Dataset opportunity
Green On — 移动遥测数据集机会
Green On 持有的中等规模移动遥测数据集，可用于预测性维护和异常检测。
Score
71.5
Score (0–100) blends weighted dimensions — dataset rarity, training value, buyer demand, evidence strength and right-to-license. 70+ is deal-ready. See the scored dimensions below for the breakdown.Confidence
49%
Action
数据共享协议
The recommended deal structure for this dataset: Acquire (full buyout), License (paid usage rights), Data Sharing Agreement (controlled access, no transfer of ownership), Partnership (co-development) or Annotation Program (labeling). Chosen from data ownership, licensing complexity and accessibility.Market
全球车辆预测性维护市场 = 2024年46.6亿美元，复合年增长率17.5% (2025-2034)
Recent dated external facts that triggered this opportunity — auditable provenance.
- 📰press2026-06-03
Les électriques portent le marché allemand en mai 2026
journalauto.com ↗ - 📰press2026-06-03
Bot Auto names Brett Suma as president and COO to scale autonomous trucking
freightwaves.com ↗ - 📰press2026-06-03
VUL : Renault, Nissan et Mercedes-Benz dégainent leurs nouveaux CEE
journalauto.com ↗ - 📰press2026-06-02
B.C. Bill would make dashboard cameras mandatory on commercial vehicles
freightwaves.com ↗ - 📰press2026-06-02
L’électrique prend le pouvoir dans les flottes
journalauto.com ↗
Lineage
How this lead was derived
The signal-first chain, end to end: recent external signals → qualified niche → resolved data-holder → site verification → scored opportunity. Every lead is explainable.
Profile
Dataset profile
Type
移动遥测数据集
Modality
时间序列
Sector
移动出行
Volume
中等
Freshness
实时
Rarity
高（专有）
Accessibility
受限
Legal
公司所有 — GDPR敏感（PII审查）
Buyer persona
工业AI和维护优化供应商
Green On 拥有丰富的移动遥测数据集，主要以时间序列模式呈现，包含事件流、地理数据和物联网数据。这些粒度数据捕获了车辆的实时运行参数和使用模式，非常适合预测性维护应用，能够预测设备故障并优化维护计划。尽管存在客户合同带来的复杂性以及包含GDPR敏感的个人使用数据（位置、租赁历史），该数据集仍然极具价值。全球预测性维护市场是该数据的一个主要目标市场，2024年估计为347.7亿美元，预计到2035年将达到4496亿美元，复合年增长率（CAGR）高达26.2%（2025-2035）。具体而言，仅车辆预测性维护市场在2024年价值为46.6亿美元，预计将以17.5%的复合年增长率（2025-2034）增长，这突显了移动出行领域对此类专业物联网数据的巨大需求。⚠ 尽职调查（有价值的数据，可协商访问）：数据通过客户合同（企业和地方当局）生成；数据包含GDPR敏感的个人使用数据（位置、租赁历史）；数据可能与其运营平台和应用程序绑定。· 公司性质：独立。
Scoring
Scored dimensions
Explainable, evidence-based dimensions (0–100). The radar shows the investment axes.
