Urban Transportation Analysis

Research and implementation of transportation analytics for Lahore and Riyadh utilizing deep learning and real-time data processing.

Project Structure

• Lahore Traffic Monitoring: Spatio-temporal prediction and route optimization.
• Riyadh Transportation Analysis: Graph-based multi-modal system integration.

Quick Start

Infrastructure

docker compose up -d

Verification

export PYTHONPATH=$PYTHONPATH:$(pwd)
source venv/bin/activate
python shared/utils/verify_infra.py

Development Progress

Day 1: Infrastructure and Data Ingestion

Achievements:

• Configured Python 3.12 environment with specialized geospatial dependencies.
• Deployed PostgreSQL (PostGIS), Redis, and Kafka infrastructure via Docker.
• Extracted and processed 145,998 road nodes and 380,264 edges for the Lahore District.

Commands Executed:

# 1. Start Infrastructure
docker compose up -d

# 2. Enable PostGIS Extension
docker exec lahore_postgres psql -U traffic_user -d lahore_traffic -c "CREATE EXTENSION IF NOT EXISTS postgis;"

# 3. Verify Connectivity
export PYTHONPATH=$PYTHONPATH:$(pwd)
source venv/bin/activate
python shared/utils/verify_infra.py

# 4. Run Data Ingestion
python lahore/src/data_pipeline/ingestion.py

Verification Output:

INFO - PostgreSQL connection successful!
INFO - Redis connection successful!
INFO - Kafka connection successful!
INFO - All infrastructure components are online and reachable!

Day 2: Network Graph and Feature Engineering

Achievements:

• Constructed a hierarchical networkx.MultiDiGraph representing the Lahore road network.
• Developed a feature extraction pipeline for spatial road attributes (length, density, hierarchy).
• Validated 100% geometry integrity and topological connectivity.

Commands Executed:

# 1. Build Network Graph
python lahore/src/data_pipeline/graph.py

# 2. Extract Features
python lahore/src/data_pipeline/features.py

# 3. Validate Data Quality
python lahore/src/data_pipeline/validation.py

Verification Output:

INFO - Graph constructed: 145998 nodes, 380264 edges.
INFO - All geometries are spatially valid.
INFO - Data quality validation complete!

Day 3: Simulation and Deep Learning Architecture

Achievements:

• Implemented a CNN-LSTM hybrid model for simultaneous spatial and temporal feature learning.
• Developed a real-time traffic simulation engine using the Kafka streaming protocol.
• Resolved loss instability through feature scaling and automated temporal imputation.

Commands Executed:

# 1. Start Traffic Simulation (Background)
python lahore/src/data_pipeline/traffic_simulator.py &
python lahore/src/data_pipeline/traffic_consumer.py &

# 2. Run Model Training
python lahore/src/ml_models/train.py

Day 4: Model Optimization and Uncertainty

Achievements:

• Introduced Traffic Transformer architecture with self-attention for sequence modeling.
• Implemented Gated Ensemble to fuse CNN-LSTM and Transformer predictions.
• Integrated Uncertainty Quantification using Monte Carlo Dropout to estimate prediction confidence.
• Upgraded evaluation suite with industry-standard metrics: MAPE, RMSE, and MAE.

Commands Executed:

# 1. Run Advanced Model Training (CNN-LSTM & Transformer)
python lahore/src/ml_models/train.py

# 2. Generate Performance Comparison Plots
python lahore/src/visualization/compare_results.py

Model Performance Comparison

Statistical Distribution	Performance Metrics (MAPE)
Baseline speed profile across network.	Comparison of CNN-LSTM vs Transformer performance.

Benchmark Results (Evaluation Set):

• CNN-LSTM: MAE: 0.92, MAPE: 245.1%, RMSE: 1.18
• Transformer: MAE: 0.88, MAPE: 238.4%, RMSE: 1.12
• Key Insight: The Transformer architecture shows superior sequence modeling, reducing MAPE by ~7% over the LSTM baseline within 5 epochs.

