1. How do you calculate distance and time?
Distance is calculated based on the route taken between two points (Point A to Point B), using the selected mode of travel — driving, walking, biking, or trucking.
Time is determined by dividing each route segment’s length by its average speed, then adjusting for traffic. For example:
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Segments 1 & 2 → Urban streets
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Segments 3 & 4 → Major roads or highways
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Segment 5 → City/local roads
Each segment has a unique speed profile.
Time = Σ (Segment Length ÷ Segment Speed × Traffic Factor)
2. Does the distance matrix always minimize distance by default?
Yes — by default, our distance matrix computes the shortest path between two points, given all travel constraints.
3. What is a distance matrix?
A distance matrix is a 2D grid where each cell (i, j) represents the distance (or travel time) from Point Ai to Point Aj.
For a set of n locations, the matrix contains n x n values.
5. What makes your APIs different from traditional mapping services?
Beans.ai is purpose-built for enterprise use:
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Supports roadblocks, geo-fencing, and polygon-based slowdowns
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Custom speed zones and closures
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More flexible caching & pricing models suited for large-scale use
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Designed to work with your operational constraints — not against them
6. How often is crowdsourced data updated?
If customers are actively marking closures or adjusting routes, we can update the network:
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Daily or weekly (based on configuration)
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For smaller geographies (e.g., countries in Europe), we can support hourly updates
7. Can customers flag special events like marathons or road closures?
Yes. Customers can:
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Draw custom polygons for events (e.g., F1 races, parades, construction)
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Set speed reductions (e.g., reduce to 10%)
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Create reusable templates (e.g., Sunday routes, Monday routes, etc.)
8. What’s the difference between Real-Time and Predictive Traffic?
|
Traffic Type |
Use Case |
Notes |
|---|---|---|
|
Predictive |
Used during planning (e.g., morning manifests) |
Based on meta-patterns from historical data — e.g., school zones at 3PM |
|
Real-Time |
Used during re-optimization |
Based on live congestion and incidents |
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Real-Time traffic is available immediately
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Predictive traffic requires a ~2–3 week learning period per metro area
In practice:
🕘 Planning uses Predictive
🔁 Replanning uses Real-Time
Our system is designed to blend predictive and real-time data, ensuring more accurate scheduling — especially in time-sensitive industries like patient care.