论文标题
城市尺度合成个人级别的车辆旅行数据
City-scale synthetic individual-level vehicle trip data
论文作者
论文摘要
记录每辆车在道路网络上的旅行活动的旅行数据描述了从个人角度来看城市交通的运作，对于运输研究非常有价值。但是，受数据隐私的限制，不能为所有研究人员打开个体级别的旅行数据，而对此的需求非常紧迫。在本文中，我们通过基于历史旅行数据为每个人生成城市级合成个人级别的车辆跳闸数据集，在该数据中，可用性和旅行数据隐私保护是平衡的。隐私保护不可避免地会影响数据的可用性。因此，我们进行了许多实验，以在不同的维度和不同的粒度下证明合成数据的性能和可靠性，以帮助用户正确判断其可以执行的任务。结果表明，综合数据与汇总级别的真实数据（即历史数据）一致，从个人角度来看是合理的。
Trip data that records each vehicle's trip activity on the road network describes the operation of urban traffic from the individual perspective, and it is extremely valuable for transportation research. However, restricted by data privacy, the trip data of individual-level cannot be opened for all researchers, while the need for it is very urgent. In this paper, we produce a city-scale synthetic individual-level vehicle trip dataset by generating for each individual based on the historical trip data, where the availability and trip data privacy protection are balanced. Privacy protection inevitably affects the availability of data. Therefore, we have conducted numerous experiments to demonstrate the performance and reliability of the synthetic data in different dimensions and at different granularities to help users properly judge the tasks it can perform. The result shows that the synthetic data is consistent with the real data (i.e., historical data) on the aggregated level and reasonable from the individual perspective.