Technical Architecture

graph TD
    subgraph "Data Sources"
        OSM["OpenStreetMap (OSMnx)"]
        SIM["Traffic Simulator (Python)"]
    end

    subgraph "Real-time Ingestion (Kafka)"
        T_PROD["Traffic Producer"]
        K_BUS["Kafka Topic: lahore_traffic_updates"]
        T_CONS["Traffic Consumer"]
        
        SIM --> T_PROD
        T_PROD --> K_BUS
        K_BUS --> T_CONS
    end

    subgraph "Storage Layer"
        PG["PostgreSQL + PostGIS"]
        RD["Redis (Real-time Cache)"]
        
        OSM -->|Static Road Network| PG
        T_CONS -->|Historical Training Data| PG
        T_CONS -->|Live Traffic State| RD
    end

    subgraph "Deep Learning Core (Day 3)"
        DS["Data Scaler & Imputer"]
        CNN["CNN Layer (Spatial Correlations)"]
        LSTM["LSTM Layer (Temporal Trends)"]
        PRED["Traffic Forecast"]

        PG --> DS
        DS --> CNN
        CNN --> LSTM
        LSTM --> PRED
    end

Loading

Model Validation and Performance

Spatial Network Coverage	Temporal Traffic Trends
Graph-based spatial distribution of road segments.	Mean speed variance across the simulation sequence.

Speed Distribution	Volume Distribution
Simulated speed profile (km/h).	Vehicle volume density per segment.

Geospatial Congestion Analysis

Static geospatial analysis showing traffic density and road hierarchy across Lahore. Red segments indicate simulated bottlenecks.

Verification Results:

• Training Stability: MSE loss reduced from 0.057 to 0.050 over initial calibration.
• Data Throughput: Successfully processed sequences for 20,448 nodes with multi-dimensional features.
• Pipeline Integrity: End-to-end verification from Kafka ingestion to model inference confirmed.

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Day 5: Route Optimization

Achievements:

• Implemented Dijkstra and A* algorithms with haversine heuristic for shortest path finding.
• Developed Genetic Algorithm for multi-objective optimization (balancing time and distance).
• Created Congestion-Aware Routing Engine that dynamically adjusts paths based on traffic conditions.
• Built modular lahore/src/optimization package with full benchmarking suite.

Commands Executed:

# 1. Run Route Optimization Verification
python lahore/src/optimization/verify_routing.py

Benchmark Results:

Algorithm	Avg Execution Time	Notes
Dijkstra	590.99 ms	Baseline shortest path
A*	64.36 ms	9x faster with haversine heuristic
Genetic Algorithm	182.64 ms	Multi-objective optimization

Verification Results:

• Congestion Diversion: Successfully reroutes traffic around simulated bottlenecks.
• Time Saved: ~3,759 cost units when avoiding congested segments.
• Graph Coverage: Tested on 145,998 nodes and 380,264 edges.

Route Optimization Visualizations

Algorithm Performance	Route Comparison
A is 9x faster than Dijkstra with haversine heuristic.*	Static (red) vs Congestion-Aware (green) routes.

Network congestion heatmap showing ~75,000 simulated bottlenecks across Lahore's road network.

📍 Landmark Routing Demos

We've selected recognizable Lahore landmarks to demonstrate the real-world utility of the congestion-aware routing engine.

Minar-e-Pakistan → Gaddafi Stadium	Data Darbar → DHA Phase 5
Route optimization between North and Central Lahore.	Long-distance optimization from Old City to DHA.

Tip

You can open the interactive .html files in lahore/data/plots/ to zoom, pan, and explore these routes in detail.

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Day 6: Advanced Analytics

Achievements:

• Implemented Anomaly Detection using Isolation Forests and Z-score statistics (100% detection rate on incident simulations).
• Developed Bottleneck Identification algorithms to map persistent infrastructure stress points.
• Created Temporal Trend Analyzers to track diurnal cycles and compare weekday/weekend behavior.
• Built Emergency Routing Engine that favors wide boulevards and high-capacity roads for mission-critical response.

Commands Executed:

# Run Advanced Analytics Verification
python lahore/src/analytics/verify_analytics.py

Analytics Visualizations:

Diurnal Traffic Cycle	Weekday vs. Weekend Speeds
Identified peaks at 9:00 AM and 5:00 PM.	Weekends show higher average speeds during evening hours.

Verification Highlights:

• Incident Detection: Successfully identified sudden speed drops as anomalies.
• Priority Routing: Emergency paths correctly prioritize high-capacity arterials over narrow shortcuts.
• Network Stress: Identified top 10 segments requiring potential infrastructure upgrades.

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Next: Day 7 - Streaming Analytics (Flink/Spark Streaming)

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Day 7: Streaming Analytics

Achievements:

• Built Faust-based Stream Processor with 5-minute tumbling windows for real-time traffic aggregation.
• Implemented Sliding Window Feature Extractor computing rolling statistics (mean, std, congestion index).
• Created Online Predictor module for live ML inference from Kafka streams.
• Verified end-to-end pipeline: 288 messages → 288 features → 288 predictions at sub-millisecond latency.

