Estimating the Energy and Emissions Impacts of a Commuter Rail System in North Carolina via Spreadsheet Modeling
In the United States, transportation sector is a significant contributor to greenhouse gas emissions. In North Carolina's Triangle region, the population growth that increases vehicular presence on the roads exacerbates this issue. To address climate challenges, promoting public transit emerges as a viable solution while the proposed commuter rail projects offer promising alternatives. Yet, the integration of rail systems requires careful consideration of energy and emissions impacts. To support strategic planning for commuter rail projects in the Triangle region, we built a user-friendly spreadsheet model to assess the energy and emission impacts of various trainset, fuel, and operation scenarios, focusing on the recent proposed Greater Triangle Commuter Rail (GTCR) service running between West Durham and Auburn, NC. Our granular model provides actionable insights for stakeholders to make informed decisions, facilitating sustainable transportation development.
This project is originally a master research project at Nicholas School of the Environment, Duke University. The authors are no longer affiliated to the school but all the intellectual property are shared by the license of CC BY-SA 4.0. We store the most of the data, all the collectionand analytical scripts and reference literature in the repo. The full analysis report could be found at Duke Library. If anyone like the see the full raw data, please contact Applied Data Research Institute.
This directory is structured as follows: (1) Data, (2) Scripts, (3) Literature, (4) Documents, and (5) Deliverables (Figure 1).
Figure 1: File structure for the primary folders.
The data folder is for spatial and tabular data. It is divided into source files – those which we collect – and processed files – those which we produce/derive (Figure 2).
Figure 2: Proposed data folder structure.
There is also a metadata Excel spreadsheet that tracks the location of files within the directory.
The scripts folder is for code scripts (e.g., Python), tools (e.g., Excel models), or project files (e.g., .APRX) used for generating or processing data (Figure 3).
Figure 3: Proposed scripts folder structure.
The literature folder is for organizing external articles, reports, and other documents. This folder is different from the documents folder in that its contents are not produced by the project team.
The documents folder is for all documents generated for or by the project team, not including deliverables. I recommend subfolders for meeting notes, important correspondences (e.g., memoranda and important emails), action items (e.g., work assignments), and other similar documents. Below is a visual example of what this could look like (Figure 4).
Figure 4: Proposed documents folder structure.
File naming conventions are important. I generally recommend including dates in front of document names – formatted YYYY-MM-DD – so that an alphabetical sort results in a chronological sort. This naming convention makes more sense in some places and less sense in others. It is ideal for organizing meeting minutes and memoranda, but it might be confusing if used on a shared working document. To reduce confusion on shared documents, you could use a non-dated file (e.g., FileName.docx) as the active working document but then make a copy every week or so that you archive (e.g., 20230906_FileName.docx).
The deliverables folder is for draft and completed versions of project deliverables.
Estimating the Energy and Emissions Impacts of a Commuter Rail System in North Carolina via Spreadsheet Modeling © 2024 by Chia-Shen Tsai, Xinyi Wen, Zhengqi Jiao, Miaojun Pang is licensed under CC BY-SA 4.0