Deploy Fleet Intelligence Solution for Taxis
🚖 Track. Analyze. Optimize. Build a real-time taxi fleet control center with AI-powered insights - powered by OpenRouteService in Snowflake.
Overview
Build a fully interactive Fleet Intelligence Control Center using the OpenRouteService Native App.
This quickstart deploys a multi-page Streamlit application that simulates a taxi fleet operations dashboard. Track individual drivers, visualize routes in real-time, and analyze fleet density with interactive heat maps - all powered by Snowflake's geospatial capabilities.
What You'll Build
🚖 Fleet Intelligence Control Center - A multi-page Streamlit dashboard that:
- Tracks individual driver journeys with route visualization
- Provides AI-powered trip analysis using Snowflake Cortex
- Displays fleet density heat maps with H3 hexagon visualization
- Shows driver performance metrics including speed and distance analytics
📊 Fleet Analytics - Real-time insights including:
- Driver route tracking with pickup/dropoff locations
- Time-based filtering to analyze trips by hour
- Speed distribution analysis across the fleet
- Interactive maps with pydeck visualization
Prerequisites
IMPORTANT: This demo requires the OpenRouteService Native App to be installed and running. If you haven't built it yet, complete the Build Routing Solution in Snowflake quickstart first.
Required:
- OpenRouteService Native App deployed and activated
- Cortex Code CLI installed and configured
- Active Snowflake connection with ACCOUNTADMIN access
- Overture Maps Places and Addresses datasets from Snowflake Marketplace
What You'll Learn
- Deploy fleet analytics dashboards using Cortex Code skills
- Work with Carto Overture Maps datasets for realistic location data
- Use Snowflake Cortex AI for trip summaries and analysis
- Build multi-layer geospatial visualizations with Pydeck
- Create H3 hexagon heat maps for density analysis
- Track driver states (waiting, pickup, driving, dropoff, idle)
Deploy the Fleet Intelligence Solution
Use Cortex Code to deploy the Fleet Intelligence solution including database setup, data generation, and the Streamlit dashboard.
Clone Repository and Deploy Skill
Clone the repository:
git clone https://github.com/Snowflake-Labs/sfguide-create-a-route-optimisation-and-vehicle-route-plan-simulator cd sfguide-create-a-route-optimisation-and-vehicle-route-plan-simulator
In the Cortex Code CLI, type:
$deploy-fleet-intelligence-taxis
NOTE: The skill will first verify that the OpenRouteService Native App is installed. If it's not found, it will provide instructions to install it first.
The skill uses interactive prompting to gather required information:
- Number of drivers: 20 to 500+ (default: 80)
- Number of days: 1 to 30+ (default: 1)
- City: San Francisco (default), New York, London, Paris, Chicago, or any custom city
Cortex Code will automatically:
- Detect ORS Configuration - Reads the current OpenRouteService map region and enabled routing profiles, then recommends a matching city. If the selected city doesn't match the configured region, it will guide you through changing the map
- Verify OpenRouteService Native App is installed and running
- Create Schema - Sets up
OPENROUTESERVICE_SETUP.FLEET_INTELLIGENCE_TAXISwith required objects - Generate Sample Data - Creates drivers, trips, and routes using ORS
- Deploy Dashboard - Creates the Taxi Control Center Streamlit app
What Gets Installed
The deploy skill creates the following Snowflake objects:
Marketplace Data
| Component | Name | Description |
|---|---|---|
| Database | OVERTURE_MAPS__PLACES | Carto Overture Maps Places with POI data |
| Database | OVERTURE_MAPS__ADDRESSES | Carto Overture Maps Addresses |
Fleet Intelligence Database
| Component | Name | Description |
|---|---|---|
| Database | OPENROUTESERVICE_SETUP | Main setup database |
