SmartFlow is an innovative traffic control system leveraging Reinforcement Learning (RL) to intelligently manage traffic signal timings and minimize congestion.
Traffic congestion is a persistent issue impacting urban mobility and environmental sustainability. SmartFlow addresses this challenge by dynamically optimizing traffic signal timings using RL techniques. By integrating with SUMO, a traffic simulation tool, SmartFlow aims to alleviate traffic congestion at intersections.
Urban areas face increasing traffic congestion, leading to longer commute times, environmental pollution, and reduced fuel efficiency. Conventional traffic signal timings often fail to adapt to real-time traffic dynamics, exacerbating congestion issues. SmartFlow addresses this by employing AI-based signal control that learns and adapts to traffic conditions.
SmartFlow's core objective is to minimize traffic congestion and enhance traffic flow efficiency. By utilizing Reinforcement Learning algorithms, the system learns optimal signal timings by observing traffic patterns, aiming to reduce waiting times and enhance traffic throughput at intersections.
- Reinforcement Learning Optimization: Utilizes RL algorithms for adaptive traffic signal control.
- Integration with SUMO: Incorporates the SUMO traffic simulation tool for realistic traffic scenarios.
- Distributed Training: Leverages federated learning across Raspberry Pis for faster model training.
- Dynamic Signal Control: Adapts signal timings in real time based on traffic conditions.
- Efficiency Improvement: Aims to reduce congestion, waiting times, and overall travel durations.
SmartFlow implements Reinforcement Learning (RL) using Deep Q-Networks (DQN) to optimize traffic signal control. The DQN architecture enables the model to learn optimal traffic signal strategies through interactions with the simulated traffic environment. The model learns to make decisions (altering signal phases) by maximizing cumulative rewards, such as minimizing waiting times or queue lengths at intersections.
To stabilize training, SmartFlow employs a DQN with a target network. This approach utilizes a separate target network that updates at intervals to provide more stable and reliable Q-value estimations during training. The target network serves as a reference, decoupled from the primary network used in action selection, aiding in more consistent and efficient learning.
The reinforcement learning paradigm, along with the integration of DQN and target networks, allows SmartFlow's models to iteratively improve their traffic signal control strategies over successive episodes, progressively enhancing their decision-making abilities to better manage traffic flow.
Here's the revised readme emphasizing distributed learning instead of federated learning:
SmartFlow SmartFlow is an innovative traffic control system leveraging Reinforcement Learning (RL) to intelligently manage traffic signal timings and minimize congestion.
Overview Traffic congestion is a persistent issue impacting urban mobility and environmental sustainability. SmartFlow addresses this challenge by dynamically optimizing traffic signal timings using RL techniques. By integrating with SUMO, a traffic simulation tool, SmartFlow aims to alleviate traffic congestion at intersections.
Need Urban areas face increasing traffic congestion, leading to longer commute times, environmental pollution, and reduced fuel efficiency. Conventional traffic signal timings often fail to adapt to real-time traffic dynamics, exacerbating congestion issues. SmartFlow addresses this by employing AI-based signal control that learns and adapts to traffic conditions.
Problem Solving SmartFlow's core objective is to minimize traffic congestion and enhance traffic flow efficiency. By utilizing Reinforcement Learning algorithms, the system learns optimal signal timings by observing traffic patterns, aiming to reduce waiting times and enhance traffic throughput at intersections.
Features Reinforcement Learning Optimization: Utilizes RL algorithms for adaptive traffic signal control. Integration with SUMO: Incorporates the SUMO traffic simulation tool for realistic traffic scenarios. Distributed Learning: Leverages distributed training across Raspberry Pis for faster model training. Dynamic Signal Control: Adapts signal timings in real time based on traffic conditions. Efficiency Improvement: Aims to reduce congestion, waiting times, and overall travel durations. Reinforcement Learning with DQN and Target Network SmartFlow implements Reinforcement Learning (RL) using Deep Q-Networks (DQN) to optimize traffic signal control. The DQN architecture enables the model to learn optimal traffic signal strategies through interactions with the simulated traffic environment. The model learns to make decisions (altering signal phases) by maximizing cumulative rewards, such as minimizing waiting times or queue lengths at intersections.
To stabilize training, SmartFlow employs a DQN with a target network. This approach utilizes a separate target network that updates at intervals to provide more stable and reliable Q-value estimations during training. The target network serves as a reference, decoupled from the primary network used in action selection, aiding in more consistent and efficient learning.
The reinforcement learning paradigm, along with the integration of DQN and target networks, allows SmartFlow's models to iteratively improve their traffic signal control strategies over successive episodes, progressively enhancing their decision-making abilities to better manage traffic flow.
SmartFlow employs a distributed learning approach across multiple Raspberry Pis to train its models. This decentralized approach enables faster model training by leveraging local data generated from individual simulated traffic scenarios on each Pi. This distributed strategy enhances scalability, privacy, and efficiency by avoiding centralizing data transfer and ensuring localized model updates.
Since the models are trained from slightly different simulations across diverse traffic scenarios, the resulting global model exhibits versatility and resilience. This versatility enables the model to adapt to various traffic conditions and scenarios, making it robust in handling real-world traffic complexities. SmartFlow's global model reflects a collective understanding derived from diverse local simulations, enhancing its adaptability in dynamic traffic environments.
(These simulations are placeholders for training on real life road intersections)