This data supports the research project Bi-objective Optimization for Battery Electric Bus Deployment Considering Cost and Environmental Equity and a final report published on NITC’s website.
Dataset collected through multiple sources and organized into different formats including CSV format, JSON format, shapefile and code repository.
Context: The research project develops a bi-objective model that aims to help transit agencies to optimally deploy BEB while considering both capital investment and environmental equity. The unique spatio-temporal characteristic of BEB system, charging limitations (on-route and in-depot charging), and operational constraints are also considered and incorporated into the model

Golub, Aaron

Liu, Xiaoyue Cathy

Wei, Ran

Zhou, Yirong

English

PDXScholar (Portland State University)

Portland State University PDXScholar TREC Datasets and Databases Transportation Research and Education Center (TREC) 2021 Data Files: Bi-Objective Optimization for Battery Electric Bus Deployment Considering Cost and Environmental Equity Xiaoyue Cathy Liu The University of Utah Yirong Zhou The University of Utah Ran Wei University of California, Riverside Aaron Golub Portland State University, agolub@pdx.edu Follow this and additional works at: https://pdxscholar.library.pdx.edu/trec_data Let us know how access to this document benefits you. Recommended Citation Liu, X., Zhou, Y., Wei, R., & Golub, A. Data Files: Bi-objective Optimization for Battery Electric Bus Deployment Considering Cost and Environmental Equity. NITC-RR- 1222. Portland, OR: Transportation Research and Education Center (TREC), 2021 https://doi.org/10.15760/TREC_datasets.10 This Dataset is brought to you for free and open access. It has been accepted for inclusion in TREC Datasets and Databases by an authorized administrator of PDXScholar. Please contact us if we can make this document more accessible: pdxscholar@pdx.edu. This documentation file was generated on 2021-01-27 by Yirong Zhou  ------------------- # GENERAL INFORMATION-------------------   1. Title The Metadata for Bi-objective Optimization for Battery Electric Bus Deployment Considering Cost and Environmental Equity  2. Creator Information Name: Xiaoyue Cathy Liu Institution: University of Utah College, School or Department: Department of Civil Environmental Engineering Email: cathy.liu@utah.edu   Name:  Yirong Zhou Institution: University of Utah College, School or Department: Department of Civil Environmental Engineering Email: yirong.zhou@utah.edu   Name:  Ran Wei Institution: University of California, Riverside College, School or Department: School of Public Policy Email: ranwei@ucr.edu   Name:  Aaron Golub Institution: Portland State University College, School or Department: Toulan School of Urban Studies and Planning Email: agolub@pdx.edu   3. Contact Information  Name: Xiaoyue Cathy Liu Institution: University of Utah College, School or Department: Department of Civil Environmental Engineering Email: cathy.liu@utah.edu   ------------------- CONTEXTUAL INFORMATION-------------------   1. Abstract for the dataset  The dataset contains 9 seperate categories of data, all of which are used to generate, analyze or visualize the results of the project. The dataset is collected through multiple sources and organized into different formats including CSV format, JSON format, shapefile and code repository.   2. Context of the research project that this dataset was collected for. The research project develops a bi-objective model that aims to help transit agencies to optimally deploy BEB while considering both capital investment and environmental equity. The unique spatio-temporal characteristic of BEB system, charging limitations (on-route and in-depot charging), and operational constraints are also considered and incorporated into the model.  3. Date of data collection: The data was collected during 2019-08-01 to 2020-04-30.  4. Last modification date Date dataset was last modified on 2021-01-10.  5. Funding sources that supported the collection of the data: This project was funded by the National Institute for Transportation and Communities (NITC; grant number 1222) a U.S. DOT University Transportation Center.   -------------------------- SHARING/ACCESS INFORMATION--------------------------1. Publications related to the dataset: A. Y. Zhou, X. C. Liu, R. Wei and A. Golub, "Bi-Objective Optimization for Battery Electric Bus Deployment Considering Cost and Environmental Equity," in IEEE Transactions on Intelligent Transportation Systems, doi: 10.1109/TITS.2020.3043687.  B. Ran Wei, Xiaoyue Liu, Yi Ou, S. Kiavash Fayyaz, "Optimizing the spatio-temporal deployment of battery electric bus system," in Journal of Transport Geography, doi：10.1016/j.jtrangeo.2018.03.013.  --------------------- DATA & FILE OVERVIEW---------------------   File List   1. Filename: BusRoutes_UTA      Containing subfiles: YES       Source: Utah Transit Authority (UTA)       Summary: The folder contains the shapefile of bus routes of UTA in year 2016. Shapefile format consists of files with common filename prefix, stored in the same directory.       Software used for processing the data: ArcMap       Subfiles:  A. BusRoutes_UTA.cpg   Description: Specifing the code page (only for .dbf) for identifying the character encoding to be used.   B. BusRoutes_UTA.dbf   Description: Attribute format; columnar attributes for each shape, in dBase IV format.   