Ubercomb - Uber x Honeybees

Ever wondered how Uber sets its ride prices on-the-fly?

Imagine a city buzzing like a beehive, with riders and drivers moving like bees, and Uber trying to ensure every rider gets a ride without waiting too long, and at the right price.

Why is this such a big deal for Uber?

• Huge Scale: Uber operates in numerous cities worldwide, each with its unique geography, traffic patterns, and user behavior. Analyzing geospatial data at such a massive scale in real-time is inherently challenging.
• Dynamic Nature: The demand and supply of rides change constantly. Events, weather conditions, time of day, and even day of the week can drastically alter ride requests.

So, the question became, how do we partition the entire Earth's surface into a large spatial dataset that can help Uber to precisely locate riders on time and provide best pricing.

Think of honeybees and their perfect hexagonal honeycombs. Just as bees efficiently store honey in these hexagons, Uber uses a similar hexagonal pattern to understand city movement.

Interesting, right?

To solve this problem, we would want to visualize the entire earth partitioned into multiple regions but what is the best way to do so?

We could use multiple shapes and build a grid system that optimises the mapping of riders and drivers. Further, price the ride based on the demand and supply within that region.

So, what shapes should we use to build this grid?

Conventional grid systems used squares or triangles but they introduce significant errors because they have two and three different distances between their centerpoint and their neighbors' respectively.

With people often moving from one region to another or from one cell(square for example) to another, the non-uniform distance between the center of the square to its boundaries adds to the inconsistencies when analysing distances between rider and driver.

Hence, there's scope for improvement in those grid systems to get rid of the quantization error.

Which shape could resolve this problem?

The answer came from the hexagonal honeycomb structures that honeybees use to efficiently store honey and pollen while using least amount of wax to build their hives.

Why Hexagons?

Hexagons have uniform distances from their centers to their boundaries or their neighbors, thus providing consistent distance that simplifies the analysis and better predicts the moving patterns of drivers.

Ubercomb - How did Uber leverage these advantages?

Just as bees use hexagons to efficiently store honey and pollen, Uber overlays cities with a hexagonal grid (akin to a honeycomb) to analyze and manage its operations. Each hexagonal cell in Uber's "urban honeycomb" represents a specific area of the city.

Bees visit flowers to collect nectar, and the amount of nectar varies from flower to flower. Similarly, different hexagons in a city have varying numbers of ride requests. Some might be buzzing with activity (like a flower rich in nectar), while others might be quieter.

Bees store nectar in the honeycomb and convert it to honey. If a particular cell fills up quickly, it indicates a rich source of nectar. In Uber's case, if a hexagonal cell has a high number of ride requests compared to available drivers, it indicates high demand. Bees might prioritize a rich nectar source, Uber introduces dynamic pricing in high-demand areas, incentivizing more drivers to head there.

Worker bees might move to cells that are filling up with nectar faster. Analogously, seeing the potential for higher earnings in a surge-priced hexagon, Uber drivers might move to that area, balancing out the demand-supply ratio.

Uber's H3 Indexing System

A hexagonal hierarchical spatial index. Uber combined hexagonal global grid system with a hierarchical indexing system.

How does H3 work?

Imagine a city divided into numerous hexagonal cells using the H3 system.

1. Hexagonal Grid Creation: The city is overlaid with the H3 hexagonal grid. Each hexagon has a unique identifier. Let's take a small region with 9 hexagons in a 3x3 grid.

2. Real-time Data Collection: Uber collects real-time data on ride requests and available drivers within each hexagon. Think of it this way: there are 10 ride requests but only 2 drivers available in hexagon A. On the other hand, there are 2 rides and 5 available drivers in hexagon B.

3. Demand-Supply Analysis: Uber analyzes the ratio of ride requests to available drivers in each hexagon. A has high demand (10 requests) and low supply (2 drivers), indicating demand-supply imbalance. Hexagon B has balanced ratio.

4. Dynamic Pricing Calculation: Based on the demand-supply analysis, Uber adjusts the pricing. Due to the high demand and low supply in Hexagon A, riders might see a surge price, making it more lucrative for drivers to head to that area. In Hexagon B, with a balanced ratio, standard pricing might apply.

5. Feedback Loop for Driver Dispatch: The dynamic pricing not only affects riders but also provides incentives for drivers. Higher prices in a particular hexagon can attract more drivers to that area, helping to balance the demand-supply ratio. Seeing the surge pricing in Hexagon A, drivers from neighboring hexagons might move to Hexagon A to benefit from the higher prices.

6. Continuous Adjustment: As drivers move and rider requests change, the demand-supply ratio in each hexagon will fluctuate. H3 continuously analyzes this data to adjust pricing dynamically. As more drivers enter Hexagon A and fulfill ride requests, the demand-supply imbalance reduces, and the surge pricing might decrease or return to standard rates.

Conclusion

Just as bees have perfected the art of storage and resource management with their hexagonal honeycombs, Uber uses its H3 hexagonal grid system to optimize ride dispatching and pricing in real-time. Nature's efficiency inspires modern technological solutions!

PS: H3 indexing system is open source and available on GitHub, it's written in C but bindings are available for other languages.