Traffic Prediction ML Model

Urban traffic congestion has become a critical challenge in metropolitan areas, leading to productivity losses, increased air pollution, and reduced quality of life. Traditional traffic management systems rely on fixed signal timings and human-monitored controls, which prove inadequate for handling the dynamic nature of urban traffic flows.

The primary challenges include:

• Static Traffic Control: Fixed signal timings that don't adapt to real-time traffic conditions
• Peak Hour Congestion: Inefficient signal management during high-traffic periods
• Heterogeneous Traffic: Difficulty in managing mixed vehicle types (light and heavy vehicles)
• Real-time Optimization: Lack of predictive systems for proactive traffic management

We developed a Support Vector Regression (SVR) based machine learning model that predicts optimal green signal durations at urban intersections. Our approach goes beyond traditional predictive models by creating an integrated real-time signal optimization system.

Key Innovations:

• Lane-wise Analysis: Individual traffic volume prediction for each lane at intersections
• Weighted Vehicle Classification: Differential handling of light vehicles (weight: 1) and heavy vehicles (weight: 2)
• Multi-factor Integration: Time-of-day, day-of-week, and junction-specific patterns
• Real-time Adaptation: Dynamic signal timing based on predicted traffic conditions

Technical Implementation:

The model utilizes a comprehensive dataset spanning 10 months with traffic data from 4 major junctions, analyzing 15-minute interval patterns to predict optimal signal durations with high accuracy.

Through systematic hyperparameter optimization using GridSearchCV, we achieved significant improvements in model performance, demonstrating the effectiveness of SVR for traffic signal optimization.

Performance Metrics:

Optimized Hyperparameters:

• C (Regularization): 150 - Optimal balance between training accuracy and generalization
• Gamma (RBF Kernel): 0.0005 - Appropriate influence radius for data points
• Epsilon (Error Tolerance): 0.02 - Fine-tuned margin for prediction accuracy

Impact & Implications:

• Congestion Reduction: Significant improvements in traffic flow during peak hours
• Real-time Optimization: Dynamic signal control adapting to changing traffic patterns
• Scalable Solution: Framework applicable to urban intersections across smart cities
• Published Research: Peer-reviewed contribution to intelligent transportation systems

This research has been published in IEEE Conference proceedings, contributing to the field of intelligent transportation systems and machine learning applications in urban planning.

The research demonstrates the potential for machine learning-driven traffic optimization in smart city infrastructure, providing a foundation for future developments in intelligent transportation systems.