基于容东片区地下车库的停车位数及类型数据集

容东片区地下车库停车位数据集的构建，旨在为地下停车场数字孪生系统提供可靠的数据支持，以实现高效的无缝定位和导航展示。随着城市交通管理的日益复杂，智能停车系统的需求不断增加，地下车库的合理利用和管理成为提升城市整体交通效率的重要手段。本数据集的主要来源为CAD文件，通过将CAD图纸转化为地图数据，提取停车位信息。这一过程涉及到停车位数量、车位ID、类型等多项数据的收集与整理。具体地，数据采集于2024年9月11日在雄安新区河北建投大厦进行，覆盖的地下停车场规模不小于150万平方米，提供了约3万个停车泊位的信息，确保了数据的全面性和准确性。为了保证数据的质量，采用了多种质量保障手段。首先，为实现地下停车场的数字孪生，构建了三维地图和实时动态监控平台。通过结合Apache和Python等软件，系统能够进行实时数据更新与分析，确保停车位信息的时效性和准确性。此外，针对地上地下一体化导航的核心性能指标，如时延、准确性和移动性，使用Vmware和Cadence等工具进行了多次离线和在线实验。这些实验旨在验证系统的导航精度，确保其符合实际应用的要求。在数据处理过程中，利用地图工具测量出总体覆盖的小区面积，从而形成一个完整的停车位数据集。通过这种方式，不仅提升了数据的可用性，还为后续的停车管理和导航系统的优化提供了重要依据。The construction of the parking space dataset for underground garages in the Rongdong Area aims to provide reliable data support for digital twin systems of underground parking lots, enabling efficient seamless positioning and navigation display. As urban traffic management grows increasingly complex, the demand for intelligent parking systems continues to rise, and the rational utilization and management of underground garages have become an important means to improve overall urban traffic efficiency. The main source of this dataset is CAD files. Parking space information is extracted by converting CAD drawings into map data. This process involves the collection and organization of multiple data items such as the number of parking spaces, parking space IDs, and parking space types. Specifically, the data was collected on September 11, 2024, at the Hebei Construction and Investment Building in Xiong'an New Area. The covered underground parking lot covers an area of no less than 1.5 million square meters, providing information on approximately 30,000 parking berths, ensuring the comprehensiveness and accuracy of the dataset. To ensure data quality, multiple quality assurance measures are adopted. First, to realize the digital twin of the underground parking lot, a 3D map and real-time dynamic monitoring platform were constructed. By integrating software tools such as Apache and Python, the system can perform real-time data updates and analysis, ensuring the timeliness and accuracy of parking space information. In addition, multiple offline and online experiments were conducted using tools such as VMware and Cadence to test the core performance indicators of integrated above-ground and underground navigation, such as latency, accuracy, and mobility. These experiments aim to verify the navigation accuracy of the system and ensure it meets the requirements of practical applications. During the data processing stage, map tools were used to measure the total covered community area, thus forming a complete parking space dataset. This approach not only improves the availability of the data but also provides an important basis for subsequent parking management and navigation system optimization.

搜集汇总

背景与挑战

背景概述

该数据集旨在为地下停车场数字孪生系统提供数据支持，以实现高效的无缝定位和导航展示。数据来源于CAD文件转换，采集于2024年9月11日在雄安新区河北建投大厦，覆盖不小于150万平方米的地下停车场，包含约3万个停车泊位的信息。