DOI: 10.25338/b87p9z
title: Optimizing bikeshare service to connect affordable housing units with transit services

description: This research studies the potential of bikeshare services to bridge the
 gap between Affordable Housing Communities (AHC) and transit services to
 improve transport accessibility for the residents. In doing so, the study
 develops an agent-based simulation optimization modeling (ABM) framework
 for the optimal design of the bikesharing station network considering
 improving accessibility as the objective. The study discusses measures of
 accessibility and uses travel times in a multi-modal network. Focusing on
 the city of Sacramento, CA, the study gathered information related to
 affordable housing communities, detailed transit services, demographic
 information, and other relevant data.  This ABM framework is used
 to run three stages of travel demand modeling: trip generation, trip
 distribution, and mode split to find the travel time differences under the
 availability of new bikesharing stations. The model is solved with a
 genetic algorithm approach. The results of the optimization and ABM- based
 simulation indicate the share of bike and bike & transit trips in
 the network under different scenarios. Key results indicate that about 60%
 of the AHCs are within 25-minute active travel time when the number of
 stations ranges from 25 to 75, and when the number of stations is
 increased to 100, most AHCs are within 40 mins of active mode distance and
 all of them are less than an hour away. In terms of accessibility, for
 example, having a larger network of stations (e.g., 100) increases by 70%
 the number of Points of Interest (for work, health, recreation, and other)
 within a 30-minute travel time. This report then provides some general
 recommendations for the planning of the bikesharing network considering
 information about destination choices as well as highlighting the past and
 current challenges in housing and transit planning. 
description: The dataset for this study was collected from various sources
 including Affordable Housing Communities Data, demographic information
 from the American Community Survey, OpenStreetMap road network, Points of
 Interest (POIs) data, General Transit Feed Specification (GTFS) data, and
 Longitudinal Employer-Household Dynamics (LEHD) data. You will need to
 download all the data from their sources except for the LEHD
 data. The data was collected from the Sacramento
 Housing and Redevelopment Agency and the office of the State Treasurer
 which maintains a list of affordable housing projects. The data was then
 processed to identify and eliminate duplicates and old projects that were
 scrapped. The final list consisted of 149 affordable housing projects
 spread across the city. Demographic information like household income,
 number of family and non-family households, and number of occupied and
 vacant housing units were sourced from the 5-year estimates of 2020
 American Community Survey data. The LEHD Origin-Destination Employment
 Statistics or LODES data were used to identify origin-destination matrix
 with census blocks with residences/homes as the origin and the census
 blocks with workplaces/ offices as the destination. Bike and Walk Network
 Data from OpenStreetMap and POIs Data from OpenStreetMap were also used in
 the study. 
description: The data is sourced from several data sources such as the
 Sacramento Housing and Redevelopment Agency, American Community Survey,
 OpenStreetMap, General Transit Feed Specification (GTFS) data, and
 Longitudinal Employer-Household Dynamics (LEHD) data. These data sources
 provide data in various formats such as CSV, XML, JSON and shapefile. You
 will need to download the data from other sources before using the
 code.  For coding, you can use python and its libraries
 such as pandas, numpy, geopandas, and osmnx to handle and process the
 data. Python is an open-source programming language that can be run on
 multiple platforms such as Windows, Mac, and Linux. The Jupyter Notebook
 is an open-source web application that allows you to create and share
 documents that contain live code, equations, visualizations, and narrative
 text. If the data files are in proprietary format, you
 can use open-source libraries and software to convert them to a more
 common format, such as csv, which can be opened with a basic text editor
 or spreadsheet program, such as Microsoft Excel or Google
 Sheets.