Demand Learning and Supply Optimization for Last-Mile Transportation in Disadvantaged Neighborhoods [supporting dataset]

Details

• Alternative Title:
  Commuting Distances and Times under Different Transportation Modes for U.S. Households [dataset title]
• Creators:
  Zhang, Peter ; Hao, Hao ; Wang, Hai
• Corporate Creators:
  Mobility21, Carnegie Mellon University
• Corporate Contributors:
  United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology ; United States. Department of Transportation. University Transportation Centers (UTC) Program
• Subject/TRT Terms:
  Costs Data Files Last-mile Neighborhoods Optimization Policy Public Transit Routes Transportation Disadvantaged Persons Waiting Time
• Resource Type:
  Dataset
• Geographical Coverage:
  Pennsylvania;United States
• Corporate Publisher:
  Mobility21, Carnegie Mellon University
• Abstract:
  Efficient and fair transportation planning creates opportunities and equity for jobs, health care, and education. Therefore, data consolidation for transportation systems provides basis for evidence based policies. In this work, we construct a dataset that documents home-to-job commuting time and distance information for the 100 most populated U.S. urban areas. Our dataset builds on the U.S. Census Bureau's Longitudinal Employer-Household Dynamics Dataset, which provides origin (home) and destination (job) location information for households. For these origin-destination (OD) pairs, we derive commuting time and distance information under different travel modes, each a combination of walking, public transit, and ridesourcing. We construct data under different modes so policymakers and researchers have information about alternatives and can perform what-if analysis. Towards the end of this data sheet, we document a sample use case to illustrate this goal.
  
  The total size of the described zip file is 29.85KB. The ZIP file for this dataset contains files with the following file extensions: The .csv, Comma Separated Value, file is a simple format that is designed for a database table and supported by many applications. CSV files can be viewed in a spreadsheet software such as Excel or in a text file reader such as Notepad. PDF files can be view in the default web browser or any PDF software. MD files can be view in any text document reader, such as Notepad. Gitkeep files are used so that empty folders will be tracked via GIT and file structures will be retained even when the folders contain no files (for more information on .gitkeep files and software, please visit https://www.file-extensions.org/gitignore-file-extension).
  
  
• Content Notes:
  National Transportation Library (NTL) Curation Note: As this dataset is preserved in a repository outside U.S. DOT control, as allowed by the U.S. DOT’s Public Access Plan (https://doi.org/10.21949/1503647) Section 7.4.2 Data, the NTL staff has performed NO additional curation actions on this dataset. The current level of dataset documentation is the responsibility of the dataset creator. NTL staff last accessed this dataset at https://github.com/peteryz/employment-od on 2022-10-16. If, in the future, you have trouble accessing this dataset at the host repository, please email NTLDataCurator@dot.gov describing your problem. NTL staff will do its best to assist you at that time.
• Format:
  ZIP
• Funding:
  69A3551747111
• Collection(s):
  University Transportation Centers
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/64722/dot_64722_DS1.bin
• File Type:
  [BIN ]