Data story of Taxi Booking apps

This data story is my personal experience using Taxi Booking apps. I use both Ola / Uber. Some of the common observations. I have tried to outline the data flow, Reporting use cases, ML use cases involved based on my understanding and usage.

Observations using App

• On booking cab request we can see
• Vehicle type and expected time
• SLA to reach the destination 
• Real-time message processing, notifying, accepting and notifying rider and driver partner (stream processing, segmentation, notification, acceptances)
• Display stats of driver during the trip

Pain points Observed

• You book a trip at x price. The trip gets canceled by the driver. Now when you book again peak price is applied 
• My personal experience drivers more comfortable with cash payments 
• Reluctant to switch on AC by mileage conscious drivers
• Target driven. I have spoken to driver partners driving non stop 24hrs to meet targets 
• I never had a great experience using cab pooling. It took me 2x time most cases unless if it is an odd time

Data collected 

• Trip details
• Passenger details
• Fare details
• Ratings of driver and passenger 
• Cab bookings at each location point
• Find maximum long routes, maximum booking points location
• Find Maximum booking time across airports, bus stop, Railway stations 
• Driver partner ride and earning details 
• Data available at the city level, Area level (Slide / Dice)
• Review / Rating / Feedback on Cancellation

Data at customer Level

• Trip Details by Each Customer. Expenditure at the customer level
• Since location is shared they can identify Office, Home, Restaurants, Malls, Airports, Railway stations 

ML use cases for customers / Booking

• Segment people using services based on trip distance, number of trips, trip expense
• Classify people in terms of potential weekend travelers, shopper, Stay at home person 
• Recommend areas for peak pricing
• Recommend timing for peak pricing
• Recommend peak pricing with the highest probability of conversion (A/B testing)
• Predict top 10 cab pickup points and order numbers considering historical data seasonality
• Predict customer churn
• Promotions based on segmenting customers (High Value, Medium, Low Spending Customers)
• A lot of scope vision apps to do audio based analytics, classic drowsiness detection, distraction, use of the mobile phone ( custom object detection models)
• NLP on Customer feedback / Sentiment Analysis

ML use cases for driver partner

• Predict driver churn
• Predict the number of trips for next week and set target accordingly 
• Predict the nearest area where the probability of booking higher for driver partner
• Predict Acceptance Rate for a Route based on Driver preferences derived from historical data

Promotions

• Promotions and recommendations for eateries
• Promotion for a pass for customers 

Data collected from the vehicle (If it is fitted with sensors to collect data) - Car Manufacturers and Ride Sharing App Partnerships - 'Data Access' to understand

• Access to data which can be used to build predictive models, deep learning models for training Autonomous driving decisions
• Real-time data pipeline for sensors, devices, software, vision data for building models customized for Indian Conditions
• Access to Components Utilization patterns for different vehicles running in different Regions / State
• All this data will help in building Connected Cars, Training better models for better Data-Driven Decisions
• Driving conditions vs vehicle performance in those road conditions

Other Factors / Emerging Competitors

Quick ride has come up, which is also sharing the same space of ride-sharing apps but for a different segment. Quick ride is more economical, predictable with recurring rides.

Customers, Driver partners would have an android based smartphone. Google has all the information available to give a cab-sharing app like a social platform. If Google is going to monetize for sharing traffic details, congestion then it will also get significant revenue for the provider

Autonomous vehicles - Robo taxis is a distant dream for our country. If such a thing happens I am afraid about an alternate career for driver partners. Change is the only permanent thing that never changes

Updated May 28/2020

In an end-to-end IoT-enabled transportation ecosystem, the information would flow seamlessly throughout the network creating an information value loop. Source @DeloitteInsight Link https://t.co/vPV62egjU0 via @antgrasso @antgrasso_IT #IoT #IIoT #ecosystem #DigitalStrategy pic.twitter.com/cqUC7TOYYB

— Tech to Specialists (@Tech2Specialist) May 28, 2020

I have tried to outline certain data stories I observed using Taxi Booking apps. Your comments and feedback welcome!!!.