Deep reinforcement learning-based decision support system for transportation infrastructure management under hurricane events

The high wind, large surge/wave and heavy rainfall during a hurricane may significantly impact the performance of transportation infrastructure and associated traffic safety. Accordingly, stakeholders need to make a sequence of decisions to close (or restrict) the traffic of vulnerable components in the transportation network (e.g., aerodynamics-sensitive long-span bridges, hydrodynamics-sensitive coastal bridges and inundation-sensitive road segments) for the balance of safety and mobility. Due to large uncertainty involved in hurricane weather and traffic conditions, it is essentially a stochastic sequential decision problem and can be effectively formulated as a Markov decision process. This study proposes a deep reinforcement learning (RL)-based decision support system for stakeholders to optimally manage these critical components for the purpose of minimizing the network-level losses induced by hurricanes. Specifically, the RL policy (i.e., mapping from high-dimensional continuous traffic/weather information to management decisions) is represented by a deep neural network (DNN) and the optimal decision (corresponding to a set of DNN weights) is obtained using the deep Q learning algorithm. A case study based on a hypothetical traffic network is utilized to demonstrate the good performance of the developed deep RL-based decision support system in the application of transportation infrastructure management under hurricane conditions.

Automated summary

The uncertain weather and traffic conditions caused by hurricanes make managing transportation infrastructure a stochastic decision problem. This study proposes a deep reinforcement learning-based decision support system to minimize network-level losses caused by hurricanes.