- Overview
- Datasets
- Providers
- Use Cases
- Attributes
- FAQ
- Overview
- Datasets
- Providers
- Use Cases
- Attributes
- FAQ
What is Ride-Sharing Data?
Ride-sharing data is information collected and analyzed from rides taken through a ride-sharing service, such as Uber or Lyft. It includes details like pick-up and drop-off locations, trip duration, distance traveled, fare charged, driver ratings, and user feedback. This data is used to improve service efficiency, optimize driver allocation, enhance safety measures, and gain insights into customer preferences and behavior.
Examples of Ride-Sharing Data include information about trip duration, distance traveled, pick-up and drop-off locations, driver ratings, and passenger feedback. Ride-Sharing Data is used for analyzing patterns and trends in transportation, optimizing routes, improving service quality, and making data-driven decisions for ride-sharing companies. In this page, you’ll find the best data sources for Ride-Sharing Data.
Best Ride-Sharing Databases & Datasets
Here is our curated selection of top Ride-Sharing Data sources. We focus on key factors such as data reliability, accuracy, and flexibility to meet diverse use-case requirements. These datasets are provided by trusted providers known for delivering high-quality, up-to-date information.
Email Receipt Ride-Sharing Data | Granular Transactional Data for Food Delivery/ Ride-Sharing Sector | Emerging Markets (APAC, LATAM, Europe & USA)
Granular E-Receipt Data for Middle East | UAE / Kuwait / Qatar / Saudi | Ride-Sharing Data | Restaurant & Food Delivery Transaction Data
Consumer Transaction Data | UK & FR | 600K+ daily active users | Internet - Ride Hailing & Delivery | Raw, Aggregated & Ticker Level
SKU-Level Granular Email Receipt Data | Consumer Transaction Data for USA & Continental Europe | Ecommerce / Food Delivery / Ride Hailing / Payments
Consumer Transaction Data | UK & FR | 600K+ daily active users | Retail - Footwear | Raw, Aggregated & Ticker Level
Consumer Transaction Data | UK & FR | 600k+ daily active users | Airlines - Cargo | Raw, Aggregated & Ticker Level
Consumer Transaction Data | UK & FR | 600K+ daily active users | Autos - OEMs | Raw, Aggregated & Ticker Level
HERE Routing - provided by MBI Geodata
Analysys Qianfan：China mobile app usage tracker capturing activity for 600m+ MAU and 80m+ DAU
Consumer Transaction Data | UK & FR | 600K+ daily active users | Consumer - Services | Raw, Aggregated & Ticker Level
Can't find the data you're looking for?
Let data providers come to you by posting your request
Post your request
Top Ride-Sharing Data Providers & Companies
-
Measurable AI
CoverageUSAUK+248- Largest e-receipts panel for emerging markets: Asia, Latin America, Middle East, Turkey, and India - SKU level high definition transactional data that is anonymized and available in granular or aggregated form on a weekly basis - We own our datasets with strict privacy measures in placeGDPR/CCPACompliantSourceOwn consumer appsCoverageEmerging Markets -
90 West Data
CoverageUSAUnique US consumer transaction data. Insights from millions of daily transactions.100%Client focused100%Clean, Complete, Transparent100%Research Ready -
Kuwala
Kuwala enables data teams to use the power of location data through seamless integration via API.1,000Free Requests50 MillionMonthly Data Points+25POI Segments
Popular Use Cases for Ride-Sharing Data
Ride-Sharing Data is essential for a wide range of business applications, offering valuable insights and driving opportunities across industries. Below, we have highlighted the most significant use cases for Ride-Sharing Data.
Main Attributes of Ride-Sharing Data
Below, we outline the most popular attributes associated with this type of data—features that data buyers are actively seeking to meet their needs.
| Attribute | Type | Description | Action |
|---|---|---|---|
| String | The postal code of an address. | View 30 datasets | |
| String | The country code in Alpha-2 format (ISO 3166) | View 3 datasets | |
| String | The email address of a company or contact. | View 3 datasets | |
| String | The address of a company or contact (street name, number, zip code, city, county, country). | View 1 datasets |
Frequently Asked Questions
How is the Quality of Ride-Sharing Data Maintained?
The quality of Ride-Sharing Data is ensured through rigorous validation processes, such as cross-referencing with reliable sources, monitoring accuracy rates, and filtering out inconsistencies. High-quality datasets often report match rates, regular updates, and adherence to industry standards.
How Frequently is Ride-Sharing Data Updated?
The update frequency for Ride-Sharing Data varies by provider and dataset. Some datasets are refreshed daily or weekly, while others update less frequently. When evaluating options, ensure you select a dataset with a frequency that suits your specific use case.
Is Ride-Sharing Data Secure?
The security of Ride-Sharing Data is prioritized through compliance with industry standards, including encryption, anonymization, and secure delivery methods like SFTP and APIs. At Datarade, we enforce strict policies, requiring all our providers to adhere to regulations such as GDPR, CCPA, and other relevant data protection standards.
How is Ride-Sharing Data Delivered?
Ride-Sharing Data can be delivered in formats such as CSV, JSON, XML, or via APIs, enabling seamless integration into your systems. Delivery frequencies range from real-time updates to scheduled intervals (daily, weekly, monthly, or on-demand). Choose datasets that align with your preferred delivery method and system compatibility for Ride-Sharing Data.
How Much Does Ride-Sharing Data Cost?
The cost of Ride-Sharing Data depends on factors like the datasets size, scope, update frequency, and customization level. Pricing models may include one-off purchases, monthly or yearly subscriptions, or usage-based fees. Many providers offer free samples, allowing you to evaluate the suitability of Ride-Sharing Data for your needs.
What Are Similar Data Types to Ride-Sharing Data?
Ride-Sharing Data is similar to other data types, such as Marine Traffic Data, Transport and Logistics Data, Rail Traffic Data, Import Export Data, and Global Trade Data. These related categories are often used together for applications like Transaction Data Enrichment.
