Intelligence Traffic Light Control using Machine Learning Algorithms

One of theoretical in intelligence traffic light control is computational learning theory. It’s analyze computational complexity of machine learning algorithms. There are two types of machine learning, supervised learning, unsupervised learning and regression learning. It is mainly deal with supervised learning.  Supervised learning is learning where the sample dataset is labeled with useful information. There are two variable types of supervised learning, categorical and continuous.  Categorical variable (nominal variable) is one that has two or more categories. For example, male and female. Continuous variable can only take on a certain number of values. For example, 1 or 2. We can conclude the hypothesis where the intelligence traffic light control using machine learning algorithm based on supervise sample training data.

Sample training dataset from driver’s behavior to determine traffic light status.

Sample training dataset from driver’s behavior to determine traffic light status.

In intelligence traffic light system, we believe the system embedded with proper sophisticated communication and sensor network system. The traffic lights are able to communicate each other so it can utilize more resources to ever increasing travelling times and diminishing waiting times before red traffic lights. The information gathered from sensor network system applied inside the traffic light so it can study driver’s behavior. Besides that, drivers will get the traffic information from mobile app given by the government so they can plan well before they drive to their destination. More advanced traffic light system when emergency vehicle such as police or ambulance go through the road so the traffic light will remain green to avoid any collision with other vehicles.

Intelligence traffic light control with communication and sensor network system.

Intelligence traffic light control with communication and sensor network system.

Based on observation and experimental retrieved from traffic light sensor, driver’s behavior can be collected and convert into valuable information to diminish waiting times before red traffic lights occur. Besides that, the data also been collected will be transform into information using machine language algorithm to create an efficient and accurate model for prediction analysis. Using predictive analysis knowledge, waiting times can be reduced even limited resources provided by current infrastructures lead to ever increasing travelling times. There are a lot of machine language algorithms such as neural network, linear regression, random forest, KNN and many more. The more data been collected, the more model will be accurate because the model not only suitable on certain time, it’s need to be supervise from time to time.

Deep learning understanding.

Deep learning understanding.

One thought on “Intelligence Traffic Light Control using Machine Learning Algorithms

Leave a Reply

Your email address will not be published. Required fields are marked *