This project is one part of MUSA 801 course in the Master of Urban and Spatial Analysis in University of Pennsylvania, with the supervision of Ken Steif, Michael Fichman, Matthew Harris. Many thanks to them for gracefully providing insightful suggestion during five months. Special thanks to Jonathan Hartman from Cap Metro who provided the ridership data this project is using. Thanks to Kimberly Feldbauer from AECOM’s Austin office who provided insights on transit planning in Austin.

Visit MUSA 801 website for more projects in this course: https://pennmusa.github.io/MUSA_801.io/

1. Introduction

1.1 Background

While evaluating the performance of a transit system, ridership has been an extremely important aspect helping the transportation planning development. Even in the challenging time brought by the pandemic, when ridership decrease becomes the nightmare for every transit agency in the US, it is still critical to explore transit ridership in relation to built environment, land use, demographics and system dynamics. Using Automatic Passenger Counter data provided by the sensors, Austin bus ridership data offers a great chance in helping planners to assess the system performance with predictions to backup better decisions. The following image shows how the data is collected on each bus. source

1.2 Use Case

Austin is a city full of development opportunity and a city growing vibrantly. The city’s demand for bus system also keeps changing with the development. In Jun 2018, Austin rolled out a large-scale bus system redesign, CapRemap. Cap Remap adjusted the transit network according to internal analysis and community outreach and aims to provide a more frequent, more reliable, and better connected bus system. This project brings an opportunity to understand what factors influence bus ridership.

Given the renewed interest in bus transit in US cities, such as Austin, there is an opportunity to streamline the bus planning process using modern data science methods. Currently, cities have to gather all the information, such as land use, built environment, demographics etc., from different sources, to gain understanding of bus ridership change in the future. This method is usually time consuming and requires a lot of human resources. Oftentimes, cities have to outsource those analysis to third parties, which inevitably leads to higher project cost. The goal of this project, therefore, is to present a scenario planning tool for planners to test how changes in local land uses and characteristics of bus routes predict bus ridership. If such a predictive model proves robust, planners can use it to evaluate a series of possible pictures regarding the development of different land use and change of bus routes in the future and make strategic decisions efficiently in Austin.

1.3 What Data Are We Using?

The ridership data collected by sensors is calculated into a monthly average format for each stop. This data is going to be our dependent variable in this project.

The dataset also provides info on route characteristics, such as route types and high ridership lines, which we call hotlines.