactual analysis. A headline finding is that roughly 28% of fatal and serious injuries cluster within only about 6% of weekly hours, creating an unusually concentrated target for enforcement reallocation. The LSTM achieves RMSE = 2.47 with MASE = 0.83, beating ARIMA by 35% while maintaining robustness during COVID disruptions (RMSE = 2.38 in the post-acute period 2022–2024 versus 2.61 in the acute period 2020–2021). Temporal analysis confirms 28% of fatalities (95% CI: 26.0–30.0%) cluster within 6% of weekly hours. Enforcement elasticity triangulated from three independent sources converges at α ≈ 0.31 (90% CI: 0.25–0.40). The optimization model allocates 56% of enforcement resources to Thursday–Friday midnight-to-4 AM windows, projecting a 17.1% casualty reduction (90% CI: 13.5–20.6% under Monte Carlo uncertainty in α). Monte Carlo sensitivity analysis with 10,000 iterations confirms a median benefit-cost ratio of 1.88 (90% CI: 1.18–2.97), with P (BCR > 1.0) = 98.9%, using locally calibrated VSL = SAR 4.2 million (equivalent to approximately USD 1.12 million at the SAMA-pegged rate of 3.75 SAR/USD, in constant 2024 prices). Counterfactual evaluation finds that the post-2018-reform period was associated with a 22.1% casualty reduction (95% CI: 16.4–27.8%), with magnitude robust across four methods (LSTM counterfactual, Bayesian Structural Time-Series, Synthetic Control, and an inverse-variance-weighted synthesis of the three); we stress, however, that attribution to the driving reform itself cannot be cleanly separated from concurrent Saher camera expansion, public awareness campaigns, and trauma-care improvements. By translating prediction into evidence-based, resource-efficient enforcement, the framework supports sustainable road safety policy in middle-income and rapidly motorizing settings.