发布巴黎大区高精度个人移动数据集,涵盖3337人一周轨迹。
The NetMob25 Dataset: A High-resolution Multi-layered View of Individual Mobility in Greater Paris Region
- 通过GPS设备每2-3秒记录一次位置,采集3337名居民一周轨迹
- 包含超8万条标注行程,含时间、交通方式和出行目的
- 数据经匿名化处理,可用于城市规划与行为研究
尽管研究人员和城市决策者对个体移动模式的兴趣日益增长,高质量的移动数据仍然稀缺。Netmob25数据挑战赛通过发布一份基于GPS的独有移动数据集,填补了这一空白。该数据源自2023年法国法兰西岛地区(巴黎大区)的EMG 2023 GNSS移动调查,覆盖3337名年龄在16至80岁之间的志愿者,数据收集时间为2022年10月至2023年5月。每位参与者配备专用GPS追踪设备,以每2-3秒一次的频率记录位置,并需配合数字或纸质日志记录出行。所有推断的移动轨迹均通过算法处理并经后续电话访谈验证。数据集包含三个部分:(i) 个体数据库,包含人口统计、社会经济及家庭特征;(ii) 行程数据库,包含超过8万条标注位移,涵盖时间戳、出行方式和出行目的;(iii) 原始GPS轨迹数据库,包含约5亿个高频位置点。提供统计加权机制以支持人群层面估计。对GPS轨迹实施了全面的匿名化流程,确保符合GDPR要求的同时保留分析价值。访问需签署保密协议并同意挑战赛条款。本文详述了调查设计、采集流程、处理方法及数据集特性。
原文摘要 · Abstract (English)
High-quality mobility data remains scarce despite growing interest from researchers and urban stakeholders in understanding individual-level movement patterns. The Netmob25 Data Challenge addresses this gap by releasing a unique GPS-based mobility dataset derived from the EMG 2023 GNSS-based mobility survey conducted in the Ile-de-France region (Greater Paris area), France. This dataset captures detailed daily mobility over a full week for 3,337 volunteer residents aged 16 to 80, collected between October 2022 and May 2023. Each participant was equipped with a dedicated GPS tracking device configured to record location points every 2-3 seconds and was asked to maintain a digital or paper logbook of their trips. All inferred mobility traces were algorithmically processed and validated through follow-up phone interviews. The dataset includes three components: (i) an Individuals database describing demographic, socioeconomic, and household characteristics; (ii) a Trips database with over 80,000 annotated displacements including timestamps, transport modes, and trip purposes; and (iii) a Raw GPS Traces database comprising about 500 million high-frequency points. A statistical weighting mechanism is provided to support population-level estimates. An extensive anonymization pipeline was applied to the GPS traces to ensure GDPR compliance while preserving analytical value. Access to the dataset requires acceptance of the challenge's Terms and Conditions and signing a Non-Disclosure Agreement. This paper describes the survey design, collection protocol, processing methodology, and characteristics of the released dataset.
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