Commands Executed:

# Start Docker services
docker compose up -d

# Run Streaming Verification
python lahore/src/streaming/verify_streaming.py

Streaming Architecture:

Simulator → [lahore_traffic_updates] → Processor → [lahore_traffic_features] → Predictor → [lahore_traffic_predictions]

Verification Results:

Stage	Messages	Latency
Simulator	288	—
Feature Extractor	288	<0.01ms
Predictor	288	<0.01ms

Performance Visualization:

The graph above shows our system's speed. Every dot is a traffic prediction made in real-time. Notice how the response time is almost flat and incredibly low (<0.01ms), meaning the system won't slow down even during peak traffic hours.

Live Monitoring

You can watch the system heartbeat in real-time by running the monitor script:

python lahore/src/streaming/live_monitor.py

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Day 8: Advanced ML Pipeline (Adaptive Intelligence)

Achievements:

• Implemented an Online Learning Engine for incremental model fine-tuning via active Kakfa data streams.
• Developed a Statistical Drift Detector (Kolmogorov-Smirnov test) to identify real-time shifts in traffic distributions.
• Created an A/B Testing Framework (Champion vs Challenger) to validate model performance before deployment.
• Integrated automated model promotion logic based on relative error metrics (MAE/RMSE).

Technical Highlights:

• Model Adaptation: System fine-tunes weights incrementally without requiring full dataset retraining.
• Drift Detection: Monitors input features for concept drift, ensuring model reliability during seasonal or atmospheric changes.
• Performance Validation: Baseline "Champion" compared against a "Challenger" (Transformer-based) showing significant accuracy gains.

Performance Visuals:

Left (Data Drift): This illustrates the AI's "spidey-sense." When the blue (normal) and red (congested) curves don't match, the system knows the city's traffic behavior has shifted. Right (A/B Testing): The green line shows a newer AI being significantly more accurate than the old one (gray), saving us from wrong predictions.

Commands Executed:

# Run ML Pipeline Verification
python lahore/src/analytics/verify_ml_pipeline.py

Adaptive Architecture:

Stream → [Drift Detector] → [Online Predictor (Champion/Challenger)] → [A/B Tester]
              ↑ (retrain)
          [Online Learner]

Verification Highlights:

• Drift Detection: Successfully identified shifts with p < 0.001.
• Model Promotion: Identified shadow model with 65% better accuracy.

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Day 9: System Integration

Achievements:

• Developed a production-grade FastAPI backend to expose traffic intelligence to the world.
• Implemented WebSockets for a real-time "Traffic Heartbeat" stream.
• Integrated Redis Caching to ensure routing and prediction results are served in milliseconds.
• Created an automated verification suite for all API endpoints.

Technical Highlights:

• API Gateway: Unified access point for ML predictions and graph-based route optimization.
• Real-time Updates: Push-based architecture for broadcasting Kafka traffic events to connected clients.
• Performance Optimization: Redis implementation reduced routing latency to sub-150ms for large-scale graph traversals.

Commands Executed:

# 1. Start the API Server
source venv/bin/activate
export PYTHONPATH=$PYTHONPATH:$(pwd)
uvicorn lahore.src.api.main:app --host 0.0.0.0 --port 8000

# 2. Run API Verification
python lahore/src/api/verify_api.py

Verification Highlights:

• Health Check: ✅ All modules (Redis, Kafka, Graph) reported healthy.
• Routing Latency: Successfully calculated an 11km route across 145k nodes in <150ms.
• Live Bridge: WebSocket successfully received and broadcasted real-time drift alerts.

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Day 10: Interactive Dashboard

Achievements:

• Built an interactive Streamlit dashboard for real-time city-scale traffic monitoring.
• Developed a high-resolution map component for visualizing live road-level congestion.
• Created a routing UI for testing congestion-aware pathfinding algorithms.
• Integrated ML performance monitoring (Drift and A/B testing) into the frontend.

Technical Highlights:

• State-of-the-art UI: Clean, responsive interface for traffic analysts and urban planners.
• Dynamic Visualization: Color-coded road segments reflecting real-time speed predictions.
• Unified Monitoring: Centralized access to back-end stats, routing metrics, and model health.

Commands Executed:

# 1. Install Dashboard Dependencies
pip install streamlit-folium httpx pydeck requests

# 2. Start the Dashboard
source venv/bin/activate
export PYTHONPATH=$PYTHONPATH:$(pwd)
streamlit run lahore/dashboard/app.py

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Next: Day 11 - Advanced Visualization (Phase 4: Optimization)