CAMMSE Student Shaojie Liu will defend his dissertation

Event Date:
May 18, 2022 – 2:00 PM to 4:00 PM

Location:
EPIC 3226

Candidate Name:

Shaojie Liu Title: The impact of connected and autonomous vehicles on the superstreets May 18, 2022 2:00 PM Location: EPIC Room 3226 Abstract:

Connected and autonomous vehicles (CAVs) are a type of emerging technology that has promising potentials in improving many aspects of the existing transportation infrastructure, including operations, safety, and the environment. With the capability of traveling on the roads with shorter headways and more stable speeds, CAVs can yield a larger road capacity compared to human-driven vehicles (HDVs). Additionally, since the CAVs run on the roads with the guidance of computers or algorithms, accidents caused by errors from human drivers may be prevented, which can greatly reduce significant economic and societal losses. Less speed fluctuations are also beneficial to decrease emissions and contribute to the environment.
Thanks to the rapid development of computer science and communication technology, CAVs have evolved from theoretical experiments in academic labs to reliable products by commercial companies. Since both academic and industrial professionals have high expectations for CAVs, many studies have been conducted to explore and identify the impacts of CAV technologies on the transportation performances in many scenarios. These scenarios included conventional intersections, highway segments, on/off ramps, and roundabouts. Through extensive investigations on CAVs in different scenarios, it can be concluded that CAVs can perform better overall than HDVs. Nevertheless, it has also been found that the performances of CAVs are affected by many factors such as communication range, acceleration capabilities, and market penetration rates. Improvement in operational performance has been confirmed by existing studies when the market penetration of CAVs reaches a certain rate.
Superstreet is one of the innovative intersection designs and was proposed to alleviate the road congestion especially where unbalanced traffic volumes from main street and minor street exist. Superstreets have been successfully implemented in numerous states. Nevertheless, how CAVs would affect the performances of superstreets has not been explored, even to a minimum extent. This research is designed to investigate how CAVs with different technologies perform in the environment of superstreets. To be specific, the following questions will be answered: (1) at what market penetration rate CAVs would bring benefits towards operational performances; (2) at what extent CAVs would bring benefits towards operational performances of superstreets; (3) how the impact of CAVs on the operational performance would vary across different traffic scales and market penetration rates.
To achieve the research goals, models for CAV platooning, trajectory planning, and signal optimization have been developed, respectively. The effects of these models are tested respectively in a simulation environment where relevant traffic measures are extracted to evaluate the performances. The finding of this research may also be applied to other innovative intersection designs which have similar geometric characteristics and traffic patterns.