Ride-Share Company Enables Faster, More Accurate Routing for Multiple Drop-Offs

About the Company

The customer is an on-demand ride-sharing service that connects office commuters with other like-minded professionals traveling on the same route and at the same time. The service helps its customers by converting a normal office commute into a carpool trip. The key differentiator is innovative technology that helps drivers discover, connect, match routes, coordinate and share costs in a seamless manner.

The Problem

The customer’s application relied on an algorithm to compute the fastest routes between any given origin and destination points. The application also enabled users to choose an alternative route if they preferred. To allow for customer selection of routes, the company had to leverage Google’s routing platform; however, these routes would not be in sync with the customer’s underlying application algorithm.

The primary pain point for the customer was to correctly map the entry and exit points of building complexes, such as residential societies, shopping malls and IT parks. With the existing solution, the drivers were routed to pickup and drop-off points that were incorrectly marked in the outer parameters of the building complexes, which forced the drivers to manual reroute to the correct location points, resulting in time and fuel wastage.

Another pain point for the customer was in using a navigation system that was unable to process polylines as input and provide turn-by-turn directions for the driver.

Our Solution:

Since the need of the hour was for a flexible, cost-effective and accurate routing solution, the customer decided to evaluate NextBillion.ai’s Snap-to-Road, Distance Matrix and Directions APIs, and assess how they would work with their existing algorithm and data. The customer also wanted to assess whether NextBillion.ai’s APIs could scale as required and replace their existing platform.

They tested the following:

1. Snap-to-Road API – To help track vehicles by converting GPS point data, which comes in the format of latitude and longitude geographical coordinates, and returning them as a list of objects that form a route. The customer felt this was the primary API that needed evaluation, and wanted to test if the application was able to track drivers during an assigned trip. The customer was happy with the evaluation results, and integrated NextBillion.ai’s Snap-to-Road API to their existing map stack.
2. Distance Matrix API – To help calculate the fastest routes between multiple origins and destinations. Using NextBillion.ai’s large Distance Matrix API, the routes generated were in sync with the customer’s algorithm, and that helped them seamlessly scale their operations by adding more drivers to their network.
3. Directions API – To display clear directions for reaching a destination. Since NextBillion.ai’s Directions API is platform-agnostic, the customer had no trouble integrating it into their existing map stack.

The Outcome

On evaluating NextBillion.ai’s capabilities with their existing map stack, the customer found NextBillion.ai’s solutions to be perfect for addressing their unique use case. Importantly, they were able to improve their overall customer experience by enabling accurate pickup and dropoff points, while reducing trip times and fuel costs.

For the next phase of evaluation, the customer is now actively testing NextBillion.ai’s Navigation SDK, which addresses the pain point of enabling polylines as an input.

Key Takeaways:

• Helped the customer scale operations by fixing inaccurate pickup and drop-off points for building complexes, enabling addition of multiple unplanned stops in an already assigned route and also more drivers to their network
• Provided a dedicated support team to simplify the evaluation and integration processes 
• Enabled significant cost savings with better performance and easy integration