  Large events such as conferences, concerts and sports games, often cause
surges in demand for ride services that are not captured in average demand
patterns, posing unique challenges for routing algorithms. We propose a
learning framework for an autonomous fleet of taxis that leverages event data
from the internet to predict demand surges and generate cooperative routing
policies. We achieve this through a combination of two major components: (i) a
demand prediction framework that uses textual event information in the form of
events' descriptions and reviews to predict event-driven demand surges over
street intersections, and (ii) a scalable multiagent reinforcement learning
framework that leverages demand predictions and uses one-agent-at-a-time
rollout combined with limited sampling certainty equivalence to learn
intersection-level routing policies. For our experimental results we consider
real NYC ride share data for the year 2022 and information for more than 2000
events across 300 unique venues in Manhattan. We test our approach with a fleet
of 100 taxis on a map with 2235 street intersections. Our experimental results
demonstrate that our method learns routing policies that reduce wait time
overhead per serviced request by 25% to 75%, while picking up 1% to 4% more
requests than other model-based RL frameworks and classical methods in
operations research.
