Getting from A to B with shared rides can be quite a hassle. It involves a fair bit of planning and often cars are shabby and unsafe. Drivers and fellow passengers can be equally unreliable. We believe this puts people off ride sharing, even though they could save money and reduce their carbon emissions.
Ridealong is our attempt to make ride sharing a better and more popular means of transport.
What it does
Ridealong offers a seamless carpooling experience with high quality rented cars, reliable drivers and smart context-based pricing.
As a hybrid between rental cars, car sharing and ride sharing, it matches cars, drivers and passengers going to the same destination. Riders can either join an existing ride or be the driver for a new ride—all vehicles come from rental car companies or car sharing providers.
After booking, riders can track their arriving ride on the live map. Everybody benefits from a trusted platform and the flexibility of rental cars.
How we built it
Our backend is written in Ruby and hosted as a container on Azure. We aggregate available cars from Sixt and various car sharing providers and rank them based on the user's context, ie, their location, their preferences and current as well as past demand.
We built our progressive web app with React and Redux. It displays available cars and rental car stations on a live map and displays available ride options with live demand-based pricing.
We also use data to estimate future demand, suggest better ride options and routes, and give users tips on the best time to ride.
Challenges we ran into
Some car sharing providers do not provide fully supported public APIs. We also found that some rental car APIs had a long response time. Eventually, we overcame both challenges by parallelising requests and caching data.
Initially, we planned on highlighting the approaching ride through augmented reality. However, since AR in mobile browsers is quite a novel technology, we ran into time constraints while implementing it.
Accomplishments that we're proud of
We are rather proud of our progressive web app, which provides the user experience of a native app. We also think our backend does an excellent job of aggregating ride options and real-time pricing.
What's next for Ridealong
We created a scalable platform that is full of growth opportunities. Our next step would be the integration of additional car sharing providers and other transport options. We would also employ machine learning to improve ride recommendations, routes (we use an API at the moment) and demand prediction. We did not have enough time to implement AR tracking, but it is high up on our feature list.
We believe that Ridealong can stay relevant, all the way towards an eventually autonomous future of mobility.