CalTrans Rapid Trash Assessment Method

Latest News

Caltrans has completed an initial statistical model for identifying trash hotspots and currently developing predictive and image recognition models. These models will be improved with data collected from trash control implementation and provide insight on how and where to deploy resources.

Project Description

Caltrans Machine Learning Project for Trash Control Implementation

Caltrans is currently developing statistical, predictive and image recognition models with machine learning to assist with its trash control efforts statewide and in the SF Bay Area.

Specifically, these models are being used to identify trash hotspots, provide estimates of future trash collection and verify visual assessment results with image recognition.

For more information on this project, please contact Walter Yu: walter.yu@dot.ca.gov

Image Recognition

Initial results from training dataset which will be used to evaluate visual assessment photos

Predictive Modeling

Regression algorithms compared to evaluate the best one for a predictive model:

Correlation Analysis

Relationship identified between trash volume and labor/materials/resources (red = high, teal = low):

Trash Collection Efficiency Rates

Clustering algorithm used to identified efficiency groups within high trash corridors (red dot = center of group, shades = groups):