Geospatial Trucking Industry Decarbonization Explorer (Geo-TIDE): Technical Guide and Methodology

Geospatial Trucking Industry Decarbonization Explorer (Geo-TIDE): Technical Guide and Methodology

Geo-TIDE is a public, interactive decision-support tool developed by the MIT Climate & Sustainability Consortium (MCSC) to help trucking industry stakeholders identify and evaluate early opportunities for fleet and infrastructure decarbonization. By integrating public geospatial datasets such as regional freight flows, policy incentives, and spatially resolved cost and emissions models, Geo-TIDE enables data-driven decisions about where, when,and how to invest in low-carbon technologies.

In early 2023, the MCSC convened a panel [1] of trucking industry experts and researchers to explore the challenges and opportunities of transitioning fleets to low-carbon alternatives like battery electric and hydrogen fuel cell trucks. One key need identified was comprehensive tools to help stakeholders navigate the complex decisions around fleet and infrastructure transition. While substantial, high-value public data exists to support fleet decarbonization, ranging from freight flow databases [2] to infrastructure maps [3] and federal and state-level incentives [4], it is often scattered across multiple platforms, making it difficult for stakeholders to integrate them into actionable insights.

1) Geospatial Data to Explore Regional Variation. From the outset, it was clear that transition decisions would vary regionally due to differences in electricity rates, diesel prices,grid emissions, infrastructure, and regulations. These factors shape key variables like total cost of ownership and lifecycle emissions. To capture this, we added geospatial attributes to each dataset, enabling regional comparisons.

2) User Interface Design. Initially, we compiled the data into a QGIS project with layered maps showing the geospatial datasets. While useful for demonstration, this static setup Geo-TIDE: Technical Guide and Methodology required users to download the underlying GeoJSON files and lacked interactivity. To improve accessibility and flexibility, we transitioned to a custom, open-source web interface built on OpenLayers. This dynamic platform enables real-time data delivery, interactive features like click-based exploration, and seamless updates as new datasets are added.

3) Geographic Scope. Geo-TIDE currently focuses on the United States, which accounts for 22% of global transportation emissions—more than China, Russia, and India combined [5]. The U.S. is also uniquely well-suited for tool development thanks to its wealth of high-quality, publicly available data on freight, infrastructure, and policy, making it an ideal testbed for building and refining Geo-TIDE’s capabilities.

4) Integrating with the MCSC DataHub. To make Geo-TIDE publicly accessible, we further integrated it into the MCSC’s DataHub platform. This integration ensures that all data—now served through a public AWS S3 bucket—is readily available to users without requiring local installations or file downloads. Anyone with a free DataHub account can simply log in via a web browser.

5) Iterative Stakeholder Engagement. As we refined Geo-TIDE, we sought frequent input from industry experts, leading to multiple development cycles. Early feedback stressed the need for two major enhancements:

1. Deep-dive Data Exploration. Users wanted to click on states, highways, or facilities to see specific data, e.g., detailed descriptions of incentives or corridor traffic volumes. This fueled the development of our “click” feature, currently piloted on the“State-Level Incentives and Regulations” and “Hourly Grid Emissions” layers (see Section 3.5).
2. Overlay of Private Data. Many fleets maintain proprietary route and facility information. The ability to upload these data for custom analysis—without exposing them publicly—was a repeated request. This led us to design a user data upload and overlay feature, which merges user data with our public layers (see Section 3.6).

6) Tool Demos. To illustrate Geo-TIDE’s capabilities in realistic scenarios, we developed a series of case studies—ranging from short-haul freight electrification in Texas to potential hydrogen corridors, presented in Section 4. We presented these demos at MCSC partner meetings and documented them in videos and blog posts. This approach offers fleets and policymakers a tangible view of how to leverage geospatial insights to make decisions about infrastructure siting, fuel pathways, and cost/emission trade-offs.

While Geo-TIDE can benefit many different organizations within the trucking ecosystem, its development has thus far targeted three primary stakeholder groups:

• Trucking Fleets: Geo-TIDE helps fleet managers evaluate when and where to transition to low-carbon energy carriers by combining cost modeling, infrastructure availability, grid capacity, and relevant incentives. Section 4.2 illustrates how fleets in Texas and the Great Lakes region use the tool to tailor transition strategies to their specific routes and facilities.
• Truck Manufacturers: OEMs can leverage Geo-TIDE to identify regions where freight demand, infrastructure readiness, and supportive policies align to create strong markets for zero-emission trucks. Section 4.1.1 shows how manufacturers might use the tool to compare regional signals for BEV vs. hydrogen vehicles.
• Infrastructure Providers and Public Planners: Geo-TIDE supports infrastructure siting by highlighting corridors with high freight volume, layering in grid capacity, andmapping incentive availability. Section 4.1.2 presents a private-sector use case, but public agencies can also leverage these layers to plan investments and coordinate multi-state corridor development.

Across stakeholder groups, Geo-TIDE enables tailored, data-driven decisions—from early fleet pilots to infrastructure planning and policy design. Its geospatial framework highlights key regional differences in cost, infrastructure, and incentives, helping users align strategies with specific market and regulatory conditions.

