Revolutionizing Traffic Management with AI: A Solution Using Amazon Rekognition
State and local agencies spend approximately $1.23 billion annually to operate and maintain signalized traffic intersections. On the other end, traffic congestion at intersections costs drivers about $22 billion annually. Implementing an artificial intelligence (AI)-powered detection-based solution can significantly mitigate congestion at intersections and reduce operation and maintenance costs.
State and local agencies rely on traffic signals to facilitate the safe flow of traffic involving cars, pedestrians, and other users. There are two main types of traffic lights: fixed and dynamic. This increase in road users can negatively impact the efficiency of either of the two traffic systems.
Solution overview
At a high level, our solution uses Amazon Rekognition to automatically detect objects (cars, bikes, and so on) and scenes at an intersection. After detection, Amazon Rekognition creates bounding boxes around each object (such as a vehicle) and calculates the distance between each object (in this scenario, that would be the distance between vehicles detected at an intersection). Results from the calculated distances are used programmatically to stop or allow the flow of traffic, thus reducing congestion. All of this happens without human intervention.
Prerequisites
The proposed solution can be implemented in a personal AWS environment using the code that we provide. However, there are a few prerequisites that must in place. Before running the labs in this post, ensure you have the following:
- An AWS account. Create one if necessary.
- The appropriate AWS Identity and Access Management (IAM) permissions to access services used in the lab. If this is your first time setting up an AWS account, see the IAM documentation for information about configuring IAM.
- A SageMaker Studio Notebook. Create one if necessary.
Solution architecture
The following diagram illustrates the lab’s architecture:
This solution uses the following AI and machine learning (AI/ML), serverless, and managed technologies:
- Amazon SageMaker, a fully managed machine learning service that enables data scientists and developers to build, train and deploy machine learning applications.
- Amazon Rekognition supports adding image and video analysis to your applications.
- IAM grants authentication and authorization that allows resources in the solution to talk to each other.
Conclusion
This post provides a solution to make traffic lights more efficient using Amazon Rekognition. The solution proposed in this post can mitigate costs, support road safety, and reduce congestion at intersections. All of these make traffic management more efficient. We strongly recommend learning more about how Amazon Rekognition can help accelerate other image recognition and video analysis tasks by visiting the Amazon Rekognition Developer Guide.
About the authors
Author information goes here.