Finding Risk-Averse Shortest Path with Time-dependent Stochastic Costs
This addresses route planning for transportation users needing risk-aware decisions, but it is incremental as it adapts existing methods.
The paper tackled risk-averse route planning with time-dependent stochastic costs by proposing an A* algorithm adaptation for monotonic risk measures, demonstrating it in a case study on Manhattan's transportation network.
In this paper, we tackle the problem of risk-averse route planning in a transportation network with time-dependent and stochastic costs. To solve this problem, we propose an adaptation of the A* algorithm that accommodates any risk measure or decision criterion that is monotonic with first-order stochastic dominance. We also present a case study of our algorithm on the Manhattan, NYC, transportation network.