One of the biggest responsibilities of any city is to provide means of transportation to its residents and visitors. Traditionally “providing transportation” meant maintaining a road network, and perhaps in addition offering some sort of metro and/or bus network for its residents to utilize. In recent years, numerous other modes of transportation have emerged, giving rise to more choice by consumers. This paper explores over 300,000 bikeshare journeys to draw conclusions on when a consumer should, and should not, utilize bike share. The paper concludes by discovering that journeys under 1.5 miles can be done fastest with a bike. Other conclusions are that while trips in the center of a city can generally be done much faster on a bike, trips several miles away from the city center can generally be done in comparable time regardless of mode of transportation.
This project, done in collaboration with Tom Bain, served as my final project for the Social and Technlogical Networks course at the University of Edinburgh.
This repo contains various data-sets used to calculate trip duration between two locations in London. Each trip's duration between point A and B (taken randomly from a set) is calculted as if the ride had been conducted via bike-share, uber, AND Tube. Various calculations-and-experiments were conducted and can be reviewed within the provided iPython file. Helper scripts used throughout the research phase can be found as well.
These experiments were used to generate the findings-graphs. I then draw conclusions in my final [report](/STN Project - Submitted.pdf) based on these experiments. Findings are split into two categories: proximity (prox) and range, as well as the specified interval. Each edge that exists is also assigned a color based on the optimal mode of transport in that experiment. The colors are:
| Mode | Color |
|---|---|
| Bike | blue |
| Train | green |
| Uber | orange |
To run the entirely of the provided code, API tokens for Google Maps and Uber are needed. All token I had used previously have been revoked. Data is provided and with (minimal) tweaking should be sufficient to calculate your own results. See the iPython file for more information.
See the provided iPython file for attribution.