Traffic Flow Optimization Bloat Control Genetic Programming Algorithm

Traffic Flow Optimization Bloat Control Genetic Programming Algorithm

Introduction

With the rapid growth of urban populations and increasing vehicle ownership, traffic congestion has become a critical challenge in many metropolitan areas. Efficient traffic flow optimization is essential to reduce travel time, minimize congestion, and enhance overall transportation efficiency. Traditional traffic assignment models, such as static and dynamic approaches, have been widely studied, but they often struggle with scalability and adaptability in large-scale, dynamic environments.

Genetic Programming (GP) has emerged as a powerful evolutionary computation technique for solving complex optimization problems, including traffic flow assignment. However, a common issue in GP is bloat—the tendency for individuals in the population to grow excessively in size without corresponding improvements in performance. This bloat increases computational overhead and reduces decision-making efficiency. To address this, various bloat control methods have been proposed to constrain the growth of GP individuals while maintaining or improving their effectiveness.

This article presents a comprehensive study on applying bloat control techniques in GP for dynamic traffic flow optimization. The goal is to develop more compact and efficient hyper-heuristic strategies that can guide traffic assignment decisions in different urban road network structures, including grid, ring radial, and free-style layouts. The performance of these strategies is evaluated through extensive simulations on real-world road networks of varying scales and traffic volumes.

Background and Related Work

Traffic Flow Assignment Models

Traffic flow assignment models can be categorized into static and dynamic approaches. Static models pre-assign traffic demand based on fixed conditions, while dynamic models adapt to real-time changes in traffic conditions. Dynamic models are further divided into analytical and simulation-based approaches. Analytical models use mathematical formulations such as optimization and control theory, whereas simulation-based models rely on traffic simulation software to replicate real-world traffic behavior.

Genetic Programming in Traffic Optimization

GP is an evolutionary algorithm that evolves tree-structured programs to solve optimization problems. In traffic flow optimization, GP can generate hyper-heuristic strategies that dynamically guide vehicle routing decisions. These strategies evaluate road conditions and assign routes to minimize overall travel time. However, GP often suffers from bloat, where individuals grow unnecessarily large, increasing computational complexity without improving performance.

Several bloat control methods have been explored in GP, including:
• Linear Parametric Parsimony Pressure (LPPP): Adjusts fitness based on both performance and individual size.

• Double Tournament (DT): Uses two selection stages—one for fitness and another for size.

• Tarpeian Method: Penalizes oversized individuals by degrading their fitness.

• Niche-based Simplification: Encourages smaller individuals by grouping similar solutions.

These methods aim to balance exploration and exploitation in GP while maintaining compact and effective solutions.

Methodology

Road Network Definition and Classification

The study uses real-world road network data from OpenStreetMap, covering cities with different structural layouts:
• Grid-style networks (e.g., Shenzhen, Xi’an): Feature a regular, checkerboard-like pattern.

• Ring radial networks (e.g., Chengdu): Consist of concentric rings connected by radial roads.

• Free-style networks (e.g., Chongqing): Have irregular, organic layouts influenced by terrain.

Each network is represented as a directed graph, where intersections are nodes and road segments are edges with attributes such as length and traffic capacity.

Genetic Programming Framework

The GP algorithm evolves hyper-heuristic strategies represented as tree structures. Each strategy evaluates road conditions and assigns routes to minimize average travel time. The key components of the GP framework include:

1. Individual Encoding: GP trees consist of terminal nodes (e.g., road attributes) and function nodes (e.g., arithmetic operations).
2. Fitness Evaluation: Strategies are assessed based on their ability to minimize average travel time in simulated traffic scenarios.
3. Genetic Operators: Crossover, mutation, and reproduction are used to evolve the population.

Bloat Control Methods

Four bloat control techniques are investigated:

1. Linear Parametric Parsimony Pressure (LPPP): Combines fitness and size into a single objective, favoring smaller individuals.
2. Double Tournament (DT): Conducts two rounds of selection—first based on fitness, then on size—to promote compact solutions.
3. Tarpeian Method: Randomly penalizes oversized individuals to reduce their selection probability.
4. Niche-based Simplification: Groups similar individuals and retains the smallest representative from each group.

Each method is tested on different road network structures to determine its effectiveness in controlling bloat while maintaining solution quality.

Experimental Setup

Simulation Environment

The experiments use the CBLab traffic simulator, which supports large-scale microscopic traffic simulations with over 100,000 intersections and 1 million vehicles. The simulator dynamically generates vehicles with random origins and destinations, applying GP-derived strategies to guide routing decisions.

Training and Testing

• Training Phase: GP strategies are trained on small-scale road networks (50 intersections) for computational efficiency.

• Testing Phase: The best-performing strategies are evaluated on larger networks (100–500 intersections) under varying traffic volumes.

Performance Metrics

The primary metric is average travel time, calculated as the mean time taken by all vehicles to complete their journeys. Secondary metrics include:
• Individual Size: The number of nodes in GP trees.

• Computational Efficiency: Time required for strategy evaluation.

Results and Analysis

Bloat Control Performance

1. Grid-style Networks (Xi’an):
   • LPPP and DT performed best, achieving shorter travel times and smaller individual sizes.

   • LPPP showed superior scalability, maintaining performance as network size increased.

2. Ring Radial Networks (Chengdu):
   • DT outperformed other methods, producing the most compact and effective strategies.

   • Niche-based simplification struggled due to high diversity in solution structures.

3. Free-style Networks (Chongqing):
   • DT again demonstrated the best balance between performance and size.

   • Tarpeian method was less effective due to the irregular network layout.

Comparative Analysis

• Effectiveness: DT consistently delivered high-quality solutions across all network types, making it the most robust bloat control method.

• Efficiency: LPPP and DT reduced computational overhead by up to 30% compared to standard GP.

• Generalizability: Strategies trained on small networks generalized well to larger, more complex scenarios.

Discussion

Implications for Traffic Management

The study highlights the importance of bloat control in GP for traffic optimization. Smaller, more efficient strategies reduce computational costs and improve real-time decision-making. The success of DT suggests that combining fitness and size-based selection is highly effective in dynamic environments.

Limitations and Future Work

• Network-specific Adaptation: Future research could explore adaptive bloat control methods that adjust parameters based on network structure.

• Real-world Deployment: Testing in live traffic systems would validate the practical applicability of GP-derived strategies.

• Multi-objective Optimization: Extending the framework to consider additional objectives (e.g., emissions, fuel consumption) could enhance its utility.

Conclusion

This study demonstrates the effectiveness of bloat control techniques in GP for dynamic traffic flow optimization. The Double Tournament method emerged as the most robust approach, delivering compact and high-performing hyper-heuristic strategies across diverse road network structures. By reducing unnecessary complexity, these strategies enhance computational efficiency and scalability, making them suitable for large-scale urban traffic management. Future work should focus on adaptive bloat control and real-world validation to further advance intelligent transportation systems.

doi.org/10.19734/j.issn.1001-3695.2024.05.0177

Was this helpful?

YesNo

0 / 0