Concept of a Data Thread Based Parking Space Occupancy Prediction in a Berlin Pilot Region
Concept of a Data Thread Based Parking Space Occupancy Prediction in a Berlin Pilot Region
复制标题
柏林试点地区基于数据线程的停车位占用预测的概念
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
F. Kirchner
中科院分区：
文献类型：
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作者：
Tim Tiedemann;T. Voegele;M. M. Krell;J. H. Metzen;F. Kirchner
在提出的研究项目中，开发了柏林公共试验区以停车位为重点的多式联运路线规划的软件和硬件基础设施。其中一个中心主题是开发一个预测系统，该系统可以在未来给定的日期和时间内估计试点地区的停车位占用情况。占用数据将通过项目中开发的路边停车传感器在线收集。占用率预测将使用“神经气体”机器学习与使用数据线程来提高预测质量的提出方法相结合来实现。在本文中，简要概述了整个研究项目。此外，提出了软件框架的概念和学习方法，并展示了首次收集的数据。更详细地解释了使用数据线程的预测方法。
In the presented research project, a software and hardware infrastructure for parking space focussed inter-modal route planning in a public pilot region in Berlin is developed. One central topic is the development of a prediction system which gives an estimated occupancy for the parking spaces in the pilot region for a given date and time in the future. Occupancy data will be collected online by roadside parking sensors developed within the project. The occupancy prediction will be implemented using “Neural Gas” machine learning in combination with a proposed method which uses data threads to improve the prediction quality. In this paper, a short overview of the whole research project is given. Furthermore, the concept of the software framework and the learning methods are presented and first collected data is shown. The prediction method using data threads is explained in more detail.