Case Study: How This Data-Powered Ride-Sharing Startup Is Revolutionising Bengaluru Traffic
What is the solution to the choked roads and bumper-to-bumper traffic in a city like Bengaluru? Thanks to emerging technology like artificial intelligence and data analytics, ride-sharing apps and services are aiming to solve this problem efficiently. Take the example of Quick Ride. This is an app-based ridesharing platform that allows vehicle owners, to list their vacant seats and offer rides to people commuting on the same route. Both the parties, the ride-giver and the ride taker, give their respective details on the app with respect to route, timing etc. The ride giver gets points in exchange from each person who joins the ride and the points can be redeemed for fuel purpose across any fuel stations. Before we proceed further, let us spend a minute on the illustration below: We are sure that these images will trigger something dark and deep within the daily commuter in Bengaluru. That is why the work done by apps like Quick Ride is significant. Let us understand how: Cost Effective: The inflation of our country is increasing every year (roughly 7 % current rate), which means the value of the money keeps depreciating every year. Such ride-pooling apps provide common people with a very simple solution to solve the daily commutation problem through an economical and effective way. In a normal scenario, Quick Ride reduces daily travel costs by nearly 50% compared to a cab aggregator. Socialising: Since it is a community of people who are involved in giving and taking rides, social bonding tends to develop over a period of time. People from different organisations get an opportunity to interact which also leads to knowledge sharing and overall productive conversation. In a city like Bangalore which is over-boiled with the menace of traffic, Quick Ride comes as a saviour on how an individual can meaningfully use the travel time. Time optimisation: We see a lot of social media posts where individuals blast out at app-based cab aggregators for the driver’s behaviour or refusal to come to pick-up or go to drop location; taking a lot of mental strain on the individual. The advanced analytics capability and ride-matching algorithm of the application ensure that people travelling in similar routes are displayed. This ensures the no redundant time is wasted for either of the parties involved. Flexibility: Both the ride taker and giver are flexible to experimenting their journey towards finding the perfect travel companions. Through features such as daily booking, recurring booking, percentage ride match and type of vehicle (two or four-wheeler), these ride-sharing apps ensure that both the parties are in a win-win situation. Safety: QuickRide does an organisational verification cum validation process where the users have to verify their work email IDs on the QuikRide platform. The application also designs a profile for all users based on past ride experience, peer rating, feedback and other historical information which provides a holistic view of the individual. Evolution Like other startups, even Quick Ride experienced challenges through-out its journey ranging from legal problems of the app, fixing bugs and improving its interface and mapping algorithm and most importantly -its wide-scale adoption. But because they had a good vision of its own and were on a track to solve the traffic menace in the Bengaluru, it kept striving forward and today it has over 10,000 registered users in Bengaluru.
What is the solution to the choked roads and bumper-to-bumper traffic in a city like Bengaluru? Thanks to emerging technology like artificial intelligence and data analytics, ride-sharing apps and services are aiming to solve this problem efficiently. Take the example of Quick Ride. This is an app-based ridesharing platform that allows vehicle owners, to list […]
What is the solution to the choked roads and bumper-to-bumper traffic in a city like Bengaluru? Thanks to emerging tech like AI and data analytics, ride-sharing services are aiming to solve this problem efficiently.
["AIM Writers Programme", "data mapping", "Ola", "Uber"]
["Go", "Ola", "artificial intelligence", "programming_languages:R", "AI", "AIM Writers Programme", "programming_languages:Go", "GAN", "Aim", "analytics", "Uber", "data mapping", "R", "startup"]
["AI", "artificial intelligence", "analytics", "Aim", "R", "Go", "GAN", "startup", "programming_languages:R", "programming_languages:Go"]
Krishanu is a part of the AIM Writers Programme. He has done his MBA from Institute of Management, Christ University in Marketing. After that, he has worked in Marketing and Business development spanning across Market Intelligence, Competitive Intelligence, Account Management, Sales Enablement, Campaign Management and various aspects of Digital Marketing.