这些证据共同证实了 Green On 拥有一个丰富、专有的时间序列数据集，该数据集来源于其广泛的电动自行车车队运营。这些数据对于寻求开发先进车辆预测性维护解决方案的工业AI和维护优化供应商高度相关，该市场预计到2024年将达到46.6亿美元。其稀有性以及对实际移动遥测的直接适用性，使其在快速扩张的领域中，对于释放新效率和降低运营成本具有极高的价值。
See dimension details ↓- Dataset Specificity90
主导的“物联网数据”，移动出行领域，3种特定类型
How sharply the data targets a specific, hard-to-substitute domain or task. Niche, well-defined data scores higher than generic. - Dataset Rarity82
专有领域数据
How scarce and proprietary the data is. Unique domain data scores high; openly available data lowers it. - Dataset Volume52
3条证据命中
Apparent scale of the data, inferred from the number of evidence hits and any explicit volume mentions. - Dataset Freshness82
实时/流式
How current the data stays — real-time/streaming scores highest, periodic dumps lower. - Training Value84
适用于预测性维护
How useful the data is for the target AI use-case — its fit for model training or fine-tuning. - Buyer Demand92
汽车预测性维护市场，严重依赖移动遥测数据进行AI/ML，预计到2030年将超过1300亿美元，从2024年到2030年以惊人的21%复合年增长率增长。
How strongly AI builders and companies are likely to want this data, based on market signals. - Legal Accessibility20
受限/未知
How legally easy the data is to obtain and use — open/API access scores high; PII or regulated data scores low. - Acquisition Feasibility30
中等难度，独立
How realistic it is to actually obtain the data, given access difficulty and the holder's corporate structure. - Evidence Strength62
3种证据类型，3条命中
How solid the proof is that the company holds this data — diversity of evidence types and number of hits. - Right to License62
所有权=自有，许可=GDPR敏感
Whether the company can legally license the data out — based on ownership and licensing complexity. - Corporate Independence90
独立
Whether the holder can decide alone — an independent company scores higher than a subsidiary of a large group. - Data Orientation22
0个数据需求信号（0种类型）
How actively the company invests in data, measured by its data-appetite signals (hires, products, APIs…). - Dormant Data Surplus92
盈余=高，5个近期外部信号 — 超出已货币化范围的专有数据
Volume and value of proprietary data this company holds BEYOND what it already monetises — the dormant surplus we can unlock. A company can sell some insights AND still sit on a far larger dormant asset. - ICP Audit100
✓ 目标良好 — Green On (France) 是一家在法国运营电动自行车共享服务的西班牙中小企业，在其运营过程中会产生有价值的出行遥测数据，并且目前似乎没有出售这些数据，使其成为一个强有力的目标。问题：存在多个名称相似的“Green On”公司，需要根据提供的 URL 进行仔细区分；虽然 Green On 的运营本身会产生遥测数据，但它们并未明确宣传“遥测数据”
Evidence
Dataset evidence & lineage
What the typed evidence proves the company holds — reframed for clarity and set against the market.
IoT / sensor data
此证据证实 Green On 从其运营的电动自行车车队收集物联网遥测数据，为车辆性能提供对预测分析至关重要的粒度洞察。
Geospatial data
这表明收集了匿名位置数据，为理解移动模式和优化资产部署提供了有价值的背景信息。
Event streams
这证实了捕获运营事件流，详细说明了服务使用情况，包括租赁指标和站间流量，这对于需求预测和服务优化至关重要。
Marketplace
Dataset details
Geographic coverage
Global
Time range
Real-time
Update frequency
Real-time
Delivery
API
Formats
Time Series, JSON
License
One-time license for predictive maintenance use cases, subject to GDPR and client contract restrictions.
Personal data
Contains PII
Indicative estimate, derived from public signals — not a quote, not contractual, and not agreed with the company. Is this your company? Correct it.
This dataset's value is driven by its proprietary nature and direct application to the high-growth predictive maintenance market. The real-time telemetry from a proprietary electric bike fleet offers unique insights for AI-driven vehicle maintenance optimization.
Detailed schema & sample available on access request.
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This listing was generated automatically from public signals. It is not verified, and we are not affiliated with this company.
Coverage
Scanned sources
Deliverable
Premium dataset report
Green On Mobility Telemetry — a Moderate mobility telemetry dataset (Time Series modality) in the mobility domain. Primary AI use-case: Predictive Maintenance. Market signal: Global Predictive Maintenance for Vehicles Market = $4.66 Billion in 2024, CAGR 17.5% (2025-2034). Investment score 71.5/100 (confidence 0.49). Recommended action: Data Sharing Agreement.
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