🚦 Welcome to TrafIQ
Traffic congestion has become a defining challenge of urban life in India, with cities routinely paralysed by gridlocked intersections, prolonged wait times, and rising levels of fuel consumption and air pollution.
At the heart of this dysfunction lies an antiquated system of fixed-timing traffic signals — blind to the real-time movement of vehicles, insensitive to fluctuating demand, and incapable of adapting to the dynamic rhythm of urban mobility.
The Challenge
Despite the rapid growth of cities and vehicles, traffic management systems have remained stubbornly unresponsive, unable to match the pace of urban transformation.
The following manual approaches have been attempted in the past:
- Adaptive Traffic Signal Control (ATSC): Sets different signals according to different geographical areas.
- Pressurized Routing Algorithms: Optimize vehicle flow through pressure-based decision rules.
- Greedy Models & 2D Automata Matrices: Simulate vehicle movements using simplified assumptions.
- Holiday Factor Consideration: Adjust timing based on calendar and event-based traffic variations.
While these methods have seen limited success, traffic signal control remains a complex optimization problem.
Several intelligent algorithms — including fuzzy logic, evolutionary algorithms, and reinforcement learning (RL) — have been explored to address this issue.
Incorporating Reinforcement Learning
Traffic signal control is fundamentally a sequential decision-making problem, making it ideally suited to the Markov Decision Process (MDP) and RL framework.
In this setup, an "agent" learns optimal control policies through trial and error interactions with its environment.
| Element | Description |
|---|---|
| State (S) | Represents the current environment (e.g., vehicle queues, signal phase). |
| Action (A) | The timing adjustment taken by the agent. |
| Reward (R) | A numerical feedback, tied to reduced waiting time or congestion. |
| Policy (Ï€) | The strategy mapping states to actions. |
| Value Function (V) | Estimates expected cumulative rewards from a given state. |
The goal is to learn an optimal policy (Ï€*) that maximizes the discounted cumulative reward, ensuring smoother traffic flow through continuous adaptation.
The TrafIQ Pipeline
The TrafIQ system integrates computer vision, SUMO, and deep reinforcement learning into a unified pipeline:
SUMO (Simulation of Urban Mobility) reflects real-world traffic behavior. Through TraCI, a Python controller retrieves live state data every second — including:
- Vehicle positions
- Speeds
- Queue lengths
- Emission levels
Deep RL agents (e.g., Q-Learning, PPO, DQN,MAPPO) consume this data to decide optimal signal phases, which are then applied back into SUMO creating a feedback loop.