Portland State University. Department of Civil & Environmental Engineering
Miguel A. Figliozzi
Term of Graduation
Date of Publication
Doctor of Philosophy (Ph.D.) in Civil & Environmental Engineering
Civil and Environmental Engineering
Local transit -- Oregon -- Portland -- Statistics, Local transit -- Oregon -- Portland -- Management, Performance -- Management, Travel time (Traffic engineering)
1 online resource (xxv, 280 pages)
Performance metrics have typically focused at two main scales: a microscopic scale that focuses on specific locations, time-periods, and trips; and, a macroscopic scale that averages metrics over longer times, entire routes, and networks. When applied to entire transit systems, microscopic methodologies often have computational limitations while macroscopic methodologies ascribe artificial uniformity to non-uniform analysis areas. These limitations highlight the need for a middle approach.
This dissertation presents a mesoscopic analysis based around timepoint-segments, which are a novel application of an existing system for many transit agencies. For this research, routes are divided into a consecutive group of bus stops with one timepoint at the center. Each timepoint-segment includes all data collected in that segment during one hour of operation.
The utilized data sources are widespread and generally available to transit agencies. A methodology for merging and cleaning the data sources is proposed that: first, identifies broken data collection system to flag missing and inaccurate data; second, defines parameters of probability distributions, representative of specific locations, times, and routes, using sufficient statistics; and third, replaces flagged values with a random, but probabilistically representative value. The merged and stochastically cleaned data is aggregated by timepoint-segment to reduce subsequent computational requirements, yet maintains high granularly for statistical analysis after aggregation.
The results of linear and non-linear regressions for service durations, at and between bus stops, are presented and discussed. Independent variables were chosen based on previous published literature, but also included several updated classes of variables to provide comparisons for stop types, traffic signals, vehicle interactions, and time-of-day. The coefficients and performance of aggregated models are compared to previously published methods. The results show that factors identified at the microscopic scale (e.g. passenger movements, bus interactions at stops, travel times, travel speeds, unplanned stops, bus bunching, etc.), can be examined in aggregate without lost utility and without the heavy computation burden required to process large microscopic datasets, while also capturing double the variability in the data.
Visuals for congestion and headway performance, based on the aggregated datasets, are designed to examine transit performance along a route, between routes, and for specific segments. These visuals are a potentially useful tool for evaluating performance along routes and for identifying areas that may require a closer examination. Additionally, the methods are not computationally intensive and may be easily customized to examine specific locations, times, or feature sets.
The aggregated analysis reduces variability caused by singular atypical events, but still preserves enough detail for a robust statistical analysis. Overall, this approach improves realism, which is beneficial for evaluating the key trade-offs ridership, service, accessibility, and costs.
© 2020 Travis Bradley Glick
In Copyright. URI: http://rightsstatements.org/vocab/InC/1.0/ This Item is protected by copyright and/or related rights. You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
Glick, Travis Bradley, "Methodologies to Quantify Transit Performance Metrics at the System-Level Using High-Resolution GPS, Stop-Level, and GTFS Archived Transit Data" (2020). Dissertations and Theses. Paper 5623.