Machine Learning for Predicting Traffic and Determining Road Capacit

Alex Lewis, Rina Azoulay, Esther David

Research output: Contribution to journalConference articlepeer-review


This study proposes the use of machine learning techniques to predict traffic speed based on traffic flow and other road-related features, utilizing the California Freeway PeMS traffic dataset. Extensive research has been dedicated to the prediction of road speed; however, the primary challenge lies in accurately forecasting speed as a function of traffic flow. The learning methods compared include linear regression, K-nearest neighbors (KNN), decision trees, neural networks, and ensemble methods. The primary objective of this research is to develop a model capable of estimating road capacity, a crucial factor in designing an auction system for road usage. The findings reveal that the performance of each algorithm varies with the selection of features and the volume of data available. The results demonstrate that ensemble methods and KNN surpass other models in accuracy and consistency for predicting traffic speed. These models are then employed to create a flow-speed graph, which aids in determining road capacity.

Original languageEnglish
Pages (from-to)1162-1169
Number of pages8
JournalInternational Conference on Agents and Artificial Intelligence
StatePublished - 2024
Externally publishedYes
Event16th International Conference on Agents and Artificial Intelligence, ICAART 2024 - Rome, Italy
Duration: 24 Feb 202426 Feb 2024

Bibliographical note

Publisher Copyright:
© 2024 by SCITEPRESS - Science and Technology Publications, Lda.


  • Capacity Estimation
  • Machine Learning
  • Traffic Forecasting


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