| Schema | OPENROUTESERVICE_SETUP.FLEET_INTELLIGENCE_TAXIS | Core data tables and views |
| Warehouse | ROUTING_ANALYTICS | Compute warehouse, XSMALL (auto-suspend 60s) |
| Stage | STREAMLIT_STAGE | Stage for Streamlit files |
Data Tables
| Table | Description |
|---|---|
TAXI_LOCATIONS | POIs and addresses from Overture Maps for the target city |
TAXI_LOCATIONS_NUMBERED | Indexed location pool for deterministic trip assignment |
TAXI_DRIVERS | Configured drivers with shift assignments |
DRIVER_TRIPS | Trip assignments per driver |
DRIVER_TRIPS_WITH_COORDS | Trips with pickup/dropoff coordinates |
DRIVER_ROUTES | Raw ORS route responses |
DRIVER_ROUTES_PARSED | Parsed route geometries and distances |
DRIVER_ROUTE_GEOMETRIES | Routes with timing data |
DRIVER_LOCATIONS | Interpolated GPS positions with driver states |
Analytics Views
| View | Description |
|---|---|
DRIVER_LOCATIONS_V | Location points with LON/LAT and state |
TRIPS_ASSIGNED_TO_DRIVERS | Trip routes with geometry |
ROUTE_NAMES | Human-readable route descriptions |
TRIP_ROUTE_PLAN | Route data for Heat Map page |
TRIP_SUMMARY | Trip statistics and speed metrics |
Streamlit Application
| Component | Name | Description |
|---|---|---|
| Streamlit | TAXI_CONTROL_CENTER | Multi-page fleet dashboard |
Explore the Control Center
Once deployment completes, navigate to the Fleet Intelligence Control Center:
- Go to Projects > Streamlits in Snowsight
- Click on Taxi Control Center
Main Dashboard
The main page shows fleet overview statistics:
- Total Trips - Number of trips in the simulation
- Active Drivers - Drivers with assigned trips
- Total Distance - Combined distance driven across all routes
- Location Points - Number of location data points in the simulation
Driver Routes Page
Track individual driver journeys:
-
Select a Driver from the sidebar dropdown
-
Choose a Trip to visualize the route
-
View the Map with:
- Route geometry (orange line)
- Pickup and dropoff locations (blue markers)
- Current driver position (dark marker) controlled by a time slider
-
Analyze Trip Details:
- Total distance traveled
- Trip duration
- Average speed
- AI-generated trip summary using Cortex
NOTE: The AI trip analysis feature uses Snowflake Cortex AI (by default
claude-3-5-sonnet). Other LLM models available in your account can also be used. Ensure Cortex AI is enabled in your Snowflake region.
Fleet Heat Map Page
This page combines fleet statistics with an interactive density map.
Top Section - Fleet Statistics:
- Top Streets - Most popular pickup and dropoff locations
- Route Distances - Shortest and longest routes across all drivers
Map Section - Driver Density:
Use the sidebar controls to explore fleet distribution:
- Time Slice - Select both Hour (0-23) and Minute (0-59) to see each driver's latest position at that moment
- Show Driver Locations - Toggle clickable dots showing individual driver positions. Click a dot to display its route on the map
- H3 Resolution - Adjust hexagon granularity (6-9) for density aggregation
- Colour Scheme - Choose between "Contrast" and "Snowflake" palettes
Analyze Patterns:
- Peak hour driver concentrations via H3 hexagons
- Popular pickup/dropoff zones from the Top Streets charts
- Coverage gaps in the fleet
Understanding the Data Model
Driver States
The simulation tracks realistic driver states throughout each trip:
| Point Index | State | Speed | Description |
|---|---|---|---|
| 0 | waiting | 0 km/h | Waiting for fare (2-8 min before trip) |
| 1 | pickup | 0 km/h | Passenger boarding |
| 2-12 | driving | Variable | En route with traffic simulation |
| 13 | dropoff | 0-3 km/h | Slowing for passenger exit |
| 14 | idle | 0 km/h | Brief idle after dropoff |
Speed Distribution
Realistic traffic patterns are simulated:
| Speed Band | Percentage | Description |
|---|---|---|
| 0 km/h (Stationary) | ~23% | Waiting, pickup, dropoff, idle |
