Deep Learning for Traffic Lights Control for Heavy Cars

By Sultan Khaibar Safi

Details: BSc. IT & Artificial Intelligence and Robotics engineering

Published: May 10, 2024 12:00

Deep learning can be employed to optimize traffic light control systems, particularly for managing the flow of heavy vehicles such as trucks and buses. These vehicles have different dynamics and impact on traffic flow compared to lighter vehicles, and their efficient management can significantly improve overall traffic conditions. Here is a step-by-step guide on how deep learning can be applied to control traffic lights for heavy vehicles:

1. Problem Definition
The objective is to optimize traffic light signals to minimize wait times, reduce congestion, and improve traffic flow, with a particular focus on accommodating heavy vehicles. The problem can be approached as a reinforcement learning task where the goal is to learn an optimal policy for traffic light control.

2. Data Collection
Collect data from traffic intersections, including:

Traffic camera footage or sensor data.
Vehicle counts and classifications (distinguishing between heavy and light vehicles).
Traffic flow patterns at different times of the day.
Historical traffic signal timings and their effects on traffic flow.
3. Data Preprocessing
Preprocess the data to make it suitable for training a deep learning model:

Labeling and Segmentation: Label vehicles in images/videos as heavy or light. Segment traffic flow data into appropriate intervals (e.g., every minute).
Normalization: Normalize traffic counts and signal timings to a consistent scale.
Feature Engineering: Extract relevant features such as vehicle speed, traffic density, and signal phase duration.
4. Model Design
Design a deep learning model suitable for controlling traffic lights. Here, we can use a reinforcement learning approach with deep Q-learning or a policy gradient method.

State Representation: Encode the current traffic situation as the state. This includes the number of heavy and light vehicles in each lane, current traffic light phase, and time since the last phase change.
Action Space: Define the possible actions as changing the traffic light phases (e.g., green to red, red to green, or extending the green phase).
Reward Function: Design a reward function that incentivizes reducing the wait time for heavy vehicles, balancing the overall traffic flow, and minimizing congestion. For example, a positive reward can be given for reducing the queue length of heavy vehicles.
5. Reinforcement Learning Algorithm
Implement a deep reinforcement learning algorithm to learn the optimal traffic light control policy:

import numpy as np
import tensorflow as tf
from tensorflow.keras import layers
from collections import deque
import random

# Environment parameters
state_size = ... # Define based on the state representation
action_size = ... # Define based on the number of possible actions
gamma = 0.95 # Discount factor
epsilon = 1.0 # Exploration rate
epsilon_min = 0.01
epsilon_decay = 0.995
learning_rate = 0.001
batch_size = 32
memory = deque(maxlen=2000)

# Define the neural network model for Q-learning
def build_model():
model = tf.keras.Sequential()
model.add(layers.Dense(24, input_dim=state_size, activation='relu'))
model.add(layers.Dense(24, activation='relu'))
model.add(layers.Dense(action_size, activation='linear'))
model.compile(loss='mse', optimizer=tf.keras.optimizers.Adam(lr=learning_rate))
return model

# Initialize the model
model = build_model()

# Define the replay memory and training function
def replay(memory, batch_size):
minibatch = random.sample(memory, batch_size)
for state, action, reward, next_state, done in minibatch:
target = reward
if not done:
target += gamma * np.amax(model.predict(next_state)[0])
target_f = model.predict(state)
target_f[0][action] = target
model.fit(state, target_f, epochs=1, verbose=0)
if epsilon > epsilon_min:
epsilon *= epsilon_decay

# Training loop (simplified)
for e in range(1000): # Number of episodes
state = ... # Initialize state from the environment
for time in range(500): # Time steps per episode
action = np.random.choice(action_size) if np.random.rand() <= epsilon else np.argmax(model.predict(state)[0])
next_state, reward, done, _ = ... # Step in the environment
memory.append((state, action, reward, next_state, done))
state = next_state
if done:
break
if len(memory) > batch_size:
replay(memory, batch_size)


6. Simulation and Training
Simulate the traffic environment to train the deep reinforcement learning model. Use traffic simulation software such as SUMO (Simulation of Urban MObility) to create a realistic traffic scenario and provide a platform for the RL agent to interact with.

7. Evaluation and Optimization
Evaluate the trained model using metrics such as average wait time for heavy vehicles, overall traffic flow efficiency, and reduction in congestion. Optimize the model by fine-tuning hyperparameters and incorporating more advanced techniques such as Double DQN or Dueling DQN.

8. Deployment and Monitoring
Deploy the trained model in a real-world traffic control system. Continuously monitor its performance and make adjustments as necessary to account for changing traffic patterns and new data.

By using deep reinforcement learning, we can develop an intelligent traffic light control system that optimizes the flow of heavy vehicles, reducing congestion and improving traffic efficiency. This approach allows for adaptive and data-driven management of traffic signals, leading to better outcomes for all road users.