Lime Micromobility Vehicle Dataset – Scooters by App & Location

# **Description**

This dataset is a sample **micromobility records** collected from shared mobility applications (e.g., scooters, bikes, or compact EV rentals). It contains detailed, structured information about each available vehicle at the time of extraction, including:

- **🗺️ Geolocation & Positioning:** Vehicle latitude and longitude, proximity center coordinates, auto-ring and proximity radii, remaining range in miles.
- **💰 Pricing Information:** Hire price, price per minute, and additional dynamic pricing metadata.
- **🚗 Vehicle & App Attributes:** Title, type, app/platform name, vehicle availability logic, and available actions.
- **📊 Usage Context & Distance Logic:** Distance remaining, distance to proximity center, and assigned range category.

# **Use Case Examples**

- **🔍 Urban Mobility Analytics:** Track availability and distribution of vehicles by zone, provider, and proximity logic.
- **📈 Pricing Optimization:** Analyze pricing strategies per distance, location, or time of day.
- **🌍 Geospatial Modeling:** Use in simulations, heatmaps, or coverage analysis for smart cities and transportation planning.
- **🤖 Machine Learning Features:** Feed into demand forecasting models, pricing recommendation engines, or geofencing behavior predictors.
- **📡 Mobility Platform Benchmarking:** Compare how different apps implement vehicle availability logic and pricing rules.

# **Sample Size & Customization**

This sample features 1,000 records of Lime e-scooters available in the City of London.

We offer full customization based on your project needs, including:

- Data volume (up to full-scale vehicle availability datasets),
- Update frequency (daily, weekly, or near real-time),
- Schema adaptation or field selection.

We can provide data from a wide range of shared mobility apps and services, including but not limited to: **Lime, Bolt, Spin, ofo, TIER Mobility, Voi Technology, Dott, micromobility.com, BinBin, Marty Technologies, Bird, Circ, Veo, Wheels, nextbike, HOP, NAVEE TECH.**

Need a specific service or app not listed above? We can expand the source list to include **additional platforms relevant to your project**.

**📩** Contact us to define your data needs — our team can create a custom Snowflake listing tailored to your specific use case and schema preferences.

原始信息汇总

Lime Micromobility Vehicle Dataset – Scooters by App & Location

数据集概述

• 提供商: GroupBWT
• 试用状态: 免费试用
• 样本大小: 1,000条记录（伦敦市Lime电动滑板车数据）
• 更新频率: 每日
• 时间覆盖范围: 最近6个月
• 地理覆盖范围: 全球（按经纬度）

数据集描述

包含从共享出行应用（如电动滑板车、自行车或紧凑型电动车租赁）收集的微移动记录，结构化信息包括：

• 🗺️ 地理位置与定位：车辆经纬度、接近中心坐标、自动环和接近半径、剩余里程
• 💰 定价信息：租赁价格、每分钟价格、动态定价元数据
• 🚗 车辆与应用属性：标题、类型、应用/平台名称、车辆可用性逻辑、可用操作
• 📊 使用上下文与距离逻辑：剩余距离、到接近中心的距离、分配的范围类别

使用案例

• 🔍 城市移动性分析：按区域、提供商和接近逻辑跟踪车辆可用性和分布
• 📈 定价优化：分析按距离、位置或时间段的定价策略
• 🌍 地理空间建模：用于智能城市和交通规划的模拟、热图或覆盖分析
• 🤖 机器学习特征：用于需求预测模型、定价推荐引擎或地理围栏行为预测器
• 📡 移动平台基准测试：比较不同应用的车辆可用性逻辑和定价规则

业务需求

• 市场分析：评估供应密度、提供商分布和区域移动趋势
• 定价分析：探索按位置、车辆类型和可用性的定价模型
• 需求预测：使用历史和定价数据预测时间和地理需求
• 人流量分析：分析车辆接近和可用性模式以推断热门通勤路径
• 位置规划：识别高需求区域、服务缺口和新移动枢纽的最佳位置
• 机器学习：训练需求预测、动态定价、车辆重新定位或范围估计的预测模型

数据字典

• APP: Varchar
• START_POINT_LATITUDE: Number
• START_POINT_LONGITUDE: Number
• ID: Varchar
• TITLE: Varchar
• TYPE: Varchar
• LATITUDE: Number
• LONGITUDE: Number
• HIRE_PRICE: Varchar
• PRICE_PER_MIN: Varchar
• DISTANCE_REMAINED: Varchar
• EXTRACTED_AT: Timestamp_NTZ
• RANGE_MILES: Number
• PRICING_INFO: Varchar
• AVAILABLE_ACTIONS: Varchar
• PROXIMITY_RADIUS_METERS: Number
• AUTO_RING_RADIUS_METERS: Number
• PROXIMITY_CENTER_LAT: Number
• PROXIMITY_CENTER_LON: Number
• AVAILABLE_SECTIONS: Varchar
• AVAILABLE_MAIN_ACTIONS: Varchar
• DISTANCE_TO_PROXIMITY_CENTER: Number
• RANGE_CATEGORY: Varchar
• DISTANCE_REMAINED_NUMERIC: Number
• PRICE_PER_MIN_NUMERIC: Number

定制选项

• 数据量（可达全规模车辆可用性数据集）
• 更新频率（每日、每周或近实时）
• 模式适应或字段选择
• 可提供来自多种共享移动应用和服务的数据

联系方式

• 销售: [e-mail]
• 支持: [e-mail]

搜集汇总

背景与挑战

背景概述

该数据集提供伦敦市1000条Lime电动滑板车的微移动记录，包含地理位置、定价策略、车辆属性和使用场景等结构化信息，适用于城市交通分析、定价优化和地理空间建模等场景。支持按需定制数据量、更新频率和字段选择。