Implementing Machine Learning at State Departments of Transportation: A Guide (2024)
Chapter: Glossary of Key Terms
Visit
NAP.edu/10766
to get more information about this book, to buy it in print, or to
download it as a free PDF.
Previous Chapter:
References
Suggested Citation:
"Glossary of Key Terms." National Academies of Sciences, Engineering, and Medicine. 2024. Implementing Machine Learning at State Departments of Transportation: A Guide. Washington, DC: The National Academies Press.
doi: 10.17226/27880.
Glossary of Key Terms
| Term | Definition |
|---|---|
| Autoencoders | A type of neural network used for unsupervised learning, particularly effective in dimensionality reduction and feature learning. |
| Classification | A common type of ML task where the goal is to assign a category or label to the input data (e.g., vehicle, pedestrian, stop sign). |
| Computer vision | A field of artificial intelligence that focuses on enabling computers to interpret and understand visual information such as images and videos. |
| Convolutional Neural Network | A type of deep learning model particularly effective for image and video processing, known for automatically learning salient features. |
| Deep learning (DL) | A subset of ML models that uses a neural network with multiple layers. |
| Feature engineering | Selecting, transforming, and creating features from raw data to improve the performance of models. For example, transforming the time of day in hours-minutes-seconds into something discrete like morning peak period, afternoon peak period, and others could be informative for an ML task. |
| Fine-tuning | The process of making small adjustments to a pre-trained model to better fit to a specific task. |
| Ground truth | The correct labeled data which are used to evaluate the performance of ML models. |
| Hyperparameters | Settings or configurations in ML models, such as the depth of decision trees or learning rate in neural networks, that are not learned from the data but set before the training process. |
| Label | A tag or category assigned to data such as its class. |
| Machine learning (ML) | A subset of artificial intelligence which involves training models to learn from data and make predictions without being explicitly programmed. |
Suggested Citation:
"Glossary of Key Terms." National Academies of Sciences, Engineering, and Medicine. 2024. Implementing Machine Learning at State Departments of Transportation: A Guide. Washington, DC: The National Academies Press.
doi: 10.17226/27880.
| Term | Definition |
|---|---|
| Model validation | The process of testing an ML model with new, unseen data to evaluate its performance. |
| Natural language processing (NLP) | A field of artificial intelligence that focuses on the interaction between computers and human language. It includes tasks such as text analysis, sentiment analysis, and language generation. |
| Object recognition | The process of recognizing and categorizing objects in images or videos. |
| Personally Identifiable Information (PII) | Any information that can be used to identify an individual, either directly or indirectly. PII can be stored in electronic, paper, or other media. |
| Pipeline | A series of data processing steps often used to train models to make predictions. |
| Pre-trained model | A model that has been trained on a large and often general dataset that can be used as a starting point for a more specific task. |
| Principal Component Analysis (PCA) | A technique used to reduce the dimensionality of data, emphasizing variation and capturing strong patterns. |
| Reinforcement learning (RL) | A type of ML in which an agent interacts with an environment and tries to maximize a reward function. |
| Semantic segmentation | The process of categorizing each pixel in an image or video with a label. |
| Semi-supervised learning | This type of ML practice combines elements of both supervised and unsupervised learning, typically using a small set of labeled data with a larger amount of unlabeled data. |
| Structured data | Formatted and organized data with a defined schema, such as tabular data. |
| Supervised learning | An ML strategy where the model is trained using only labeled data. |
| Unstructured data | Data lacking a specific format or organization, such as text, images, or audio. |
| Unsupervised learning | An ML strategy where the model is trained on unlabeled data. Clustering, dimensionality reduction, and anomaly detection are common examples of unsupervised learning techniques. |
Suggested Citation:
"Glossary of Key Terms." National Academies of Sciences, Engineering, and Medicine. 2024. Implementing Machine Learning at State Departments of Transportation: A Guide. Washington, DC: The National Academies Press.
doi: 10.17226/27880.
This page intentionally left blank.
Suggested Citation:
"Glossary of Key Terms." National Academies of Sciences, Engineering, and Medicine. 2024. Implementing Machine Learning at State Departments of Transportation: A Guide. Washington, DC: The National Academies Press.
doi: 10.17226/27880.
Abbreviations and acronyms used without definitions in TRB publications:
| A4A | Airlines for America |
| AAAE | American Association of Airport Executives |
| AASHO | American Association of State Highway Officials |
| AASHTO | American Association of State Highway and Transportation Officials |
| ACI–NA | Airports Council International–North America |
| ACRP | Airport Cooperative Research Program |
| ADA | Americans with Disabilities Act |
| APTA | American Public Transportation Association |
| ASCE | American Society of Civil Engineers |
| ASME | American Society of Mechanical Engineers |
| ASTM | American Society for Testing and Materials |
| ATA | American Trucking Associations |
| CTAA | Community Transportation Association of America |
| CTBSSP | Commercial Truck and Bus Safety Synthesis Program |
| DHS | Department of Homeland Security |
| DOE | Department of Energy |
| EPA | Environmental Protection Agency |
| FAA | Federal Aviation Administration |
| FAST | Fixing America’s Surface Transportation Act (2015) |
| FHWA | Federal Highway Administration |
| FMCSA | Federal Motor Carrier Safety Administration |
| FRA | Federal Railroad Administration |
| FTA | Federal Transit Administration |
| GHSA | Governors Highway Safety Association |
| HMCRP | Hazardous Materials Cooperative Research Program |
| IEEE | Institute of Electrical and Electronics Engineers |
| ISTEA | Intermodal Surface Transportation Efficiency Act of 1991 |
| ITE | Institute of Transportation Engineers |
| MAP-21 | Moving Ahead for Progress in the 21st Century Act (2012) |
| NASA | National Aeronautics and Space Administration |
| NASAO | National Association of State Aviation Officials |
| NCFRP | National Cooperative Freight Research Program |
| NCHRP | National Cooperative Highway Research Program |
| NHTSA | National Highway Traffic Safety Administration |
| NTSB | National Transportation Safety Board |
| PHMSA | Pipeline and Hazardous Materials Safety Administration |
| RITA | Research and Innovative Technology Administration |
| SAE | Society of Automotive Engineers |
| SAFETEA-LU | Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users (2005) |
| TCRP | Transit Cooperative Research Program |
| TEA-21 | Transportation Equity Act for the 21st Century (1998) |
| TRB | Transportation Research Board |
| TSA | Transportation Security Administration |
| U.S. DOT | United States Department of Transportation |
Subscribe to Email from the National Academies
Keep up with all of the activities, publications, and events by subscribing to free updates by email.