Finding Risk-Averse Shortest Path with Time-dependent Stochastic Costs

TL;DR

This paper introduces a risk-averse shortest path algorithm for time-dependent stochastic costs, adaptable to various risk measures, demonstrated through a case study on Manhattan's transportation network.

Contribution

It proposes an A* algorithm extension for risk-averse route planning with stochastic, time-dependent costs, applicable to any monotonic risk measure.

Findings

Effective in modeling risk-averse routing with stochastic costs

Demonstrated on Manhattan transportation network

Flexible for different risk measures

Abstract

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.