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.

翻译：大型活动（如会议、音乐会、体育赛事）常引发交通服务需求的激增，这类需求无法通过平均需求模式捕捉，给路径规划算法带来独特挑战。我们提出一种面向自动驾驶出租车队的学习框架，利用互联网中的事件数据预测需求激增并生成协同路径规划策略。该框架通过两大核心组件实现：（i）基于事件文本信息（包括事件描述与评论）的需求预测框架，用于预测街道交叉口的事件驱动型需求激增；（ii）可扩展的多智能体强化学习框架，该框架利用需求预测，通过单智能体逐次滚动优化结合有限采样确定性等价方法，学习交叉口级别的路径规划策略。实验采用2022年纽约市真实网约车数据及曼哈顿300个独特场馆的2000余个事件信息，在包含2235个街道交叉口的地图上部署100辆出租车进行测试。结果表明，我们的方法学习到的路径规划策略可将每单服务请求的等待时间成本降低25%至75%，同时比基于模型的其他强化学习框架及运筹学经典方法多完成1%至4%的订单接载。