Integrating Data and Physics for Assessing Maintenance Strategies to Enhance Winter Road Resilience

PI: Yong Deng (WSU),, ORCID:

Co PIs: Xianming Shi (WSU), Chuang Chen (WSU)

AMOUNT & MATCH: $40,000 federal from PacTrans; $40,000 federal Match

PERFORMANCE PERIOD: 8/16/2023 – 8/15/2025

STATUS: Active

CATEGORIES: Winter Road Maintainace, Climate Change, Safety


FINAL PROJECT REPORT: will be available once completed

PROJECT DATA: will be available once completed

DESCRIPTION: Winter road maintenance (WRM) operations are critical to nearly 70% of the roads and more than 70% of the population located in cold regions of U.S. In light of climate change, the frequency and intensity of extreme weather events such as snowstorms are increasing. Transportation agencies are under increasing pressure to provide a high level of service (LOS) and to improve safety and mobility in a fiscally and environmentally responsible manner.
Currently, there is an urgent demand for more effective solutions to address the resilience challenges on roadways enduring extreme winter weather. However, there is a lack of investigation on how to quantify the effects of various maintenance strategies on winter road resilience. By definition, resilience is the ability of a roadway to bounce back to a performance level before a disruptive event or other disturbances, and it can be characterized by metrics such as robustness, adaptability, agility, redundancy, response time, recovery time, level of recovery, and performance loss. For instance, the recovery of traffic speed and traffic volume could be used as a resilience index for the effectiveness of WRM operations, whereas vulnerability is one of the most explored indexes in transportation system resilience studies.

In addition to resilience index, it is imperative to investigate the appropriate model type for quantifying the effects of maintenance strategies. In previous research, regression analysis and path analysis have been conducted on exploring the relations between winter weather variables, road condition and maintenance operations. With the advancements in data analysis and modeling techniques, such as machine learning (ML), it is valuable to explore a model type that can effectively capture the maintenance process and its impact on road resilience. This investigation can provide insights into the dynamic relationship between maintenance activities and road resilience, enhancing the understanding in this area.

Research Project Progress Report #1 10/10/2024
Research Project Progress Report #2 4/10/2025
No Cost Extension Request 6/15/2025
Draft Report 6/15/2025
Final Project Report 7/15/2025