Uniform traffic flow has been shown to be unstable in certain flow regimes due to collective behaviors of human drivers, resulting in the well-observed stop-and-go waves. These traffic waves can arise even in the absence of merges, bottlenecks, or lane changing, and may lead to higher vehicle fuel consumption and emissions. In this article, we aim to smooth unstable traffic flow via optimal control of autonomous vehicles (AVs) in a predominantly human-driven traffic flow. These controlled AVs act as mobile actuators in the traffic without changing the way human-driven vehicles (HVs) normally operate. We develop a dynamic model to describe mixed traffic flow in the presence of both HVs and AVs, whose dynamics follow general nonlinear car-following principles. Based on this general framework, we formulate an optimal control problem with the objective of minimizing vehicle speed perturbation, and prove the existence of optimal AV control policy. Following the necessary conditions of optimality prescribed by the well-known Pontryagin’s minimum principle, we present a computational algorithm to determine the optimal AV control strategy and prove its convergence. The mathematical model is further illustrated using the intelligent driver model (IDM) and optimal velocity with relative velocity (OVRV) model for HVs and AVs, respectively. Numerical results are presented to show the effectiveness of the proposed approach on traffic smoothing, as well as the improvement on vehicle fuel economy.

Adaptive cruise control (ACC) vehicles have the potential to impact traffic flow dynamics. To better understand the impacts of ACC vehicles on traffic flow, an accurate microscopic car-following model for ACC vehicles is essential. Most of the ACC car-following models utilize a continuous function to describe vehicle acceleration and braking, e.g., the optimal velocity relative velocity (OVRV) model. However, these models do not necessarily describe car-following behavior with sufficient accuracy. Recent studies have proposed switching models to better describe realistic ACC dynamics. However, they often fail to accurately capture the driving behavior around the switching points, where a vehicle switches between acceleration and deceleration. In this study, we develop a two-condition, continuous asymmetric car-following (TCACF) model to capture ACC driving behavior in a physically interpretable manner, while preserving numerical soundness. The proposed TCACF model and multiple other car-following models are calibrated based on a real-world ACC trajectory dataset. The results show that the TCACF model better describes the asymmetric driving behavior of ACC vehicles than any of the commonly used car-following models, especially at switching points. The results indicate that the TCACF model considerably increases model accuracy by up to 32.46% when compared with other switching models and by up to 36.98% when compared to commonly used car-following models. The TCACF model is expected to offer new insights into modeling and simulating emerging ACC car-following dynamics with a higher degree of accuracy and can be used in applications where correctly simulating acceleration behavior is important.

Electric vehicles (EVs) promise significant advancements, including high energy efficiency and the facilitation of grid-stabilizing technologies such as vehicle-to-grid. However, their increased adoption introduces challenges such as elevated congestion, compromised safety, and grid instability. These challenges stem from differences in acceleration and deceleration patterns between EVs and internal combustion engine vehicles (ICEVs), mismatches between charging station demand and grid supply, and potential cyberattacks on the communications of EVs with charging stations and local grids. To address these issues, novel mathematical and machine-learning models have been developed. These models incorporate both simulated and real-world traffic flow data, charging station distribution and utilization data, and in-vehicle energy management and driver assistance data. The outcomes include optimally planned routes for EVs to destinations and charging stations, stabilized power distribution systems during peak hours, enhanced security in EV-station-grid communication, more energy-efficient storage systems, and reduced range anxiety for EV drivers. This paper systematically reviews the emerging impacts of EVs on evolving transportation systems, highlighting the latest developments in these areas and identifying potential directions for future research. By reviewing these specific challenges and solutions, this paper aims to contribute to the development of more efficient and sustainable electrified transportation systems.

With the emergence of vehicles featuring advanced driver-assistance systems like adaptive cruise control (ACC) and additional automated driving functionalities, there has arisen a heightened potential for cyberattacks targeting these automated vehicles (AVs). While overt attacks that lead to collisions are more conspicuous, subtle attacks that slightly modify driving behaviors can cause widespread impacts, including increased congestion, fuel consumption, and crash risks without being easily detected. To address the detection of such attacks, we first present a traffic modeling framework for three types of potential cyberattacks: malicious manipulation of vehicle control commands, data poison attacks, and denial-of-service (DoS) attacks. Subsequently, we examine the consequences of these attacks on both singular vehicle dynamics (micro) and broader traffic flow patterns (macro). We introduce a new anomaly detection model based on generative adversarial networks (GAN) designed for the real-time pinpointing of such attacks using vehicle trajectory data. Numerical results are presented to show the effectiveness of our machine learning strategy in identifying cyberattacks on vehicles equipped with ACC. The proposed approach is observed to outperform contemporary neural network models in detecting irregular driving patterns of ACC vehicles.

While automated vehicles (AVs) are expected to revolutionize future transportation systems, emerging AV technologies also open the door for malicious actors to compromise intelligent vehicles. As the first generation of AVs, adaptive cruise control (ACC) vehicles are particularly vulnerable to cyberattacks. Although recent efforts have been made to understand the impact of attacks on transportation systems, little work has been done to systematically model and characterize the malicious nature of these attacks. In this study, we develop a general framework for modeling and synthesizing two types of candidate attacks on ACC vehicles: direct attacks on vehicle control commands and false data injection attacks on sensor measurements, with explicit characterization of their adverse effects. Based on linear stability analysis of car-following dynamics, we derive analytical conditions that characterize the malicious nature of potential attacks. This ensures a higher degree of realism in modeling attacks with adverse effects, as opposed to simply considering constant or random attacks. The conditions derived provide an effective method for synthesizing strategic candidate attacks on ACC vehicles. We conduct extensive simulations to examine the impacts of intelligently designed attacks on microscopic car-following dynamics and macroscopic traffic flow. Numerical results illustrate the attack mechanism, offering useful insights into understanding the vulnerability of future transportation systems. The methodology developed allows for further study of the widespread impact of strategically designed attacks on traffic cybersecurity, inspiring the development of efficient attack detection techniques and advanced vehicle controls.