Introduction

In this project, I try to understand the relationship between demographic characters and use of public transit in the Greater Vancouver Area, and model how access to transit services and infrastructure impacts transit use.

Firstly, I gathered General Transit Feed Specification (GTFS) data for Greater Vancouver, and Canada's 2016 census. I cleaned both datasets and selected relevant variables.

Secondly, I extracted information about people's access to, and usage of public transportation across Greater Vancouver's neighborhoods. I also conducted an overview of Vancouver's transit service, and local residents' demographic characters in general.

Thirdly, I built the data table for machine learning modeling, and applied LASSO and random forest models. I concluded that access to transit services is a key determinant of residents' use of public transportation. I identified areas where increased transit services will lead to the most increase in transit usage.

Report

The final report can be found here, and a Jupyter Notebook for presentation can be found here.

Other posts in this series include data sources, key variables and machine learning modeling.

Usage

Using Docker

note - the instructions in this section also depends on running this in a unix shell (e.g., terminal or Git Bash)

To replicate the analysis, install Docker. Then clone this GitHub repository and run the following commands at the command line/terminal from the root directory of this project:

docker run --rm -it -v /$(pwd):/home zibowangkangyu/transit_van /bin/bash
cd home

Download census data

Ideally, we would want to use an API to directly download Canada's 2016 census data. However, the volumne of data required by this analysis, which goes down to the Dissemination Area (DA) level, exceeds what available free APIs can download. Therefore, I have stored the zipped dataset in my personal OneDrive folder for you to download.

wget --no-check-certificate -O Census.zip "https://onedrive.live.com/download?cid=FD70C8A06BE32527&resid=FD70C8A06BE32527%21701526&authkey=ADw5q9yT-Y-RX0A"

Download GTFS data

wget --no-check-certificate -O TL_data.zip https://transitfeeds.com/p/translink-vancouver/29/20200606/download    

Unzip downloaded data

python src/Unzip_downloads.py

Data cleaning

python src/GTFS_cleaning.py
python src/Census_reading.py

Exploratory analyses

python src/Temporal_analysis.py
python src/Access_analysis.py
python src/Usage_analysis.py

Modeling and Analyses

python src/Preprocessing.py
python src/Modeling.py
jupyter nbconvert --to HTML --execute Report.ipynb --ExecutePreprocessor.timeout=600

License

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <https://www.gnu.org/licenses/>.

Dependencies

• Python 3.8.3 and Python packages:
  • altair=4.1.0
  • contextily=1.0.0
  • dbfread=2.0.7
  • geopandas=0.8.1
  • matplotlib=3.3.1
  • numpy=1.19.1
  • pandas=1.1.1
  • re=2.2.1
  • sklearn=0.23.2
  • shap=0.37.0
  • shapely=1.7.1
  • tabulate=0.8.7