Quantifying the Benefits of Roadside Vegetation

Ryu, Kyoeng Rok; Im, Joowon; Park, June Young; Shamshiri, Alireza; McCullough, Steven Tanner; Somani, Shikha · 2025 · ROSA P / Texas. Department of Transportation

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Summary

This study addresses the lack of a comprehensive framework for quantifying the benefits and risks of roadside vegetation, a gap that hinders effective management by transportation agencies. While Texas Department of Transportation (TxDOT) invests $36 million annually in the Green Ribbon Program to enhance sustainability, aesthetics, and quality of life, existing guidelines typically focus narrowly on operational maintenance rather than holistic evaluation. The research aims to develop the Roadside Vegetation Evaluation Toolkit (RVET) to assist planners, environmental practitioners, and landscape designers in evaluating roadside vegetation projects across five critical dimensions: environmental benefits, operational and maintenance measures, lifecycle costs, and public perception regarding both road experiences and aesthetics. The methodology involved creating an integrated database incorporating extensive geospatial data to account for the variability of vegetation across Texas. The dataset includes TxDOT roadway inventory, control sections for site selection, annual average precipitation data from USDA NAIP (1991–2020), and hydrologic soil group classifications from ORNL DAAC (2018). The RVET framework assesses specific parameters within each module. Environmental benefits are modeled using academic literature to estimate carbon sequestration, urban heat island mitigation, soil loss, and runoff reduction. Operational and maintenance modules provide recommendations for mowing, herbicide application, seeding for erosion control, and wildflower programs. Lifecycle costs are projected over 20 years, accounting for installation, maintenance, and environmental benefits, while adjusting for discount and inflation rates. Public perception is evaluated through literature reviews and a small-scale focus group survey using a 5-point Likert scale to rate the appeal of vegetation combinations based on road features, vegetation characteristics, landforms, and cultural landscape elements. The primary finding is the successful development of the RVET, a comprehensive tool that quantifies roadside vegetation impacts across the five defined modules. The toolkit provides high-level estimates for environmental impacts suitable for initial planning, though it notes these should not replace formal environmental impact statements. It offers specific operational guidance, such as identifying suitable herbicides and wildflower species for erosion control, and calculates the total cost of maintenance activities. The lifecycle cost analysis provides long-term financial insights, while the perception module identifies how factors like tree height, vegetation density, and topography influence driver satisfaction and safety. The study also synthesizes current practices from TxDOT and other state DOTs, highlighting best practices for pollinator support, such as delaying mowing until after the first frost and using diverse native seed mixes. The significance of this research lies in its provision of a standardized, data-driven approach to roadside vegetation management for TxDOT. By integrating geospatial data with multi-dimensional assessment modules, the RVET enables statewide implementation of improved vegetation management strategies. This tool supports decision-making that balances safety, environmental sustainability, and aesthetic appeal, ultimately enhancing the health and safety of Texans while optimizing the economic efficiency of transportation infrastructure investments.

Key finding

The study introduces the Roadside Vegetation Evaluation Toolkit (RVET), a comprehensive framework that quantifies roadside vegetation benefits across environmental, operational, economic, and perceptual dimensions using integrated geospatial data and literature-based modeling.

Methodology

modeling

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

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• perceptual countermeasures