Graduation Semester and Year




Document Type


Degree Name

Doctor of Philosophy in Civil Engineering


Civil Engineering

First Advisor

(Taylor) Dr.Pengfei Li


As the population increases day by day, several cities are having trouble dealing with the increasing demand for a sustainable and equitable transportation system. At the same time, application of newer technology is also increasing . By definition, a sustainable transportation system indicates a method that includes the needs of each individual road user and mode of transportation. A sustainable transportation system also supports the compatibility of the system with the change in demand, regional development, and available resources. In a sustainable and equitable transportation system, integrating the virtual world and the physical world is crucial since it makes the overall system dynamic. This integration includes data monitoring and analysis from the virtual and the real world, developing new strategies, and applying simulation to evaluate the proposed methods. The goal of the sustainable and equitable transportation system is to generate a sustainable system that benefits all road users, i.e., both vehicles and pedestrians. Therefore, transportation sustainability and equitability can be divided into three main focuses (i) sustainable and equitable intersection design in mixed traffic conditions (ii) sustainable and equitable system for pedestrian detection ,and (iii) network performance analysis within the big data-driven environment. In this dissertation, a sustainable and equitable transportation system is presented in a connected vehicle and big data driven environment. The initial objective develops a congestion-aware heterogeneous connected automated vehicle cooperative scheduling problem at intersections with the objective to present a method that can provide a systematic approach to the green request accommodations with different priorities at intersections. The Mixed Integer Linear Programming (MILP) formulation is developed in the context of discrete space-time and phase-time networks whose choice variables are space-time with respect to individual vehicles and phase-time. An efficient search algorithm based on the “Arrival and Departure Curves (A-D curves)” for real-time applications is also built. Three experiments are conducted to validate the proposed MILP formulation and search algorithm. The simulation-based performance evaluation for the congestion-aware Connected and Automated Vehicles (CAV) scheduling reveal promising results for real-world applications in the future. Later, a novel dynamic flash yellow arrow (D-FYA) solution using the LIDAR-based tracking technique is developed. It can address the safety concerns in the FYA while recovering the permissive left-turn capacity after the concurrent pedestrians are cleared. Depending on the pedestrian volumes, the corresponding FYA with each cycle will either start as scheduled, be postponed, or be canceled within each cycle. The proposed D-FYA was deployed at an intersection next to the campus of the University of Texas at Arlington, and its real-time D-FYA decisions in the field were verified for over 100 traffic signal cycles through simultaneous observation in the field. The proposed D-FYA solution is further evaluated within an “ATC-cabinet-in-the-loop” traffic signal simulation platform to compare its mobility performance with another two permissive left-turn strategies: (I) “Protected + Permissive left turn (PPLT)” and (II) “PPLT with Minus-pedestrian-phase”. The experiment results reveal the D-FYA is accurate and adaptive compared to the other two permissive left-turn strategies. Furthermore, the dissertation also presents an innovative framework for travel demand forecasting. The current practice of travel demand forecasting in DFW is the classic “four-step” method based on household surveys and traffic counts. Like the connected vehicle trajectories, the emerging new traffic data bring both opportunities and challenges. The novel data sets reveal much more information about the traveler than before and pave the way for enhanced accurate and high-fidelity travel demand forecasts. On the other hand, the traditional travel demand forecast cannot take advantage of the total power of such data sets. The inconsistency of various data sets and heterogeneous data quality impact fusing the emerging traffic data with the traditional ones. This task explores the innovative framework of travel demand forecasting based on connected vehicle data using state-of-the-art big data analytics and high-performance computing to address these issues.


Sustainable and equitable transportation system, Network model, Traffic assignment


Civil and Environmental Engineering | Civil Engineering | Engineering


Degree granted by The University of Texas at Arlington