Proactive Traffic Operational Risk Assessment Using Variational Autoencoders

Traditional traffic safety analysis has long suffered from a reliance on retroactive, sparse crash logs, which mathematically struggle to capture the highly stochastic and non-linear dynamics of real-time traffic streams, rendering proactive safety prevention difficult. To bridge this gap, this study introduces an innovative, data-driven Traffic Operational Risk (TOR) assessment framework that integrates unsupervised deep learning with extreme-value statistics to achieve continuous, proactive risk monitoring. By mapping macroscopic traffic flow parameters and microscopic aggressive driving behaviors (ADBs) onto a unified spatiotemporal grid, we deploy a Variational Autoencoder (VAE) to learn the continuous latent “safe traffic manifold”. On this basis, Extreme Value Theory (EVT) is introduced to mathematically calibrate a dynamic, robust safety frontier on a unified scale (0–100), effectively suppressing sensor noise. The evaluation results demonstrate that the VAE-EVT framework robustly quantifies dynamic operational risks, effectively overcoming the linear limitations of traditional surrogate models. Furthermore, spatial frequency mapping reveals that elevated operational risks inherently cluster at geometric bottlenecks, such as merge/diverge zones and sharp curves. This spatial aggregation elucidates a typical “High Risk, Low Crash” phenomenon primarily driven by driver compensatory behaviors. Crucially, the integration of a novel multidimensional risk decoupling mechanism successfully isolates micro-behavioral volatility from macro-flow degradation. By tracing this causal progression, the framework captures the mechanistic evolution of traffic breakdowns, securing a critical 10 to 15 min proactive pre-warning window before systemic crashes or congestion materialize. Ultimately, this methodology liberates risk assessment from retroactive crash logs, providing a mathematically rigorous paradigm for precision-guided highway safety management.