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Characterizing highway runoff and quantifying the influencing factors in the United States with a machine learning approach


Stormwater runoff from highways is a significant non-point pollution sources, leading to the watercourse and aquatic ecosystem impairments. The primary contaminants include sediments, nutrients, heavy metals, and polycyclic aromatic hydrocarbons (PAHs) (Trenouth and Gharabaghi, 2016). There is significant differences in contaminants concentrations among different sample sites. The large variety of contaminants are attributed to a combination of factors, such as traffic volume, average daily traffic, vehicles during a storm, antecedent dry period, rainfall pattern, climatic factors, and the adjacent land use (Opher and Friedler, 2010; Huber et al., 2016). Although there is a number of research projects studying the physical, chemical and biological processes associated with highway runoff events (Crabtree et al., 2006; Alo et al., 2007), there are still open questions regarding the characteristics of highway runoff and the complex mutual influences of the many factors.

A comprehensive Highway-Runoff database (HRDB) was developed by USGS and published in Nov. 2018. This database provides planning level information of highway runoff, by recording 242 highway sites and 6873 storm events data from 1975 to 2017 across the United States. The dataset includes more than 100,000 concentration values for 414 water quality constituents (Granato et al., 2018). This dataset provides an opportunity to study highway runoff characteristics and mechanisms across the United States, and thus informing planning approaches to control highway runoff contaminants. In this study, a data-driven model will be built based on this database to explore how traffic volume, adjacent land use and storm characteristics combine to predict highway runoff contaminants.


The objective of this research include:

1) To identify several distinguishable typologies of highway runoff characteristics. Specifically, the 242 highway sites will be clustered into several groups, with sites in each group sharing similar characteristics regarding contaminants concentration, rainfall pattern, traffic volume and land use information.

2) To build a machine learning model, measuring how traffic, land use, and climatic factors combine to predict highway runoff. The importance of different factors will be investigated, as well as their mutual influences.

3) To provide planning guidelines according to the model developed. Stormwater management strategies will be suggested for highway sites with different characteristics.

Related Research:

Several past and present studies focus on the model development to estimate highway runoff. These studies are useful resources as a reference to understand the complex process of highway runoff. With the application of the new highway runoff dataset and the machine learning models, a more robust and efficient model can be developed to estimate highway runoff. The related research include, but not limited to:


The Stochastic Empirical Loading and Dilution Model (SELDM) for runoff-quality analyses developed by Oregon Water Science Center (Risley and Granoto, 2014).


TRB research project NCHRP 25-53: Approaches for Determining and Complying with TMDL Requirements Related to Roadway Stormwater Runoff.


TRB research project NCHRP 25-20: Evaluation of Best Management Practices for Highway Runoff Control.


This research topic has relevance for researchers, stormwater engineers and planning agencies. It will help improve the understanding of highway runoff processes and mechanisms, as well as find a more effective approach to control pollution from highway runoff.

Sponsoring Committee:AKD40, Landscape and Environmental Design
Research Period:6 - 12 months
Research Priority:Medium
RNS Developer:Runzi Wang and Ming-Han Li
Date Posted:11/20/2019
Date Modified:02/13/2020
Index Terms:Runoff, Highways, Machine learning, Data files, Contaminants, Data analysis, Traffic volume, Land use, Storms,
Cosponsoring Committees:AKD50, Hydrology, Hydraulics and Stormwater
Data and Information Technology
Hydraulics and Hydrology

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