Advisory Committee Chair
Virginia P Sisiopiku
Advisory Committee Members
Michael Anderson
Akhlaque Haque
Muhammad Sherif
Christopher Waldron
Document Type
Dissertation
Date of Award
2023
Degree Name by School
Doctor of Philosophy (PhD) School of Engineering
Abstract
The rise of ride-hailing and on-demand transportation services offered by Transportation Network Companies (TNCs) (such as Uber and Lyft) has been one of the key factors contributing to the growth of shared mobility services in recent years. The presence of contradictory findings on the impacts of TNCs necessitates the undertaking of an investigation into the shifts in mode choice that occur in the context of TNC services and their impacts on urban congestion. As a result of difficulties in gathering TNCs field data, it is still not possible to fully assess the impact of these services on urban congestion. Moreover, studies that investigated how TNC services affect the operational efficiency of the transportation system focused primarily on large-sized cities and the impacts of such services in medium-sized cities are still not well understood. In an effort to address such gaps, the purpose of this study is to demonstrate the feasibility of using simulation modeling to assess the impact of TNC services on urban congestion in medium-sized cities, utilizing Birmingham, AL as a case study. The study commenced by conducting a comprehensive literature review and examining research case studies to identify simulation platforms suitable for modeling shared mobility. This process helped to identify the Multi-Agent Transport Simulation (MATSim) as the most viable and established platform for simulating TNCs services. The study then utilized the MATSim platform to evaluate the impact of various types of TNC services on urban congestion. Significant efforts were placed in the development of a comprehensive model of the Birmingham area that realistically represented trips of Birmingham travelers using a variety of transportation modes including private automobile, transit, walking, and on-demand shared modes (Uber and Lyft). In order to model the latter, a survey of Uber drivers was conducted and used in combination with population statistics from census data to generate realistic Uber rides for the Birmingham agent-based simulation. In this study, two categories of ride requests from a TNC were simulated, namely individual ride requests and ride-pooling requests. Two types of ridepooling services were considered in the simulation, namely door-to-door (d2d) and stopbased (sB) services. Key findings of the study revealed that the addition of TNC vehicles to the network resulted in a significant increase in Vehicle Kilometer Traveled (VKT) for TNCs' individual ride requests and a reduction in VKT for both ride-pooling categories (d2d and sB). Moreover, the study allowed to identify the optimal TNC fleet size for the Birmingham region, which was found to be double the size under the TNC individual ride option, compared to the ride-pooling service options. Given the limited existing research on the effects of TNCs on traffic congestion in medium-sized cities, the findings of this study hold substantial value in terms of bridging the gap between the introduction of TNCs and their impact on traffic operations in a medium-sized city. This research work provides valuable contributions to the current body of knowledge related to multimodal modeling using an open-source large-scale agent-based transportation simulation platform. As such, the findings and results of this study are anticipated to be beneficial for researchers and practitioners in their planning efforts of including TNC services into their planning models. The findings of the case studies reported can also assist transportation decision makers, urban planners, and TNC providers in their efforts to optimize their operations and serve the needs of the traveling public better in the future.
Recommended Citation
Salman, Furat Shakir, "Impacts of Transportation Network Companies on Urban Congestion in a Medium-Sized City" (2023). All ETDs from UAB. 22.
https://digitalcommons.library.uab.edu/etd-collection/22