| 1-5 km/h (Crawling) | ~11% | Traffic jams, red lights |
| 6-15 km/h (Slow) | ~14% | Heavy traffic |
| 16-30 km/h (Moderate) | ~26% | Normal city driving |
| 31-45 km/h (Normal) | ~20% | Clear roads |
| 46+ km/h (Fast) | ~6% | Late night, highways |
Shift Patterns
Drivers are distributed across 5 shifts for 24-hour coverage:
| Shift | Hours | % of Fleet | Coverage |
|---|---|---|---|
| Graveyard | 22:00-06:00 | 10% | Overnight |
| Early | 04:00-12:00 | 22.5% | Morning rush start |
| Morning | 06:00-14:00 | 27.5% | Full morning rush |
| Day | 11:00-19:00 | 22.5% | Midday + evening start |
| Evening | 15:00-23:00 | 17.5% | Evening rush |
Customize the Solution
Change Number of Drivers
Edit the driver count in the shift patterns:
-- Default: 80 drivers total SELECT 1 AS shift_id, 'Graveyard' AS shift_name, 22 AS shift_start, 6 AS shift_end, 8 AS driver_count UNION ALL SELECT 2, 'Early', 4, 12, 18 UNION ALL SELECT 3, 'Morning', 6, 14, 22 UNION ALL SELECT 4, 'Day', 11, 19, 18 UNION ALL SELECT 5, 'Evening', 15, 23, 14
Change Location or Map
In the Cortex Code CLI, type:
$customize-main
NOTE: See the Build Routing Solution in Snowflake quickstart for more content about location customization.
Scaling Recommendations
| Drivers | Days | Est. Rows | Warehouse | Est. Time |
|---|---|---|---|---|
| 20 | 1 | ~4K | SMALL | 2-3 min |
| 80 | 1 | ~18K | MEDIUM | 5-8 min |
| 80 | 7 | ~125K | LARGE | 20-30 min |
| 200 | 1 | ~45K | LARGE | 15-20 min |
| 200 | 7 | ~315K | XLARGE | 45-60 min |
| 500 | 7 | ~800K | XLARGE | 2-3 hours |
Uninstall the Solution
To remove the Fleet Intelligence solution execute:
DROP SCHEMA OPENROUTESERVICE_SETUP.FLEET_INTELLIGENCE_TAXIS;
This will remove the OPENROUTESERVICE_SETUP.FLEET_INTELLIGENCE_TAXIS schema and its contents.
NOTE: The OpenRouteService Native App remains installed. You can uninstall it separately.
Available Cortex Code Skills
| Skill | Description | Command |
|---|---|---|
deploy-fleet-intelligence-taxis | Deploy the full solution (data generation, routes, and Streamlit app) | $deploy fleet intelligence dashboard$ |
Conclusion and Resources
Conclusion
You've deployed a complete Fleet Intelligence Control Center that demonstrates:
- OpenRouteService Native App - Real road-following route generation
- Snowflake Cortex AI - AI-powered trip summaries and analysis
- Pydeck Visualization - Interactive maps with multiple layer types
- H3 Hexagons - Spatial aggregation for density analysis
What You Learned
- Deploy fleet analytics solutions using Cortex Code skills
- Generate simulated taxi fleet data with realistic patterns
- Track driver states and positions over time
- Build heat maps with H3 hexagon visualization
- Use AI for trip analysis and fleet insights
Related Quickstarts
- Build Routing Solution in Snowflake with Cortex Code - Build and customize the routing solution (prerequisite)
- Deploy Route Optimization Demo - Build a visual route optimization simulator (prerequisite)
- Retail Catchment Analysis with Overture Maps - Build an interactive retail catchment analysis tool using real-world POI data - powered by OpenRouteService in Snowflake
- Deploy Snowflake Intelligence Routing Agent - Build an AI-powered route planning assistant that understands natural language locations - powered by OpenRouteService and Snowflake Intelligence
Source Code
- Source Code on GitHub - Skills, scripts, and Streamlit apps
OpenRouteService Resources
- OpenRouteService Official Website - Documentation and API reference
- VROOM Project - Vehicle Routing Open-source Optimization Machine
Cortex Code & Snowflake
- Snowflake Cortex - AI-powered features in Snowflake
- Carto Overture Maps - POI and address data
- H3 in Snowflake - Hexagonal hierarchical geospatial indexing
This content is provided as is, and is not maintained on an ongoing basis. It may be out of date with current Snowflake instances
