Distance Matrix API: How NextBillion.ai Boosted Duke University’s Route Optimization Application and Lowered Costs

About the Company

Duke University, a prestigious private research university located in Durham, North Carolina, was developing a routing utility web app to optimize paths with multiple stops. The app’s goal was to determine the best order for visiting locations, helping users save time and travel costs.

The Problem

The routing app Duke was developing required robust APIs to manage a large number of destinations and efficiently calculate travel distances. However, their existing API had the following limitations:

• Limited Array Size: The API they previously used only supported ~25 destinations and versions, so they wanted an alternative that supported a more significant number of destinations.
• High Costs: Their previous provider’s pricing was too high compared to the scale of work being implemented, so they were looking for an alternative with a more reasonable pricing structure.

The Solution

NextBillion.ai’s Distance Matrix API aligned perfectly with the client’s needs. Here are a couple of key features that made the difference:

• High-Order Volume Processing: With the Distance Matrix API’s 5000 * 5000 matrix size, you can easily add thousands of origin and destination points within a single API request to estimate transit distances and durations.
• Predictable and Fair Operational Costs: With NextBillion.ai, Duke University could keep costs from rising steeply as the order volume and API calls went up. The Distance Matrix API’s pay-as-you-go pricing model also allowed the client to get the fastest and shortest estimates. The pricing plan also provided the client with estimates for future trips and estimates without tolls, highways, and ferries.
• Vehicle Constraints: With the Distance Matrix API, the client could plan routes with 50+ multi-dimensional capacity constraints, specifically to optimize the travel cost, travel time, and distance.
• Pre-Processed Map Data: The Distance Matrix API also allowed the client to calculate ETAs and distances between thousands of locations in just a few seconds with pre-processed maps.
• Browse API: Additionally, NextBillion.ai’s Browse API (a component of NextBillion.ai’s Places API) allowed the client’s web application to look up places based on different filters such as name or categories, ranked by the distance from a given search center. With a limit of 2400 queries per minute, NextBillion.ai’s Places API was the right choice to replace the Places API the client used previously.

The Outcome

Transitioning to NextBillion.ai’s Distance Matrix API brought significant improvements for Duke University. The solution offered greater control over operational expenses and considerably faster API response times, allowing them to manage their route optimization needs more effectively. Additionally, NextBillion.ai’s predictive ETA modeling, using real-time and historical traffic data, provided highly accurate arrival time estimates, further enhancing the overall efficiency of their operations.