Revealing Long Vehicle Dynamics: A Comprehensive Journey from Visualization Tools for Heavy Vehicles to Conversion Factors and Public Transit Performance Analysis

Graduation Year

2024

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Civil and Environmental Engineering

Major Professor

Fred Mannering, Ph.D.

Co-Major Professor

Robert L. Bertini, Ph.D.

Committee Member

Pei-Sung Lin, Ph.D.

Committee Member

Ismail Uysal, Ph.D.

Committee Member

Seckin Ozkul, Ph.D.

Keywords

Big Traffic Data, CAV, GTFS, ITS, PCE

Abstract

Over the past few decades, there has been an increasing fascination with Intelligent Transportation Systems (ITS) and Connected and Autonomous Vehicle (CAV) technology, particularly in the realm of data collection and traffic management. Thanks to the possibilities unlocked by ITS and CAV technology, traffic data collection now serves a diverse array of safety and mobility applications, delivering real-time or near-real-time information for both road users and transportation professionals.

The integration of ITS and CAV technology is crucial for the creation of safer, more reliable, and sustainable transportation environments. As traffic-related data collection, analysis, and visualization form essential components of this effort, the widespread adoption of ITS and CAV technology facilitates the establishment of such environments and ensures effective monitoring of traffic flow over time and space. While contemporary road systems are predominantly occupied by passenger vehicles, the undeniable significance of long/heavy vehicles and public transit systems in facilitating the movement of goods, with their connection to the economy, and people necessitates the comprehensive analysis of various traffic data, encompassing a diverse spectrum of vehicles. This holistic approach is pivotal for gaining a nuanced understanding of roadway conditions.

This dissertation employs a multi-faceted analysis by utilizing three distinct data sources to investigate the influence of long/heavy vehicles and assess stop-level on-time performances for public transit buses. The first dataset, comprising three subcategories, is derived from dual-loop detectors situated along Interstate-5 (I-5) in the Portland/Oregon region. This dataset is utilized to analyze the effects of different classifications of heavy vehicles. Visualization techniques are applied to convert their effects into passenger vehicle equivalents. The second and third datasets, sourced from General Transit Feed Specification (GTFS) and Basic Safety Message (BSM) repositories, respectively, are utilized to identify and compare on-time performance metrics at the stop level, as well as to analyze stop-specific dwell time within a public transit system.

The findings indicate that the utilization of more compact visualization tools, capable of displaying diverse vehicles for each lane on a station basis, enables transportation practitioners to discern which lanes are predominantly occupied by different vehicle types at various time intervals. This facilitates better planning and the selection of specific time frames for rerouting vehicles requiring schedule adjustments. Additionally, the availability of ample dual-loop detector data, encompassing various vehicle types and their length information, serves to bridge the conversion gap between passenger vehicles and a range of trucks, enabling location-specific conversions for planning, design, and operational analysis. The results further highlight the effectiveness of merging high-density data sources such as General Transit Feed Specification (GTFS) and Basic Safety Message (BSM) in evaluating the quality of on-time bus services. This integration facilitates the adjustment of stop-specific bus schedules and contributes to providing more reliable services.

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