pacific nw transportation consortium region 10 Pactrans

Enhancing Mobility: A Roadmap for Improving Highway Conditions

PI: Kishor Shrestha (WSU), kishor.shrestha@wsu.edu, ORCID:

Co PIs: none

AMOUNT & MATCH: $30,000 federal from PacTrans; $30,000 federal Match

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

STATUS: Active

CATEGORIES: Roadway Construction, Mapping, Safety

UTC PROJECT DOCUMENTATION:

FINAL PROJECT REPORT: will be available once completed

PROJECT DATA: will be available once completed

DESCRIPTION:  The roadway system is an essential component of millions of Americans’ mobility and economic growth. According to the American Society of Civil Engineers, 43% of public roadways (across the US) in 2021 were in poor condition, which increased from 21% in 2015. In the Pacific Northwest, the Washington State Department of Transportation (DOT) missed 44% of its highway maintenance asset condition targets in 2022, an increase from 32% in 2020 and 33% in 2019. Such misses in targets increased the ‘due’ and ‘past due’ maintenance backlog in Washington state. When roadway assets are not maintained on time, reactive maintenance or rehabilitation becomes necessary, which is substantially more expensive due to decreased asset lifespans, lost production time, and poor planning. For instance, the chip seal can cost $50,000 to $60,000 per lane mile (Weston, 2012). Suppose chip sealing is “past due” or not performed promptly. In that case, it can harm the lower pavement layers, potentially leading to the need for costly reconstruction ($400k to $500k per lane mile). This posed critical challenges to state DOTs, which weakened their capacity to upkeep the roadways. Challenges include imbalanced asset conditions and increasing maintenance costs because of reactive maintenance.

State DOTs primarily rely on a traditional fund allocation approach (based on need-based subjective judgments), which could be more effective and efficient. Many states continue using this approach, even though they have historical data and great potential to utilize a practical data-driven approach. Therefore, there is an immediate need to develop an innovative roadmap/ framework to allocate funds effectively based on future asset conditions utilizing a data-driven strategy. The principle objective of this study is to develop an innovative roadmap that maximizes the utilization of funds and streamlines processes for highway asset management, all based on predicted asset conditions. The roadmap will utilize predictive models to forecast the level of service (LOS) condition of the roadway assets and Machine learning (ML) algorithms. The ML algorithms will be trained, validated, and tested using historical data.

Finally, implementing the proposed roadmap aims to revolutionize state DOTs’ capacity by utilizing a data-driven approach to allocate funds, prioritize highway assets for funding, make informed decisions, predict asset LOS conditions accurately, and effectively utilize public funds. This project will significantly impact roadway asset management in 50 state DOTs. The roadmap can also be applied to other infrastructure assets such as bridges, railway lines, and water and sewer networks.