Establishing baseline travel patterns from smart-phone and spatial data

Abstract

Investment in public transit infrastructure and services is essential to providing effective transportation alternatives, and it is important to monitor the progress of key performance indicators (KPIs) to ensure goals of major transit projects are being achieved. These key performance indicators provide replicable measurements related to different aspects of transportation and mobility. Through this thesis, data were collected and analyzed in relation to a set of key performance indicators in the context of Downtown Kitchener in the Region of Waterloo with the implementation of the ION Light Rail system to assess the current state of Downtown Kitchener, and its progression toward goals outlined in the Region of Waterloo’s Community Building Strategy and the Kitchener Planning Around Rapid Transit Stations plan. Data related to transit ridership, modal splits, and active transportation networks were summarized from a collection of datasets to establish a baseline of data prior to the introduction of light rail. This thesis investigated the process to collect and analyze these types of data through smart-phone GPS data collection during February and March of 2017 and Python scripts, alongside demographic surveys and other datasets for Downtown Kitchener. Overall, a sample of baseline indicators have been gathered and assessed for Downtown Kitchener that demonstrated a high propensity for transit and active transportation usage, supported by public policy, with some exceptions or areas of improvement. The process taken in this thesis may be applied to additional areas throughout the Region of Waterloo prior to and following commencement of ION Light Rail operation.