重新审视效用：揭示合成移动数据生成算法在现实场景中的局限性
Reconsidering utility: unveiling the limitations of synthetic mobility data generation algorithms in real-life scenarios
AI总结：
本文评估了五种合成移动数据生成算法在真实场景中的效用，发现现有模型在地图匹配、行程长度和交通流建模等方面存在明显不足，难以实现高效用与高灵活性的承诺。
摘要：
近年来，合成移动数据生成模型的发展迅速增长。这些模型旨在促进数据共享的同时保护隐私，并确保在潜在应用方面具有高效用和高灵活性。然而，当前的效用评估方法未能充分考虑现实需求。我们从现实世界适用性的角度，评估了五种最先进的合成方法，每种方法分别在有和没有差分隐私（DP）保障的情况下进行评估。具体而言，我们关注所谓的行程数据，这些数据编码了细粒度的城市移动，例如GPS追踪的出租车行程。这类数据对于道路网络层面的下游任务尤其有价值。因此，我们的第一步是对合成数据进行适当的地图匹配，随后将生成的行程与OpenStreetMap中实现的路由算法生成的行程进行比较，后者作为一个高效且隐私友好的基线。在评估的五个模型中，一个未能在合理的计算时间内生成数据，另一个产生了过多的跳跃而无法满足地图匹配的要求。其余三个模型在一定程度上成功保持了空间分布，其中一个甚至在差分隐私保障下也能做到。然而，所有模型都难以生成具有合理行程长度的有意义的地理位置序列，也难以准确建模交叉口的交通流。需要指出的是，行程数据包含空间分布之外的多种相关特征，例如时间信息，而这些特征都被这些模型丢弃了。因此，我们的结果表明，当前的合成模型未能兑现其高效用和高灵活性的承诺。
英文摘要：
In recent years, there has been a surge in the development of models for the generation of synthetic mobility data. These models aim to facilitate the sharing of data while safeguarding privacy, all while ensuring high utility and flexibility regarding potential applications. However, current utility evaluation methods fail to fully account for real-life requirements. We evaluate the utility of five state-of-the-art synthesis approaches, each with and without the incorporation of differential privacy (DP) guarantees, in terms of real-world applicability. Specifically, we focus on so-called trip data that encode fine granular urban movements such as GPS-tracked taxi rides. Such data prove particularly valuable for downstream tasks at the road network level. Thus, our initial step involves appropriately map matching the synthetic data and subsequently comparing the resulting trips with those generated by the routing algorithm implemented in OpenStreetMap, which serves as an efficient and privacy-friendly baseline. Out of the five evaluated models, one fails to produce data within reasonable computation time and another generates too many jumps to meet the requirements for map matching. The remaining three models succeed to a certain degree in maintaining spatial distribution, one even with DP guarantees. However, all models struggle to produce meaningful sequences of geo-locations with reasonable trip lengths and to model traffic flow at intersections accurately. It is important to note that trip data encompasses various relevant characteristics beyond spatial distribution, such as temporal information, all of which are discarded by these models. Consequently, our results imply that current synthesis models fall short in their promise of high utility and flexibility.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
