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:2019,28(11):19-28
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有限感知条件下的停车数据批量式修复研究
(桂林电子科技大学 计算机与信息完全学院, 桂林 541004)
Research on Batch Repair of Parking Data under Limited Sensing Conditions
(School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China)
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本文已被:浏览 23次   下载 14
投稿时间:2019-04-11    修订日期:2019-05-08
中文摘要: 随着城市中私家车保有量和使用频次显著增加,“停车难”问题逐渐成为制约城市发展的“瓶颈”.为了合理利用城市中有限的停车资源,最好的方式是建立城市级的停车诱导系统,而现阶段尚且没有有效的方案出现,究其原因是获取停车数据成本过于高昂导致的.因此,如何在不影响停车数据准确性的前提下降低其获取成本成为解决“停车难”问题的关键.本文首先基于停车数据的时空敏感性,将数据差异明显的停车场分为不同簇;再验证同一簇中停车数据符合二八定律后,筛选出影响力最大的前20%的停车场作为样本停车场,对其安装传感器获取实时停车数据并作为样本数据;考虑到现有算法得到的修补数据效果不理想,本文将一维停车数据升至二维,使用改进后的深度卷积对抗生成网络(Deep Convolution Generative Adversarial Networks,DCGAN)生成与样本数据近似同分布的新数据集.新数据集的任一条可作为同簇中任一缺失的停车数据.实现结果表明,本文提出的方案不仅可在有限感知的条件下批量式的获得大量高仿分的“伪数据”,大幅降低停车数据的获取成本,而且修复效果较当前研究有明显提高.
Abstract:With the significant increase in the number and frequency of private cars in cities, the problem of "parking difficulties" has gradually become a "bottleneck" that restricts urban development. In order to make reasonable use of the city's limited parking resources, the best way is to establish a city-level parking guidance system. At this stage, there is no effective solution. The reason is that the cost of obtaining parking data is too high. Therefore, how to reduce the procurement cost without affecting the accuracy of the parking data becomes the key to solving the problem of "parking difficulties". First, based on the spatiotemporal sensitivity of parking data, parking lots with significant data differences are divided into different clusters. After verifying that parking data in the same cluster complies with Pareto's principle, the top 20% of the most influential parking lots are selected as sample parking. At the site, sensors are installed to obtain real-time parking data and used as sample data. Considering that the patch data obtained by the existing algorithm is not satisfactory, this study upgrades the one-dimensional parking data to two-dimensional, and uses the improved Deep Convolution Generative Adversarial Networks (DCGAN) to generate new data settings and sample data. Roughly the same, any new data set can be used as any missing parking data in the same cluster. The implementation results show that the proposed scheme in this study can not only obtain a large number of high-quality "pseudo-data" in batches under the condition of limited perception, greatly reduce the acquisition cost of parking data greatly, but also significantly improve the repair effect compared with the current research.
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基金项目:国家自然科学基金(61262074,61802221,61802220,61602125);广西自然科学基金(2016GXNSFBA380010,2016GXNSFBA380153,2017GXNSFAA198192,2018GXNSFAA294123)
引用文本:
张华成,邹万,刘建明,钟晓雄,杨兵.有限感知条件下的停车数据批量式修复研究.计算机系统应用,2019,28(11):19-28
ZHANG Hua-Cheng,ZOU Wan,LIU Jian-Ming,ZHONG Xiao-Xiong,YANG Bing.Research on Batch Repair of Parking Data under Limited Sensing Conditions.COMPUTER SYSTEMS APPLICATIONS,2019,28(11):19-28

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