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diSOLVE

Data Integrated Snow Operations Layer Visualization Engine

diSOLVE is a decision-support model that predicts winter road icing risk and recommends maintenance treatments by combining terrain, solar exposure, and real-time weather behavior.

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Winter in Minnesota presents persistent challenges for safe and efficient transportation. While road maintenance teams work tirelessly through storms, many early-icing and slow-thawing locations remain difficult to anticipate. diSOLVE is a prototype concept built to explore whether cities can use existing spatial data and live weather information to better predict these risks.This case study describes the origin, structure, and potential applications of diSOLVE. It serves as a communication artifact rather than a fully deployed product, but it demonstrates a clear and achievable path toward predictive winter road intelligence.

Background and Motivation

My interest in winter road microclimates started in a very ordinary place. On my daily dog-walking route, there is a short section of sidewalk that stays icy for months. It receives almost no sunlight in the winter, so even moderate temperatures do not melt it. Since sidewalks are not salted, this area becomes a recurring hazard throughout the season.That small observation raised a broader question:If a single shaded sidewalk experiences this much persistent ice, how many road segments behave the same way? And are those patterns currently accounted for in maintenance planning?This curiosity grew into a deeper exploration of how winter weather interacts with topography, built infrastructure, and pavement temperature. It became the foundation of the diSOLVE prototype.

Operational Challenges in Winter Road Safety

Local governments manage winter operations with a combination of skill, institutional knowledge, and experience. Operators know which hills freeze first, which curves refreeze after sunset, and which shaded areas tend to stay icy long after a storm ends. The challenge is that much of this knowledge is informal. It is collected over decades of field experience and is rarely stored in a systematic way.Without centralized, event-specific insights, cities face several gaps:

Winter operations teams need more than maps. They need a consistent, interpretable system that supports real-time decisions during storms.

Clarifying the Need Through Practitioner Input

Conversations with the Apple Valley Streets Department were critical to shaping the realistic boundaries of this project & created a baseline understanding of how one city attacks snow events. Two insights stood out:

These constraints helped shift my focus from technical complexity toward operational clarity. diSOLVE is designed to support, not replace, the professional judgment of winter operators.

Conceptual Framework of diSOLVE

diSOLVE explores how three primary data categories influence winter icing patterns: terrain, solar exposure, and weather conditions. Each category contributes unique information about how a road interacts with the environment.

Terrain

Terrain shapes thermal behavior. Steeper and north-facing segments often freeze earlier.

Solar Exposure

Sunlight, or the lack of it, is a major driver of ice longevity.

Weather

These values link environmental conditions to real-time operations.Together, these inputs feed into a prototype event-level risk model that classifies each road segment by its likelihood of icing.

Prototype Output and Interpretation

The output of diSOLVE is a composite risk score assigned to each road segment. This score is not absolute. It is a comparison among segments within the same storm event. The model highlights areas that are structurally more vulnerable based on their terrain, shading, and thermal behavior.The prototype produces:

This framework creates a foundation for more informed pretreatment and routing decisions.

Why a Prototype Still Adds Value

Even though diSOLVE is not a production-ready application, the prototype demonstrates several important concepts that matter for municipal decision-making:

Prototypes like this act as communication tools. They help cities visualize their operational environment in new ways and identify opportunities for refinement.

Potential Uses for a Future Version

If further developed, diSOLVE could support:

The model provides a baseline for more detailed calibration, integration into dashboards, and iterative improvements informed by field staff.

Future Development Path

A logical next step would involve:

This work would turn the conceptual framework into a functional decision-support asset.

Conclusion

diSOLVE began with a small winter inconvenience and grew into a structured exploration of how cities can combine spatial data and lived experience to predict winter road icing. Although early in development, the framework shows the potential for more proactive and data-informed winter operations.This project demonstrates that local governments can use the tools they already have to better understand the winter landscape, support staff, and improve community safety.

Practical Use Guide

1. Support policy and operations recommendations

You can show crews why some areas don’t require as much salt, using data that aligns with their operational experience.

2. Integrate the risk map into stormwater, MS4, and TMDL reporting

Provide evidence that the city is moving toward targeted reduction strategies.

3. Identify chloride hotspots and vulnerable watersheds

Use diSOLVE outputs to prioritize

3. Identify chloride hotspots and vulnerable watersheds

Compare annual risk maps and salt application logs to evaluate program success.

How it Works: Technical Detail

Inputs

Core Logic

Weighted overlay model assigns risk based on:

Outputs

Delivery Options