C. BusRoutes_UTA.prj   Description: Projection description, using a well-known text representation of coordinate reference systems.   D. BusRoutes_UTA.sbn   Description: A spatial index of the features.   E. BusRoutes_UTA.sbx   Description: A spatial index of the features.   F. BusRoutes_UTA.shp   Description: Shape format; the feature geometry itself.   G. BusRoutes_UTA.shp.xml   Description: Geospatial metadata in XML format, such as ISO 19115 or other XML schema   H. BusRoutes_UTA.shx   Description: Shape index format; a positional index of the feature geometry to allow seeking forwards and backwards quickly.                        2. Filename: BusStops_UTA      Containing subfiles: YES       Source: Utah Transit Authority (UTA)       Summary: The folder contains the shapefile of bus stops of UTA in year 2016. Shapefile format consists of files with common filename prefix, stored in the same directory.       Software used for processing the data: ArcMap       Subfiles:   A. BusStops_UTA.cpg   Description: Specifing the code page (only for .dbf) for identifying the character encoding to be used.   B. BusStops_UTA.dbf   Description: Attribute format; columnar attributes for each shape, in dBase IV format.   C. BusStops_UTA.prj   Description: Projection description, using a well-known text representation of coordinate reference systems.   D. BusStops_UTA.sbn   Description: A spatial index of the features.   E. BusStops_UTA.sbx   Description: A spatial index of the features.    F. BusStops_UTA.shp   Description: Shape format; the feature geometry itself.   G. BusStops_UTA.shp.xml   Description: Geospatial metadata in XML format, such as ISO 19115 or other XML schema   H. BusStops_UTA.shx   Description: Shape index format; a positional index of the feature geometry to allow seeking forwards and backwards quickly.          3. Filename: UTA Runcut File  Aug2016.xlsx       Containing subfiles: NO       Source: Utah Transit Authority (UTA)       Summary: The file contains network information of UTA buses in year 2016.       Software or Language used for processing the data: Python     4. Filename: TAZ       Containing subfiles: YES       Source: WFRC       Summary: The folder contains the shapefile of Traffic Analysis Zone in the Salt Lake Metropolitan area.        Software or Language used for processing the data: ArcMap       Subfiles:   A. TAZ.cpg   Description: Specifing the code page (only for .dbf) for identifying the character encoding to be used.   B. TAZ.dbf   Description: Attribute format; columnar attributes for each shape, in dBase IV format.   C. TAZ.prj   Description: Projection description, using a well-known text representation of coordinate reference systems.   D. TAZ.sbn   Description: A spatial index of the features.   E. TAZ.sbx   Description: A spatial index of the features.    F. TAZ.shp   Description: Shape format; the feature geometry itself.   G. TAZ.xml   Description: Geospatial metadata in XML format, such as ISO 19115 or other XML schema   H. TAZ.shx   Description: Shape index format; a positional index of the feature geometry to allow seeking forwards and backwards quickly.      5. Filename: Marginal_Income.xlsx       Containing subfiles: NO       Source: WFRC       Summary: The file contains the size of four income group at TAZ level.       Software or Language used for processing the data: Python 2.x     6. Filename: SE_File_v83_SE19_Net19.xlsx       Containing subfiles: NO       Source: WFRC       Summary: The file contains the household size, household population, and employment level at TAZ level.       Software or Language used for processing the data: Python 2.x     7. Filename: Pollutant Concentration.xlsx       Containing subfiles: NO       Source: https://www2.purpleair.com/       Summary: The file contains readings of pollutant concentration extracted from PurpleAir Sensors from Oct 1st to Oct 14th, 2019.        Software or Language used for processing the data: Python 2.x     8. Filename: Code for Optimization       Containing subfiles: YES       Source: Yirong Zhou       Summary: The folder is the repository of code used for implementing the optimization framework.        Software or Language used for processing the data: Python 2.x       Subfiles:   A. ele_bus.py   Description: This file contains the major class defined in order to store necessary information.  B. ele_glpk_solve.py   Description: This file sotres the function to call GLPK solver.  C. ele_gui.py   Description: This file creates a Graphical User Interface for interaction, dynamic input, and multi-threads.  D. fileHandler.py   Description: This file reads in and parses all sources of data.      9. Filename: Code for Visualization       Containing subfiles: YES       Source: Yirong Zhou       Summary: The file contains readings of pollutant concentration extracted from PurpleAir Sensors from Oct 1st to Oct 14th, 2019.        Software or Language used for processing the data: Python 2.x       Subfiles:   A. vis.html    Description: The main HTML file.  B. chart.js   Description: The javascript file that defines dropdown box and proccesses data.  C. firstView.js   Description: The javascript file that generates the first view of the visualization.  D. secondView.js   Description: The javascript file that generates the second view of the visualization.  E. thirdView.js   Description: The javascript file that generates the third view of the visualization.  F. styles.css   Description: The CSS file that styles all the elements.  