RLIFE: Remaining Lifespan Prediction for E-scooters
RLIFE: Remaining Lifespan Prediction for E-scooters
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DOI：
10.1145/3583780.3615037
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发表时间：
2023-10
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影响因子：
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通讯作者：
Shuxin Zhong;William Yubeaton;Wenjun Lyu;Guang Wang;Desheng Zhang;Yu Yang
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作者：
Shuxin Zhong;William Yubeaton;Wenjun Lyu;Guang Wang;Desheng Zhang;Yu Yang
共享电动滑板车（e-scooter）因其方便和环保的特点而越来越受欢迎。由于共享性质和广泛使用，电动滑板车的使用寿命通常很短（例如，2到5个月），这使得准确预测剩余寿命，确保及时更换变得非常重要。虽然有几项研究集中在各种系统的寿命预测上，比如电池和桥，但它们存在两个缺点。首先，它们需要大量的人工劳动或额外的传感器资源来确定物体的明确状态，使它们成本低。其次，这些研究假设未来的用法与历史的用法相似。为了解决这些限制，我们的目标是在不增加额外成本的情况下，准确地预测电动滑板车的剩余寿命，其本质是准确地代表其现状，并预测其未来的使用情况。然而，这是具有挑战性的，因为：i)缺乏明确的规则对电动滑板车的状态表示；ii)电动滑板车的未来使用可能与历史使用有很大不同。在本文中，我们设计了一个名为RLIFE的框架，其关键观点是通过出行交易对用户行为进行建模对于预测共享电动滑板车的剩余寿命非常重要。具体来说，我们引入了一个无监督的对比学习组件来学习考虑退化的电动滑板车随时间的状态表示，其中用户偏好被用作状态反射器；我们进一步设计了一个基于lstm的递归组件来动态预测不确定的未来使用情况，并在此基础上融合电动滑板车的当前状态和预测使用情况，进行剩余寿命预测。广泛的实验是在从一家电动滑板车公司收集的大规模真实数据集上进行的。结果表明，RLIFE将基线提高了35.67%，并受益于学习到的用户偏好和预测的未来使用情况。
Shared electric scooters (e-scooters) have been increasingly popular because of their characteristics of convenience and eco-friendliness. Due to their shared nature and widespread usage, e-scooters usually have a short lifespan (e.g., two to five months[2]), which makes it important to predict the remaining lifespan accurately, ensuring timely replacements. While several studies have focused on the lifespan prediction of various systems, such as batteries and bridges, they present a two-fold drawback. Firstly, they require significant manual labor or additional sensor resources to ascertain the explicit status of the object, rendering them cost-ineffective. Secondly, these studies assume that future usage is similar as the historical usage. To solve these limitations, we aim at accurately predicting the remaining lifespan of e-scooters without extra cost, and its essence is to accurately represent its current status and anticipate its future usage. However, it is challenging because: i) lack of explicit rules for the e-scooters' status representation; and ii) e-scooters' future usage may significantly differ from their historical usage. In this paper, we design a framework called RLIFE, whose key insight is modeling user behaviors from trip transactions is of great importance in predicting the Remaining LIFespan of shared E-scooters. Specifically, we introduce an unsupervised contrastive learning component to learn the e-scooters' status representation over time considering degradation, where user preferences are served as a status reflector; We further design an LSTM-based recursive component to dynamically predict uncertain future usage, upon which we fuse the current status and predicted usage of the e-scooter for its remaining lifespan prediction. Extensive experiments are conducted on large-scale, real-world datasets collected from an e-scooter company. It shows that RLIFE improves the baselines by 35.67% and benefits from the learned user preferences and predicted future usage.