All ETDs from UAB

Advisory Committee Chair

Virginia P Sisiopiku

Advisory Committee Members

Akhlaque Haque

Andrew J Sullivan

Document Type

Thesis

Date of Award

2018

Degree Name by School

Master of Science in Civil Engineering (MSCE) School of Engineering

Abstract

Transportation system performance measurements are used to evaluate the operating conditions of local and regional transportation systems and determine if they meet national performance goals. Performance measurement is an integral part of the congestion management process and assists transportation agencies to set programmatic and funding priorities and make policy decisions. Transportation system performance measures are currently derived from various sources such as point sensors, automatic vehicle matching technologies, third party vendors, incident and work zone databases, weather databases, video analytics, high-resolution controller data, and management and control sources. Emerging vehicle technologies like connected vehicle (CV) technologies create new opportunities for collecting new types of transportation data that can improve the accuracy of transportation system performance measurement. Proliferation of CVs is also expected to increase data quantity and quality and enable the development of new performance measures (e.g., door-to-door travel times, queue locations, vehicle trajectories) that cannot be obtained using existing data sources. The main objective of this study is to develop a methodological framework to estimate system performance measurements using CV data. The study also provides a validation of the framework as a proof of concept by determining performance measurements from traditional and CV data. In doing so, the microscopic simulation software VISSIM with trajectory conversion algorithm (TCA) is used to generate CV data, particularly basic safety message (BSM) for a study corridor located in Birmingham, AL. The estimated performance measures can be used by a system operator, planner, or an automated system to support decisions associated with these processes. The measurements can be also used to derive information for dissemination to travelers, third-party data aggregators, traveler information service providers, and other agencies. In order to validate the proposed framework, the study utilized the proposed algorithm to calculate three performance measures, namely, travel time index (TTI), planning time index (PTI), and speed normal deviate (SND) by using BSMs. These performance measures were also calculated by the conventional method using NPMRDS data set. A statistical comparison between the two sets of performance measures was performed using the ANOVA: Single Factor statistical F-test. The results from the statistical test showed that the F value were less than the critical values of F within 5% significance level for all performance measures tested. This finding indicates that there is no significant difference between performance measures generated using CV data generated through VISSIM output and the NPMRDS data. Hence, the proposed framework is valid and it can be used in practical applications. Overall, this study serves as a reference to transportation agencies, researchers, and consultants involved in the assessment of transportation network performance using performance measures. Advances in transportation system performance measurement resulting from this study are expected to improve transportation policy decision making, optimize planning and operations, and improve transportation system outcomes in the future.

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