Route Recommendations for Idle Taxi Drivers: Find Me the Shortest Route to a Customer!
Route Recommendations for Idle Taxi Drivers: Find Me the Shortest Route to a Customer!
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DOI：
10.1145/3219819.3220055
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发表时间：
2018-07
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通讯作者：
Nandani Garg;Sayan Ranu
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作者：
Nandani Garg;Sayan Ranu
我们研究向空闲出租车司机推荐路线的问题，以便最大限度地减少出租车与预期客户请求之间的距离。最小化到下一个预期客户的距离导致出租车司机更高的生产力和更少的等待时间为客户。为了预测未来的客户请求可能来自何时何地并相应地推荐路线，我们开发了一个名为MDM的路线推荐引擎：通过蒙特卡洛树搜索最小化距离。与现有技术相比，MDM采用了一个持续学习平台，在这个平台上，预测未来客户请求的基础模型会动态更新。对来自纽约和旧金山弗朗西斯科真实的出租车数据进行的广泛实验表明，MDM比最先进的方法高出70%，并且对音乐会、体育赛事等异常事件具有鲁棒性。
We study the problem of route recommendation to idle taxi drivers such that the distance between the taxi and an anticipated customer request is minimized. Minimizing the distance to the next anticipated customer leads to more productivity for the taxi driver and less waiting time for the customer. To anticipate when and where future customer requests are likely to come from and accordingly recom- mend routes, we develop a route recommendation engine called MDM: Minimizing Distance through Monte Carlo Tree Search. In contrast to existing techniques, MDM employs a continuous learning platform where the underlying model to predict future customer requests is dynamically updated. Extensive experiments on real taxi data from New York and San Francisco reveal that MDM is up to 70% better than the state of the art and robust to anomalous events such as concerts, sporting events, etc.