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.

中文翻译：

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预测大规模在线 P2P 拼车的请求

点对点拼车 (P2P-RS) 使人们可以安排自己的私家车单次乘车，而无需专业司机的参与。这是一个突出的集体智能应用程序，为个人（降低成本）和整个社区（减少污染和交通）都带来了显着的好处，正如我们在最近的一篇出版物中所展示的那样，我们提出了一种用于大规模 P2P 的在线近似求解算法—— RS。在本文中，我们解决了评估在 P2P-RS 优化背景下预测拼车请求的好处的基本问题。公共现实世界的结果表明，通过采用完美的预测器，在 1 分钟的预测范围内，总奖励可以提高 5.27%。另一方面，