Projects (Selected)

CAREER: Conflicting Traffic Streams with Mixed Traffic: Modeling and Control

NSF Award #1944369

Period: 2020-2025

This Faculty Early Career Development (CAREER) grant supports fundamental research in understanding of traffic flow patterns in mixed autonomous and human-controlled traffic streams. Emerging connected automated vehicle (CAV) technologies hold enormous potential to reduce transportation system congestion, improve safety, and facilitate higher energy efficiency. While anticipated benefits are greatest when all vehicles on the roads are CAVs, the near-term future will consist of mixed connected vehicles (CVs) connected automated vehicles and non-connected (human-driven, or HV) vehicles. This project will study mixed traffic streams, quantifying their impact on congestion and traffic flow, and develop robust strategies intended to improve performance and safety of the system as a whole, thereby enabling better understanding of how different vehicle types interact in traffic. The results will guide the development of roadway design, policies, and long-term planning for future transportation systems. The research activities will be closely integrated with a set of education and outreach activities to effectively promote smart and sustainable transportation. These activities include (i) developing a set of tools that will be shared with the research and practice communities (e.g., portable driving simulators, and an open-sourced micro-simulation platform), (ii) enhancing existing engineering curricula, (iii) broadening participation of women in STEM, and (iv) providing outreach to a broad audience, including K-12 students and teachers as well as cross-sector research communities.

The research objectives of this project are to (i) characterize the driving behaviors of HVs and CAVs in conflicting traffic streams, (ii) the collective impact of mixed traffic on the traffic flow, and (iii) investigate robust tactical-level control strategies for CAVs to improve system performance with respect to throughput, traffic flow stability, and safety. The research involves data collection on using driving simulators and field tests, establishment of behavior models for different vehicle types (i.e., CAVs, CVs, and HVs) based on the collected data, and design and evaluation of tactical level control strategies using rule-based and Artificial Intelligent (AI)-based control approaches. This research will uncover the cooperative behavior of CAVs and the behavior of HVs and CVs under cooperation in the context of conflicting traffic streams. The research is expected to produce effective control strategies for CTS with mixed traffic. The outreach plan will provide high school students and teachers with exposure to the university's virtual driving lab as well as to research challenges in emerging transportation systems.

Mixed Traffic Dynamics Under Disturbances: Impact of Multi-Class Connected and Automated Vehicles

NSF Award #1932921

Period: 2019-2021

Connected and Automated Vehicle (CAV) technologies have garnered huge interest across private industry, academia, government, and the public. A wide range of benefits are predicted when these ground-breaking technologies become mature, including higher road efficiency, improved safety, and better energy consumption and emissions. However, these benefits will be open to question until the technologies sufficiently mature. Specifically, a major uncertainty in benefits lies in mixed traffic of CAVs and human-driven vehicles (HDVs), where interactions between them remain largely unknown. Therefore, in the foreseeable future, traffic will likely be mixed with multiple classes of CAVs and HDVs. This project will aim to better understand the anticipated behavior of this mixed traffic system, and its impact on traffic in order to help fully utilize the potentials of the CAV technology. The results will guide the development of traffic management strategies, policies, and long-term planning for the future transportation system. This project will also engage in a range of integrated research, educational and outreach activities that will extend the knowledge obtained from this research to a broader audience, including developing simulation-based educational modules, organizing workshops, sharing simulation platform for mixed traffic, and engaging undergraduate and graduate students, particularly underrepresented groups, in the research and education.

This research aims to understand how HDVs and different classes of CAVs will interact under traffic disturbances that cause (momentary) reductions in speed and affect traffic flow performance. Specifically, this project will aim to (1) characterize discernable differences in the car-following behavior of HDVs and CAVs of different classes under disturbances; and (2) elucidate their effects on traffic flow throughput and traffic flow stability. To this end, this research will develop a systematic method to bring together different control modeling paradigms for CAVs into a unifying framework to unveil their individual and collective impacts on traffic flow throughput and stability. Three CAV control paradigms will be considered in this study: linear control, model predictive control (MPC), and artificial-intelligence-based control. The vehicle-level investigation of complex interactions among CAVs and HDVs will unveil the interaction mechanisms and elucidate how they scale up to the collective behavior of traffic stream, which will inspire new modeling paradigms to describe mixed traffic flow dynamics and control CAVs.

Understanding the Impacts of Automated Vehicles on Traffic Flow Using Empirical Data

NSF Award #1826162

Period: 2019-2022

Collaborator: Prof. Jorge Laval at Georgia Institute of Technology

Emerging automated vehicle (AV) technologies are likely to disrupt and transform our transportation system. The vast number of studies on AV hinge upon assumptions on how AVs behave with respect to other vehicles. Unfortunately, few of the assumptions can be empirically validated due to the absence of AVs. And yet, a critical component of AV technologies, the adaptive cruise control (ACC), has been used for over a decade and can be used to fill this gap. The research aims to study how vehicles with ACC behave when interacting with other vehicles on the road. The research will provide important insights on the behaviors of AVs in the future. The understandings gained from this research will also have important implications in traffic management, transportation planning, and design of ACC vehicles and AV. Additionally, this project will engage in a range of integrated research, educational and outreach activities, including sharing the ACC data with the research and practice community, developing educational modules, and K-12 outreach through summer camp.

More specifically, the research will (i) collect empirical trajectory data of ACC vehicles of different car-makers and their counterparts, regular vehicles (RVs), and (ii) formulate car-following models that capture the similar and differentiating features of ACCs and RVs. The project will focus on data collection and model estimation efforts using Maximum Likelihood Estimation (MLE). This enables the novel applications of statistical inference methods (e.g., the likelihood ratio test) to test various hypotheses to assess the differences and similarities among different ACC systems and between ACCs and RVs. In particular, the research will test if ACC systems from different car-makers differ from one another and from the RVs, and if they change substantially over time. Knowing this is important because it will dictate whether or not future research in this area has to focus on analyzing each individual carmaker, or if ACC systems will eventually converge towards human-like driving. In the modeling efforts, the research will build a general stochastic model so that the behaviors of ACC vehicles and RVs can be reconciled. The research will examine different variations of the distributions of the model components to specify the model(s) and use MLE for estimation of model parameters. Additionally, the research will use the estimated model(s) of the tested ACCs to extrapolate the results to the general mixed traffic.