DOI: 10.5281/zenodo.17415891
title: Shared bicycle data

description: Comprehensive Spatiotemporal Dataset of Shared Bicycle Operations in Xiamen, China

 IMPORTANT: Data Usage Agreement

BY DOWNLOADING THIS DATASET, YOU AGREE TO THE FOLLOWING TERMS:

1. Non-Commercial Use Only

This dataset is provided for **academic research and educational purposes ONLY**. Commercial use is **strictly PROHIBITED** and violates the original data usage agreement.

2. Required Attribution

You **MUST** cite:

- Original data providers:** Xiamen shared bicycle operators via the Digital China Innovation Competition

3. Privacy Protection Requirements

You **MUST NOT**:

- Attempt to identify individuals, households, or specific residential addresses

- Reverse-engineer user identities or personal travel patterns

- Conduct individual-level identification or tracking

- Use the data in ways that could compromise user privacy

4. Aggregated Analysis Only

All analyses **MUST** be conducted at aggregated levels (neighborhood, district, or city-wide). Individual-level analysis is **PROHIBITED**.

5. No Redistribution Under Different Terms

You may share this dataset, but **ONLY** under the same CC BY-NC 4.0 license and usage terms.

6. Compliance

Your use must comply with:

- Research ethics standards

- Data protection regulations  

- The original competition data usage agreement

Dataset Overview

Temporal Coverage: December 21-25, 2020 (Monday-Friday), 6:00-10:00 a.m. (UTC+8)Spatial Coverage: Urban area of Xiamen, Fujian Province, ChinaFleet Size: 53,630 unique bicyclesCoordinate System: WGS-84

Dataset Components

1. Order Data (220,675 validated trip records)



Origin-destination pairs with precise GPS coordinates (6 decimal places)

Lock status changes (unlock/lock events)

High-resolution timestamps

Trip duration information


2. Trajectory Data (2,849,243 GPS tracking points)



Continuous GPS tracking during trips

Second-level positioning accuracy

Complete movement paths between origins and destinations


3. Electronic Fence Data (14,071 authorized parking zones)



Unique fence identifiers and names

Four-corner coordinates defining rectangular parking areas

Polygonal boundary definitions


4. Infrastructure Data



Road network information (OSM-derived bicycle-accessible routes)

Hourly weather observations (temperature, precipitation, wind, humidity, pressure)


5. Processing Scripts



Python data cleaning algorithms

MATLAB clustering and prediction models


File Structure





Shared bicycle data/
├─ Algorithm data/               # MATLAB scripts for clustering & prediction
├─ Data cleaning algorithm/      # Python scripts for preprocessing
├─ Electronic fence data/        # Fence IDs, names, and coordinates
├─ Order data/                   # Trip orders and vehicle state logs
├─ Trajectory data/              # GPS traces by bicycle and time
└─ Weather data/                 # Hourly weather observations



Data Format and Quality

Format: CSV (Comma-Separated Values) with UTF-8 encodingTime Zone: China Standard Time (UTC+8)

Units:



Distance: meters (m)

Duration: seconds

Temperature: Celsius (°C)

Wind speed: meters per second (m/s)

Precipitation: millimeters (mm)

Pressure: hPa


Quality Assurance:



Rigorous preprocessing with temporal filtering (60-3,600 seconds)

Completeness verification and anomaly removal

Cross-day correlation validation (r > 0.90)

Cryptographic hashing of all bicycle IDs for privacy protection


Key Variables

Order Data:



BICYCLE_ID: Unique bicycle identifier

LATITUDE, LONGITUDE: GPS coordinates (WGS-84)

LOCK_STATUS: 0 (unlocked/trip start) or 1 (locked/trip end)

UPDATE_TIME1, UPDATE_TIME2: Trip start and end timestamps

ORDER_DURATION: Trip duration in seconds


Trajectory Data:



BICYCLE_ID: Bicycle identifier

LOCATING_TIME: GPS positioning timestamp

LATITUDE, LONGITUDE: Real-time coordinates


Electronic Fence Data:



FENCE_ID: Unique fence identifier

FENCE_NAME: Parking zone name

LAT1-LAT4, LNG1-LNG4: Four-corner coordinates


Weather Data:



TIME: Observation timestamp

TEMP: Air temperature

RHUM: Relative humidity

PRCP: Precipitation

WSPD: Wind speed

PRES: Atmospheric pressure

COCO: Weather condition code