Risk Control of Traffic Flow Through Chance Constraints and Large Deviation Approximation
This addresses safety-critical traffic management for autonomous systems, representing a domain-specific incremental improvement.
The paper tackles the problem of regulating rare safety-critical events in traffic flow under stochastic disturbances by developing a rare chance-constrained optimal control framework, which achieves precise near-target probability control and superior computational efficiency over baselines through extensive simulations.
Existing macroscopic traffic control methods often struggle to strictly regulate rare, safety-critical extreme events under stochastic disturbances. In this paper, we develop a rare chance-constrained optimal control framework for autonomous traffic management. To efficiently enforce these probabilistic safety specifications, we exploit a large deviation theory (LDT) based approximation method, which converts the original highly non-convex, sampling-heavy optimization problem into a tractable deterministic nonlinear programming problem. In addition, the proposed LDT-based reformulation exhibits superior computational scalability, as it maintains a constant computational burden regardless of the target violation probability level, effectively bypassing the extreme scaling bottlenecks of traditional sampling-based methods. The effectiveness of the proposed framework in achieving precise near-target probability control and superior computational efficiency over risk-averse baselines is illustrated through extensive numerical simulations across diverse traffic risk measures.