Traffic Model

Overview

The Traffic Model provides network-level traffic simulation capabilities for transport and logistics systems. It enables users to model vehicle flow through complex road networks, analyze congestion patterns, calculate optimal routes, and predict traffic behavior under different conditions.

This model is designed for urban planning teams, logistics operators, and transportation analysts who need to understand and optimize traffic flow at the network level.

Key Capabilities

Road Network Modeling

• Network Generation — Generate road graphs from geographic data
• Network Topology — Model intersections, road segments, and connectivity
• Capacity Modeling — Define road capacity and traffic constraints
• Multi-Layer Networks — Support for complex network structures

Traffic Simulation

• Flow Simulation — Model vehicle movement through road networks
• Congestion Modeling — Simulate traffic congestion and propagation
• Dynamic Traffic States — Track changing traffic conditions over time
• Network-Level Interactions — Capture how congestion in one area affects connected areas

Route Optimization

• Shortest Path — Calculate optimal routes between locations
• Multi-Criteria Routing — Optimize for time, distance, or other metrics
• Dynamic Routing — Adjust routes based on current traffic conditions
• Route Comparison — Evaluate multiple routing strategies

Use Cases

Urban Transportation Planning

Scenario: A city planning department needs to evaluate the impact of adding a new road or changing traffic signal timing.

Workflow:

1. Load current road network topology
2. Run baseline traffic simulation
3. Modify network (add roads, change signals)
4. Run simulation with proposed changes
5. Compare results to understand impact

Value: Make infrastructure decisions based on predicted traffic flow rather than trial and error.

Logistics Route Optimization

Scenario: A delivery company wants to optimize vehicle routes to minimize travel time and fuel costs.

Workflow:

1. Import delivery locations and time windows
2. Generate route options for each vehicle
3. Simulate traffic conditions for different departure times
4. Select optimal routes and timing
5. Update routes dynamically based on real-time traffic

Value: Reduce delivery costs and improve on-time performance.

Autonomous Vehicle Testing

Scenario: An AV development team needs to test vehicle behavior in various traffic scenarios.

Workflow:

1. Define test scenarios (congestion levels, road types)
2. Simulate realistic traffic patterns
3. Inject AV agents into simulation
4. Monitor AV behavior and decision-making
5. Analyze performance across scenarios

Value: Test AV algorithms in diverse, repeatable traffic conditions without real-world risk.

Public Transport Optimization

Scenario: A transit authority wants to optimize bus routes and schedules to reduce congestion and improve service.

Workflow:

1. Model existing bus routes and schedules
2. Simulate passenger flow and vehicle movement
3. Test alternative route configurations
4. Evaluate impact on network congestion
5. Implement optimized routes

Value: Improve public transport efficiency while reducing overall network congestion.

Model Inputs

The Traffic Model accepts:

• Road Network Data — Network topology, road characteristics, capacity constraints
• Origin-Destination Matrices — Travel demand between locations
• Traffic Parameters — Vehicle types, speed limits, traffic rules
• Simulation Parameters — Time windows, time steps, convergence criteria

Model Outputs

The model produces:

• Route Solutions — Optimal paths through the network
• Traffic Flow Data — Vehicle counts, speeds, and densities over time
• Congestion Metrics — Bottleneck locations, delay estimates, level of service
• Network Performance — Overall network utilization and efficiency metrics
• Visualization Data — Time-series data for traffic animation and analysis

Configuration Options

Key parameters you can configure:

• Simulation Duration — How long to simulate (minutes, hours, days)
• Time Granularity — Simulation time step resolution
• Routing Algorithm — Choice of pathfinding method
• Congestion Models — How to model speed-flow relationships
• Vehicle Types — Car, truck, bus with different characteristics

Integration with Other Models

The Traffic Model works well with:

• Schedule Generation — Create optimized schedules that account for traffic patterns
• Data Ingestion — Pull real-time traffic data to calibrate simulations
• TomTom API — Enrich simulations with real-world traffic and map data
• AI Agents — Enable autonomous decision-making based on traffic predictions

Performance Notes

• Network Size — Performance scales with network complexity (nodes and edges)
• Simulation Length — Longer simulations require more computation time
• Use Caching — Enable caching when running multiple experiments with the same network
• Parallel Scenarios — Run multiple what-if scenarios in parallel to save time

Getting Started

Basic Workflow

1. Prepare Network Data — Define or import your road network
2. Configure Simulation — Set parameters for your analysis
3. Add to Workflow — Drag Traffic Model into your workflow canvas
4. Connect Data — Link network data and parameters
5. Run Simulation — Execute and analyze results

Example: Simple Route Optimization

[Load Network Data] → [Traffic Model] → [Analyze Routes]

This workflow loads network topology, calculates optimal routes, and outputs route recommendations.

Example: Congestion Impact Analysis

[Baseline Simulation] → [Modify Network] → [Updated Simulation] → [Compare Results]

This workflow compares traffic flow before and after network changes to quantify impact.

Next Steps

• Build a workflow: Building and Configuring Workflows
• Understand orchestration: Workflow Execution Manager
• Explore other models: Modelling Library