ECE143 Group 3 Project

Effectiveness of Public Transportation

Spring 2019 @ UCSD

Description

Public transportation is an economical and eco-friendly way to travel. More cities should be investing in public transport infrastructure.

This project tries to analyse:

• Annual ridership trends of public transportation.
• Fuel savings due to usage of public transport.
• Correlation between public transport ridership and road accidents.
• Correlation between public transport ridership and car sales.
• US county level public transport vehicle availability.

For the choropleth plots, we are using a native bokeh choropleth implementation, for which bokeh sampledata needs to be installed. Please install it by typing in terminal: bokeh sampledata

Code Organization

Requirements/Dependencies

1. bokeh
2. bokeh sampledata
3. matplotlib
4. seaborn
5. plotly
6. numpy

Instructions

1. Please use branch final. Select branch using git checkout final.
2. Root > Master The folder Master contains all scripts and some dataset files.
3. The repo contains dataset files totalling 9.73 MB.
4. Instead of using the provided Jupyter notebook to view plots, we recommend using the links provided in the section below to view all the plots. Mitigates the need to compile code to view plots.

Running the Code

Scripts

1. import_xls.py contains apta_utils() class to obtain data from APTA sources, and visualization (correlations, choropleths, fuel saved) for the data.
2. graph_county_properties.py - Gives a bar graph of all counties for the required property.
3. unlinked_trips_us.py - Returns plot for the number of unlinked trips in public transport in US over the years.
4. ridership_US_1922.py - Returns the plot of changing transit ridership for major modes of public transport
5. read_pdf_HK_env.py - Returns the electricity consumption and ridership data in HK in form of a dataframe from which correlation plot can be obtained.
6. plot_for_changing_ridership_trend_us.py - Returns the public transport ridership change in US over the years plot.
7. passenger_miles.py - Returns the US Passenger miles over the years plot.
8. nyvshk_traffic.py - Returns the NY vs HK Traffic Accidents plot.
9. hk_correlation.py - Returns the HK Ridership vs Traffic Accidents correlation plot.
10. employee_compensation.py - Return the public transportation employee in US compensation plots.
11. Group-3_Project_Effectiveness of Public Transportation.ipynb - Contains matplotlib plots

Plots

US Annual Trends

• Ridership
• Revenue Miles
• Employees
• Fuel Savings

Correlations

• Ridership vs Motor Vehicle Accident Deaths
• Ridership vs Personal Motor Vehicle Sales

US County Trends

• Vehicles in Service Overview
• Vehicles in Service
• Unlinked Trips

Correlations

• Ridership vs Traffic Accidents
• Ridership vs Transportation Electricity Consumption

New York vs Hong Kong

• NY vs HK Ridership
• NY vs HK Transportation Accidents

Project by:

• Himanshu Gupta

• Mingkun Yin

• Rajat Sethi

• Rohit Kumar

Project Presentation

Project Proposal

Datasets

1. *Fact_Book_Appendix_B* files contain US county wise data for transportation agencies.
2. abc-page-17-table-1.csv - File containing electricity consumption of public transport over the years in HK.
3. table21.xls - HK public transport ridership data.
4. NY_crashes.csv - NY traffic accidents data.
5. table11.xls - HK traffic accidents and avg. daily ridership data.
6. 123.pdf - HK public transport environment report.
7. 2019-APTA-Fact-Book-Appendix-A.xlsx - US APTA published ridership, revenue miles, employee compensation, etc dataset for public transport employees.
8. b.csv - HK Ridership data (annually).
9. a.csv - NY Ridership data(annually).
10. NY.csv - NY revenue miles, unlinked trips, etc data.