Guangdong Provincial Key Laboratory of Intelligent Transportation System, School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou, 510275, China.
School of Environmental and Chemical Engineering, FoShan University, FoShan, 528000, China.
Sci Data. 2023 Feb 15;10(1):96. doi: 10.1038/s41597-023-01997-4.
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.
出行数据记录了道路网络上每辆车的出行活动，从个体角度描述了城市交通的运行情况，对交通研究极具价值。然而，由于数据隐私的限制，个体层面的出行数据无法向所有研究人员开放，但对其的需求又非常迫切。为此，我们基于历史出行数据为每个个体生成出行记录，从而生成一个城市级的综合个体层面车辆出行数据集，在保证数据可用性的同时，兼顾数据隐私保护。隐私保护不可避免地会影响数据的可用性。因此，我们进行了大量实验，从不同维度和不同粒度展示了合成数据的性能和可靠性，以帮助用户正确判断它所能完成的任务。结果表明，综合数据在聚合层面与真实数据（即历史数据）一致，从个体角度来看也是合理的。
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