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

Surge Routing: Event-informed Multiagent Reinforcement Learning for Autonomous Rideshare