Data From: How Will Autonomous Vehicles Change Local Government Budgeting and Finance? Case Studies of On-Street Parking, Curb Management, and Solid Waste Collection

Authors

Benjamin Y. Clark, University of OregonFollow

Document Type

Dataset

Publication Date

2019

Subjects

Transportation -- United States -- Planning, Transportation and state -- Decision making -- Analysis

Abstract

Datafiles

Parking Occupancy Datasets: These CSV (comma separated values) datafiles include parking occupancy data from the Seattle Department of Transportation (SDOT). Each of the 11 datafiles indicates the time period that it includes, with dates ranging from January 1, 2012 to December 31, 2017. Each file contains 6 months of data on block-by-block occupancy of paid parking. The number of available spaces were calculated from this dataset in addition to the median and mean occupancy rates for each Tract. City data documentation indicates the following about the dataset: “The City of Seattle has created an on-street paid parking occupancy data set and is providing access to this data set for public use for research and entrepreneurial purposes under the City’s Open Data Program. In 2017-2018, the Seattle Information Technology and Seattle Department of Transportation (SDOT) worked collaboratively on a project to determine paid occupancy compliance in the city’s paid parking system. The City is providing data to encourage researchers and programmers for analysis and to develop applications that might help improve parking management conditions. SDOT’s interest is to make better parking decisions based on data and to help people access parking information so that they can find a space easier and spend less time circling, stuck in traffic.” These datasets were provided to the author upon request from the Seattle Department of Transportation. These 11 files together are about 500 GB is size.

These datafiles included are:

• PaidParkingOccupancyData_2012_01to2012_06.csv
• PaidParkingOccupancyData_2012_07to2012_12.csv
• PaidParkingOccupancyData_2013_01to2013_06.csv
• PaidParkingOccupancyData_2013_07to2013_12.csv
• PaidParkingOccupancyData_2014_01to2014_06.csv
• PaidParkingOccupancyData_2014_07to2014_12.csv
• PaidParkingOccupancyData_2015_01to2015_06.csv
• PaidParkingOccupancyData_2015_07to2015_12.csv
• PaidParkingOccupancyData_2016_01to2016_06.csv
• PaidParkingOccupancyData_2016_07to2016_12.csv
• PaidParkingOccupancyData_2017_01to2017_06.csv
• PaidParkingOccupancyData_2017_07to2017_12.csv

Parking Transaction Data: This dataset includes all of the parking transaction data that were used in the analysis to calculate average parking rates paid by in each Census Tract for each specified time period. These data were downloaded originally using the city’s API feed. A full metadata document describing each variable in the dataset is provide by the city here: http://wwwqa.seattle.gov/Documents/Departments/SDOT/ParkingProgram/data/SeattlePaidTransactMetadata.pdf

• transactions_seattle.csv

American Community Survey (ACS) Data: ACS data used in this study are car ownership and median household income by Census Tract. The five-year averages were used for all study years. These data were downloaded from the US Census Bureau’s American FactFinder.

Number of cars in a Census Tract: The specific variable used in these files was “Estimate; Aggregate number of vehicles available.” These data are available at the Census Tract level for each year of the study. The files below are all CSV files. The number immediately following the “ACS_” at the start of the file name is the year that the data represent.

• ACS_12_5YR_B25046_with_ann.csv
• ACS_13_5YR_B25046_with_ann.csv
• ACS_14_5YR_B25046_with_ann.csv
• ACS_15_5YR_B25046_with_ann.csv
• ACS_16_5YR_B25046_with_ann.csv

Median Household Income in a Census Tract: The specific variable used in these files was “Estimate; Median household income in the past 12 months.” These data are available at the Census Tract level for each year of the study. The files below are all CSV files. The number immediately following the “ACS_” at the start of the file name is the year that the data represent.

• ACS_12_5YR_B19013_with_ann.csv
• ACS_13_5YR_B19013_with_ann.csv
• ACS_14_5YR_B19013_with_ann.csv
• ACS_15_5YR_B19013_with_ann.csv
• ACS_16_5YR_B19013_with_ann.csv

State of Washington Data: Beer, wine, and liquor license data were collected from the State of Washington’s website. Each license holder’s address in Seattle was geolocated within a Census Tract by year. The variable, as presented in the models, is the aggregate number of establishments with a license in the spatial-temporal form of the data overall. These data are stored in a Microsoft Excel file format.

• On Premise_beerwinespirits.xlsx

Fuel Price Data: The data on the average cost of all grades of retail gasoline were sourced from the U.S. Energy Information Administration (2019). These data are aggregated to the month of each year in the sample. These data are stored in a CVS format.

• fuel_cost.csv

TNC Data: data from TNCs were provided directly to the researchers and are protected by data sharing agreements between the companies and the University of Oregon. According to these agreements the researchers cannot share these data. Should other researchers with to access these data, they would need to enter into data sharing agreements of their own with Uber and Lyft.

Description

These data support a final report published on NITC’s website “How Will Autonomous Vehicles Change Local Government Budgeting and Finance? Case Studies of On-Street Parking, Curb Management, and Solid Waste Collection” (2019). https://dx.doi.org/10.15760/trec.217

Rights

This work is marked with CC0 1.0 Universal

DOI

10.15760/TREC_datasets.03

Persistent Identifier

https://archives.pdx.edu/ds/psu/28703

Recommended Citation

Clark, Benjamin Y. Data From: How Will Autonomous Vehicles Change Local Government Budgeting and Finance? Case Studies of On-Street Parking, Curb Management, and Solid Waste Collection. NITC-SS-1174. Portland, OR: Transportation Research and Education Center (TREC), 2019. [Dataset]. https://doi.org/10.15760/TREC_datasets.03