The paper is organized as follows to provide both a conceptual overview of Geo-TIDE and the technical underpinnings:

• Sections 1–4: High-Level Tool Overview and Demos. Section 2 introduces the key geospatial layers (e.g. freight flows, cost/emissions metrics, infrastructure). Section 3 covers interactive elements (like the click feature, dynamic legends, and userdata upload). Section 4 showcases actual usage scenarios, illustrating how the tool can help different stakeholders—manufacturers, infrastructure providers, and fleet operators—strategize their low-carbon transitions.
• Sections 5–7: In-Depth Methodology. Here, we detail the data sources, analytics,and assumptions behind sets of Geo-TIDE layers whose synthesis involved significant methodological development. Section 5 focuses on freight flow processing and associated well-to-wheel emissions. Section 6 discusses the underlying total cost of ownership (TCO) model [6]. Section 7 explains the approach to quantifying grid capacity and energy demand under electrification.

A primary goal of Geo-TIDE is to be open, transparent, and community-driven:

• Open source & open data: The geospatial layers visualized on Geo-TIDE, and presented in Section 2 are freely available for download, either directly via the Geo-TIDE interface (see Section 3.4), or via the Zenodo data publication for Geo-TIDE [7]. We also maintain open-code repositories for the tool itself [8,9] and to produce the layers displayed [10,11,12,13], to provide visibility into the underlying methods.
• Local or cloud-based use: To support users in adapting Geo-TIDE for their own goals and use case, the source code can be easily executed on any computer to runand develop the tool locally. Meanwhile, the MCSC DataHub integration suits those preferring to access a central public tool.
• Continuous collaboration: MCSC has piloted the tool with industry partners, introducing features like interactive layer-clicking (Section 3.5) and data upload and overlay (Section 3.6) in response to partner feedback. We post regular blog updates, summarized in Table1 to share new developments, functionality, and tool demos. We welcome new collaborators, whether they are fleets looking to pilot EV or hydrogen transitions or policymakers wanting to refine incentive design.

Geo-TIDE merges a diverse set of public datasets into a single interactive mapping interface, enabling region-specific, data-driven decisions about trucking fleet decarbonization. By continuing to incorporate stakeholder feedback and adapt to evolving datasets and technology, we hope the tool can help support and accelerate adoption of decarbonized trucking solutions and infrastructure, ultimately driving more sustainable freight operations across the U.S.

[1] Danika MacDonell, Sayandeep Biswas, and Kariana Moreno Sader. Alternative Fuels and Powertrains to Decarbonize Heavy Duty Trucking. Tech. rep. MIT Climate and Sustainability Consortium White Paper. Massachusetts Institute of Technology, Sept. 2023. URL: https://hdl.handle.net/1721.1/152159.

[2] U.S. Department of Transportation: Federal Highway Administration. Freight Analysis Framework. https://ops.fhwa.dot.gov/freight/freight_analysis/faf/. Accessed on 2024-06-10. 2024.

[3] Alternative Fuels Data Center. Station Data for Alternative Fuel Corridors. https://afdc.energy.gov/corridors. Accessed: 2024-06-17.

[4] U.S. Department of Energy. Alternative Fuels Data Center: Laws and Incentives. Accessed: 2024-10-11. URL: https://afdc.energy.gov/laws.

[5] Hannah Ritchie, Pablo Rosado, and Max Roser. Data Page: CO2 emissions from transport. https://ourworldindata.org/grapher/co2-emissions-transport. Data adapted from Climate Watch. Part of the publication: “CO2 and Greenhouse Gas Emissions”. Accessed: 2025-04-01. 2023.

[6] Kariana Moreno Sader et al. “Battery electric long-haul trucking with overnight charg-ing in the United States: A comprehensive costing and emissions analysis”. In: Applied Energy 384 (2025), p. 125443. ISSN: 0306-2619. URL: https://www.sciencedirect.com/science/article/pii/S0306261925001734. DOI: https://doi.org/https://doi.org/10.1016/j.apenergy.2025.125443.

[7] Danika MacDonell et al. GeoJSON files for the MCSC Geospatial Fleet Transition Assessment and Decision Support Tool. Version v.0.1.0. Aug. 2024. URL: https://zenodo.org/records/13207716. DOI: https://doi.org/10.5281/zenodo.13207716

[8] Danika MacDonell, Brilant Kasami, and Brooke Bao. Geo-TIDE. Version v0.1.0. Feb.2025. URL: https://zenodo.org/records/14804046. DOI: https://doi.org/10.5281/zenodo.14804046.

[9] Danika MacDonell, Brilant Kasami, and Brooke Bao. MCSC Geospatial Trucking Industry Decarbonization Explorer (Geo-TIDE). https://github.com/mcsc-impact-climate/Geo-TIDE. Accessed: 2025-03-31. 2025.

[10] Danika MacDonell et al. Geo-TIDE Backend. Version v0.2.0. Feb. 2025. URL: https://zenodo.org/records/14803960. DOI: https://doi.org/10.5281/zenodo.14803960.

[11] Danika MacDonell et al. Backend to produce layers for the Geo-TIDE tool. https://github.com/mcsc-impact-climate/Geo-TIDE-backend. GitHub repository, accessed 2025-03-31. 2025.

[12] Danika MacDonell. Calibration and regional analysis of the Green group trucking model. Version v0.1.0. Aug. 2024. URL: https://zenodo.org/records/13205854. DOI: https://doi.org/10.5281/zenodo.13205854.

[13] Danika MacDonell. Calibration and regional analysis of the Green group trucking model. https://github.com/mcsc-impact-climate/Green_Trucking_Analysis. Accessed: 2025-03-31. 2024.