论文标题
在大规模在线P2P乘车共享中预测请求
Predicting Requests in Large-Scale Online P2P Ridesharing
论文作者
论文摘要
点对点乘车共享（P2P-RS）使人们能够在不参与专业驾驶员的情况下与自己的私家车安排一次骑行。正如我们在最近的一份出版物中所示，这是一个著名的集体情报申请（降低成本）和整个社区（减少污染和交通），为个人带来了重大收益（减少污染和交通）。在本文中，我们解决了在P2P-RS优化的背景下评估预测乘车共享请求的好处的基本问题。公共现实世界中的结果表明，通过采用完美的预测指标，总奖励可以通过1分钟的预测范围提高5.27％。另一方面，香草长的短期记忆神经网络无法改善基线预测因子，该预测只是简单地复制了前一天的请求，同时达到了几乎双重的准确性。
Peer-to-peer ridesharing (P2P-RS) enables people to arrange one-time rides with their own private cars, without the involvement of professional drivers. It is a prominent collective intelligence application producing significant benefits both for individuals (reduced costs) and for the entire community (reduced pollution and traffic), as we showed in a recent publication where we proposed an online approximate solution algorithm for large-scale P2P-RS. In this paper we tackle the fundamental question of assessing the benefit of predicting ridesharing requests in the context of P2P-RS optimisation. Results on a public real-world show that, by employing a perfect predictor, the total reward can be improved by 5.27% with a forecast horizon of 1 minute. On the other hand, a vanilla long short-term memory neural network cannot improve upon a baseline predictor that simply replicates the previous day's requests, whilst achieving an almost-double accuracy.
