基于循环生成式对抗网络实现停车场 时空数据的修复
基于循环生成式对抗网络实现停车场 时空数据的修复
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广东省科技计划重大项目(2015B010106004)

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Spatiotemporal Data Repairing of Parking Lots Based on Recurrent Generative Adversarial Networks
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    摘要:

    停车诱导技术在一定程度上缓解了高峰时段无序停车问题,并减少了司机寻找车位的时间,但停车诱导系统对实时数据和历史数据有较高的依赖。如果缺少相应数据,那么诱导系统的准确性将大打折扣。针对这一问题,该文通过挖掘停车场周围的空间数据,提出了一种停车场空间相似度度量,并计算出停车场空间相似情况下其数据的相似条件概率。当条件概率足够大时,以已知数据为学习样本,使用循环生成式对抗网络获得修复数据。实验结果表明,当停车场空间具有较高空间相似度时,其数据同样有大概率的相似性,使用循环生成式对抗网络生成的数据与真实数据具有相同的分 布。该文提出的方法可在短时间内生成大量的合理数据,现停车场数据的修复,提高诱导系统的可靠性。

    Abstract:

    The parking guidance system (PGS) can alleviate the disordered parking problem in the peak time, and reduce the time to find parking space. But existing PGS techniques are quite dependent to the realtime data and historical data, its performance will be greatly degraded when the data is insufficient. To solve this problem, a data repairing based PGS method was presented. First, by mining the spatial data around the parking lots, a spatial similarity metric of parking lot was proposed. Then, the possibility of parking data similarity was calculated when the parking lots had spatial similarity. If the conditional probability was large enough, the known data of parking lots would be used as the learning samples. Finally, the reparative data could be generated by recurrent generative adversarial networks. Experimental results show that when the parking lots have high spatial similarity, the data of parking lots have high similarity probability also. The data generated by the recurrent generative adversarial networks also have the same distribution with real data. By the proposed method, a large number of reasonable data can be generated efficiently, and the PGS performance can be improved while only few parking data is available.

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引文格式
孙玉强,彭 磊,李慧云.基于循环生成式对抗网络实现停车场 时空数据的修复 [J].集成技术,2018,7(6):9-18

Citing format
SUN Yuqiang, PENG Lei, LI Huiyun. Spatiotemporal Data Repairing of Parking Lots Based on Recurrent Generative Adversarial Networks[J]. Journal of Integration Technology,2018,7(6):9-18

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  • 在线发布日期: 2018-11-20
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