FLO (Field-based Traffic Model)

Overview

FLO models traffic as continuous vector fields (curb/median repulsion, goal attraction, inter-vehicle discomfort) combined with bicycle-model vehicles. Local interactions produce global structure: lanes and predictable flows without explicit lane rules.

Key Results

• Lane Formation Index: 1.000 (Following) - perfect self-organization
• Trajectory Entropy: 0.174 (Chaotic) - low unpredictability; structure emerges from conflict
• Stoppage Frequency: 97.8% (Merging) - realistic bottleneck behavior

These metrics show: emergence works, traffic is not random, and the model reproduces realistic congestion.

Visualization

• Real-time plots and ffmpeg-backed animations (matplotlib + FuncAnimation)
• Field visualization (quiver), vehicle trajectories, and per-frame snapshots for presentations

Implementation Details

• Core: Python (NumPy, SciPy)
• Simulation: custom continuum field + bicycle vehicle controller
• Visualization: Matplotlib (animation to MP4)
• Experiment scripts: scenario definitions, metric logging, and plot generation

Future Work

• Implement Fast Sweeping (O(m)) to replace FMM for linear-time field solve
• GPU acceleration for large-scale, real-time simulations
• Validate against real-world trajectory datasets (e.g., NGSIM)
• Add curved roads, intersections, traffic lights, and richer driver models

p.s this is my fyp, marked in highest in the entire cs batch in FYP-1 evaluation. :3