Network-Level Travel Time Prediction Considering The Effects of Weather and Seasonality

2026-02-22

AI Summary

This study addresses the influence of weather and seasonal factors on network-wide Travel Time Index (TTI) by proposing a machine learning prediction framework that explicitly integrates meteorological and seasonal features. Leveraging over 50,000 TTI observations collected over six years in Washington, D.C., the authors systematically incorporate weather and seasonal variables into predictive models and comparatively evaluate the performance of Ridge Regression, Support Vector Machines, and other methods for both short-term and long-term forecasting. The work presents the first quantitative assessment of weather- and season-induced effects on TTI at the network scale, demonstrating that Ridge Regression consistently outperforms competing models across all prediction tasks, yielding significantly improved accuracy. These findings offer a robust methodological foundation for intelligent transportation management under varying environmental conditions.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Time-Series/Data Streams
• Application Domains: Transportation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Accurately predicting travel time information can be helpful for travelers. This study proposes a framework for predicting network-level travel time index (TTI) using machine learning models. A case study was performed on more than 50,000 TTI data collected from the Washington DC area over 6 years. The proposed approach is also able to identify the effects of weather and seasonality. The performances of the machine learning models were assessed and compared with each other. It was shown that the ridge regression model outperformed the other models in both short-term and long-term predictions.

Problem

• travel time prediction
• weather effects
• seasonality
• network-level
• machine learning

Innovation

• travel time prediction
• machine learning
• weather effects
• seasonality
• ridge regression