G. screencast.mp4   Description: A screen cast that demonstrates how to use the visulaization.  H. data   Description; A folder contains the necessary data of deployment plans, bus stops, bus routes, and information of TAZ.   ----------------------------------------- TABULAR DATA-SPECIFIC INFORMATION FOR: 1-B. BusRoutes_UTA.dbf-----------------------------------------   1. Number of variables: 5  2. Number of cases/rows:  114  3. Variable List     A. Name: LineAbbr        Description: Abbreviations for bus lines.  Nominal                           B. Name: LineName        Description: Names of bus lines.  Nominal      C. Name: Service        Description: Category of service of bus lines.  Nominal, including local, express, BRT, fast bus, shuttle, ski, flex.      D. Name: Frequency        Description: Frequency of bus lines.  Ratio      E. Name: Shape_Leng        Description: Length of the bus routes.  Ratio, unit: foot   ----------------------------------------- TABULAR DATA-SPECIFIC INFORMATION FOR: 2-B. BusStops_UTA.dbf-----------------------------------------   1. Number of variables: 4  2. Number of cases/rows:  5987  3. Variable List     A. Name: StopId        Description: Unique identifier for bus stops.  Nominal                           B. Name: StopName        Description: The comlete names of bus stops.  Nominal      C. Name: StreetNum        Description: The number of the streets the stops are on.  Nominal      D. Name: OnStreet        Description: The name of the streets the stops are on.  Nominal   ----------------------------------------- TABULAR DATA-SPECIFIC INFORMATION FOR: 3. UTA Runcut File  Aug2016.xlsx-----------------------------------------   1. Number of variables: 9  2. Number of cases/rows:  6878  3. Variable List     A. Name: LineAbbr        Description: Abbreviations for bus lines.  Nominal                           B. Name: ServiceName        Description: Weekday or Weekend service.  Nominal, including WEEKDAY, SATURDAY, and SUNDAY.      C. Name: DirectionName        Description: The name of the destination stop.  Nominal      D. Name: block_num        Description: The unique identifier for different buses.  Nominal      E. Name: trip_id        Description: The unique identifier for different trips.  Nominal      F. Name: from_stop        Description: The name of the origin stop.  Nominal      G. Name: FromTime        Description: The time when buses begin the trip.  Interval      H. Name: to_stop        Description: The name of the destination stop.  Nominal      I. Name: ToTime        Description: The time when buses end the trip.  Interval   ----------------------------------------- TABULAR DATA-SPECIFIC INFORMATION FOR: 4-B. TAZ.dbf-----------------------------------------   1. Number of variables: 2  2. Number of cases/rows:  2881  3. Variable List     A. Name: CO_TAZID        Description: Unique identifier for TAZs.  Nominal                          B. Name: AREA        Description: The area of the TAZ.  Ratio, Unit: squre foot   ----------------------------------------- TABULAR DATA-SPECIFIC INFORMATION FOR: 5. Marginal_Income.xlsx-----------------------------------------   1. Number of variables: 6  2. Number of cases/rows:  2881  3. Variable List     A. Name: CO_TAZID        Description: Unique identifier for TAZs.  Nominal                           B. Name: INC1        Description: The number of households in income group 1 ($0 - $34,999).  Ratio      C. Name: INC2        Description: The number of households in income group 2 ($35,000 - $49,000).  Ratio      D. Name: INC3        Description: The number of households in income group 3 ($50,000 - $99,999).  Ratio      E. Name: INC4        Description: The number of households in income group 4 (over $100,000).  Ratio      F. Name: TOTHH        Description: The number of total households.  Ratio   ----------------------------------------- TABULAR DATA-SPECIFIC INFORMATION FOR: 6. SE_File_v83_SE19_Net19.xlsx-----------------------------------------   1. Number of variables: 5  2. Number of cases/rows:  2881  3. Variable List     A. Name: CO_TAZID        Description: Unique identifier for TAZs.  Nominal                           B. Name: TOTHH        Description: The number of total households.  Ratio      C. Name: HHPOP        Description: The total household population.  Ratio      D. Name: HHSIZE        Description: The average household size.  Ratio      E. Name: TOTEMP        Description: The number of total employment.  Ratio   ----------------------------------------- TABULAR DATA-SPECIFIC INFORMATION FOR: 7. Pollutant Concentration.xlsx-----------------------------------------   1. Number of variables: 3  2. Number of cases/rows:  438  3. Variable List     A. Name: lat        Description: Lattitude of the PurpleAir Sensor.  Interval                          B. Name: lon        Description: Longitude of the PurpleAir Sensor.  Interval      C. Name: PM2.5_ATM_ug/m3        Description: The concentration of PM 2.5.  ratio, unit: ug/m3 

Data Files: Bi-Objective Optimization for Battery Electric Bus Deployment Considering Cost and Environmental Equity

https://pdxscholar.library.pdx.edu/cgi/viewcontent.cgi?article=1011&amp;context=trec_data

Data Files: Bi-Objective Optimization for Battery Electric Bus Deployment Considering Cost and Environmental Equity

Abstract

Similar works

Full text

Available Versions

PDXScholar (Portland State University)