Doctor of Philosophy (Ph.D.)
Degree Granting Department
Civil and Environmental Engineering
Xiaopeng Li, Ph.D.
Fred Mannering, Ph.D.
Larry Head, Ph.D.
Xiaobo Qu, Ph.D.
Yujie Hu, Ph.D.
Adaptive Cruise Control, Car Following Characteristics, Empirical Method, Energy Consumption, Fundamental Diagram
Recently manufactured commercial vehicles are increasingly equipped with automated driving features. The adaptive cruise control (ACC) system, arguably the most common automated vehicle (AV) driving feature, is available on many new models of commercial vehicles in recent years. The ACC system is composed of a series of onboard sensors (e.g., millimeter-wave radars), computing and control units, which enables the commercial AVs to automatically maintain a safe headway between the subject AV and the lead vehicle by dynamically control the AV speed with real-time sensor information. Note that such commercial AVs are controlled by exact, prescriptive, and fast-responding computer-mechanical dynamic models while human-driven vehicles often exhibit uncertain, unpredictable, and slowly responding driving behaviors. Therefore, the commercial AVs may fundamentally alter traffic flow characteristics as their market penetration keeps increasing rapidly in these years. Due to this, it has a great need to study the impacts of the commercial AVs on future traffic.
To investigate the impacts, this dissertation collected high-resolution trajectory data of multiple commercial AVs following one another in a platoon with different headway settings. Then, the impacts of AV technologies on future traffic from both macroscopic and microscopic aspects were studied.To investigate the impacts of commercial AVs on macroscopic traffic flow, this dissertation proposed a general methodology that combines both empirical experiments and theoretical models to construct a fundamental diagram (FD), i.e., the foundation for traffic flow theory for AV traffic. The field experiment results revealed that the traditional triangular FD structure remains applicable to describe the traffic flow characteristics of AV traffic. Further, by comparing the FDs between AVs and human-driven vehicles, it was found that although the shortest AV headway setting can significantly improve road capacity, other headway settings may decrease road capacity compared with existing human-driven-vehicle traffic. It was also found that headway settings may affect the stability of traffic flow, which has been revealed by theoretical studies but was first verified by empirical AV data. With these findings, mixed traffic flow FDs were derived by incorporating different headway settings and AV penetration rates. The proposed method, including experiment designs, data collection approaches, traffic flow characteristics analyses, and mixed traffic flow FD construction approaches, can serve as a methodological foundation for studying future mixed traffic flow features with uncertain and evolving AV technologies.
The microscopic impacts of commercial AVs were investigated from the AV car-following characteristics (i.e., ACC system design) and AV energy consumption. To investigate the impacts of car-following characteristics of commercial AVs, parsimonious linear AV-following models that capture the first-order parameters on safety, mobility, and stability aspects were estimated with the data. The estimation results of the key parameters validated several theoretical predictions predicted by Li (2020). Specifically, it was found that as the time lag setting increases, the corresponding safety buffer decreases, indicating that AV safety could be improved with less pursuit of AV mobility or, conversely, AV mobility improvement may come at a cost of more stringent safety requirements. Also, as the time lag setting increases, AV string stability increases, indicating that stop-and-go traffic potentially could be dampened by compromising AV mobility. With this, one possible explanation to the observed string instability of commercial AV following control (i.e., ACC function) is that automakers may prefer to ensure a relatively short headway (and thus better user experience on vehicle mobility) at a cost of compromising string stability. It was also found that as the time lag increases, the cycle period of traffic oscillations gets longer, and the oscillation amplification gets smaller, which supports the tradeoff between mobility and stability. On the other hand, field experiments revealed issues beyond the predictivity of a simple linear model. That is, vehicle control sensitivity factors vary across different speed and headway settings, and the model estimation results for key parameters are not consistent over different speed ranges. This opens future research needs for investigating nonlinearity and stochasticity in the AV following modeling.
Since AVs are controlled by exact and fast-responding sensors and computers, the driving behavior as well as the driving strategies of AVs are expected to be enhanced compared with those of human-driven vehicles. With this, AVs have a great potential in reducing overall fuel consumption of traffic and consequentially achieving environmental-friendly mobility. To understand this probability, the AVs’ fuel consumption was calculated by several state-of-the-art or classical vehicle fuel consumption models by inputting the collected AV trajectory data. From empirical analyses, we found that as the AV headway setting increases, the corresponding fuel consumption decreases. It indicates that AV energy efficiency could be enhanced with less pursuit of AV mobility. One possible explanation for the tradeoff is that a longer headway may cause more stable AV following behavior and thus yields less fuel consumption. Also, we found that as the speed of AV traffic increases, the impacts of AV headway settings on fuel consumption decrease while the impacts of speed variation settings remain significant. In addition, we compared the fuel consumption of AVs and human-driven vehicles (HVs). We found that for the same experiment settings, the AVs always require less fuel consumption than the HVs. Further, we found that as the AV headway setting increases, the AV string stability increases and thus the overall fuel consumption of the AV string decreases.
Overall, this dissertation systematically investigated the impacts of AV technologies on future traffic from both macroscopic (AV FD) and microscopic (AV car-following characteristics and energy consumption) aspects. Following these findings, a set of managerial insights were provided into the relevant stakeholders for future AV traffic.
Scholar Commons Citation
Shi, Xiaowei, "Impacts of Automated Vehicle Technologies on Future Traffic" (2021). USF Tampa Graduate Theses and Dissertations.