Ridesharing with trained ML algorithms

What is ridesharing?

Ridesharing is the practice of sharing a cab with other commuters as a means of lessening traffic congestion or pollution. Ride-Sharing is to share a ride among multiple users and is one of the effective ways of saving plus sharing oil or fuel resources Presently Ride Sharing is one of the most preferred models from past years synonymous and is also known as  Ride-Hailing, Car-Pooling, and Vehicle-Pooling. 

What are Ride-sharing apps?

Ride-sharing services like Uber employ AI to determine the time needed to transport users to their desired locations. The technology lets users know details such as when their driver will arrive when they will arrive at their destination, and how long it will take for food to be delivered. Uber also uses AI to set prices depending on what they think you are willing to pay. According to The Independent, Uber also uses AI to determine if a rider is drunk before a driver accepts a pickup. It does this by analyzing and comparing factors like walking speed and typing patterns.

AI & ML with the help of Data annotation make these functions possible with utmost accuracy. 

WHAT DO WE DO?

We serve the ride-hailing industry by setting up dedicated teams of Analysts to run the manual, repetitive processes that might burden your team and limit your ability to scale. We train the entire team on your data, process, and guidelines, and commit to SLAs on the quality and timeliness of our work. Use cases include:

Real-time categorization and sentiment analysis of rider safety tickets for leading ride-sharing company:

Level 0 and Level 1 Customer Service

E.g. A rider submits a comment/safety ticket through your app after a ride reporting that her driver was speeding and reckless > an analyst will read the ticket and categorize which type of incident is reported (“Unsafe driving”), and score the sentiment of the text (Very Negative, Negative, Neutral, Positive, Very Positive) and can tag as “Urgent” > you route the ticket to the correct Customer Sat team you have in-house to take care of resolving the situation

Processed over 1 Million Tickets a month

Moderation of comments / rider profile images / etc.

We can review text and images for community safety, flagging any user-submitted content that does not meet your community’s safety guidelines

ID Verification

We have the IT Security (and experience) to ensure the privacy of PII and other sensitive data

We have multiple projects where we help manually validate ID submissions based on our customer’s guidelines (e.g. valid driver license, validation of identity across multiple source documents (ID, bank statement, etc.) 

Some of the major contributions to Ridesharing models by AI & ML are as follows: 

Filtering riders matched in the characteristics matching layer using the matching layer. 

Proper Recording user feedback and computing the two main characteristics for every user, which are Feedback-Given-Characteristic and Feedback-Received-Characteristic. 

Using a Machine Learning Algorithm to predict the two main characteristics and recommend riders for newly registering users. 

Saving energy, time and most important resources like petrol and diesel 

Learning Spiral is here to offer high-quality Data labeling services that provide potential to your Algorithms. And annotate data with utmost accuracy including Ridesharing models & automotive industry We are here to Empower your algorithm and bridge the gap between machines and humans with our reliable data labeling and data annotation services & help to serve all major models and industries in the coming years.

Thanks For reading!

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