Traffic Oscillations Research Articles

Developing a jam-absorption strategy for mixed traffic flow at signalized intersections using deep reinforcement learning

ABSTRACT Jam-absorption driving (JAD) can effectively prevent the generation and propagation of traffic oscillation. To alleviate the traffic congestion in the signalized intersection with mixed traffic flow, including human driving vehicles (HDVs) and connected and automated vehicles (CAVs), this study provides a jam-absorption driving strategy based on the traffic delay prediction of the mixed platoon under traffic congestion. An online traffic congestion prediction method with the objective of JAD is proposed and focuses on the leaving state of the trajectory to achieve fast capture of congestion features. Then, with real-time status and prediction information, we develop a Jam-absorption driving strategy based on a deep reinforcement learning (DRL) model to improve adaptability to the mixed traffic environment. The results show that this strategy can suppress more than 70% of traffic oscillations with excellent execution efficiency, improving traffic safety and efficiency.

Modeling and analysis of heterogeneous traffic flow with mixed regular and connected human-driven vehicles considering driver stochasticity

This paper presents a novel microscopic modeling framework to explore the impact of driver stochasticity and other related factors on heterogeneous traffic flows, consisting of both regular human-driven vehicles (RHDVs) and connected human-driven vehicles (CHDVs). We apply the stochastic optimal velocity car-following model for RHDVs, while an extended version of the stochastic continuous car-following model, taking into account optimal velocity guidance and driver compliance, is devised for CHDVs. The stability of CHDV car-following model is examined theoretically and numerically. Simulation experiments are conducted to analyze the stochastic heterogeneous traffic flow properties (i.e. stability and safety) with the proposed microscopic modeling framework under diverse parameter settings including CHDV penetration rate, driver compliance, and driver stochasticity. Results show that traffic flow stability and safety improve with the increase in CHDV penetration rate and driver compliance but deteriorate as the driver’s stochasticity strength increases. CHDVs significantly enhance traffic stability when the CHDV penetration rate or driver compliance exceeds a certain threshold, typically ranging between 0.5 and 0.7. Additionally, there is an interaction between driver stochasticity and CHDV penetration rate in their effect on traffic flow stability, where the contribution of stochasticity to traffic oscillations first increases and then decreases as the penetration rate rises. When the penetration rate is between 0 and 0.4, the traffic risk in emergency situations can be significantly reduced as the penetration rate increases. These findings may guide the management and control strategies of heterogeneous traffic flow.

PACC: A platoon-based adaptive cruise control strategy based on leader-following information topology to mitigate traffic oscillations under CAV environment

Traffic oscillations, often induced by repetitive acceleration and deceleration maneuvers in vehicles’ car-following behaviors, can cause many negative impacts on the traffic flow. With the development of connected and automated vehicle (CAV) technologies recently, scholars have made numerous effects on mitigating the propagation of traffic oscillations through a variety of advanced CAV control strategies, especially those related to the CAV platoon control. Different from the previous works, this paper proposed a platoon-based adaptive cruise control (PACC) strategy for CAV platoon to mitigate the traffic oscillations. The strategy is designed based on the novel and unique leader-following (LF) information topology. Built on the classical proportional-derivative (PD) controller that is implemented in the adaptive cruise control (ACC) strategy of autonomous vehicles (AVs), the PACC strategy is exquisitely designed to ensure the string stability of entire CAV platoon and the critically damped condition of each following CAV in the platoon. Credit to the rapid response of following CAV to the vibration of leading CAV’s dynamic status under LF information topology and the thorough consideration of string stability and damping characteristics in the PD controller design, the PACC strategy enables the CAV platoon to mitigate the traffic oscillations more efficiently than the existing cooperative adaptive cruise control (CACC) and ACC strategies. The numerical experiment for the mixed traffic flow on a single-lane ring road indicates that, when the CAV platoon adopts the PACC strategy, the performance of traffic flow in terms of operational efficiency, driving safety, passenger’s comfort, and fuel economy is substantially enhanced, compared with CACC and ACC strategies. In addition, the performance of PACC strategy gradually improves with the increase of market penetration rate (MPR) of CAVs and length of CAV platoon. Overall, the proposed PACC strategy is a promising solution to the mitigation of traffic oscillation under the CAV environment.

Effects of uncertain anomalous information on traffic flow of automated vehicles with V2V communication

Automated vehicles (AVs) equipped with vehicle-to-vehicle (V2V) communication can operate by sensing real-time status information through onboard sensors and wireless connections. Nevertheless, under the influence of multifarious random factors in real traffic, this critical information that support the normal movement of such vehicles may be anomalous, raising concerns on their mobility and traffic security. Due to the lack of appropriate analytical model, previous studies have not comprehensively uncovered the effects of uncertain anomalous information on traffic flow of AVs with V2V communication. Therefore, this study aims to bridge this critical gap. Firstly, by introducing a probabilistic parameter (i.e., information anomaly probability), we propose a general model that integrates the normal and compromised models, thereby capturing the longitudinal dynamics of AVs featuring V2V communication in the presence of uncertain anomalous information. To enable the detailed theoretical and experimental analyses, we specify it through the cooperative adaptive cruise control model calibrated with real-car data. Subsequently, we define the concept of pseudo string stability and parameterize the stability condition based on the characteristic equation method, so as to demonstrate the relationship between traffic flow stability and the parameters and probability of information anomaly. Finally, we refine the proposed probabilistic model and conduct extensive numerical experiments. The findings show that uncertain anomalous information could result in sudden or even frequent acceleration and deceleration of AVs, causing traffic oscillation, reduced traffic efficiency, and even collision accidents. In particular, the greater the information anomaly probability, the larger the disturbances experienced by traffic flow. Meanwhile, at the same level of anomaly, the combined impacts of various anomalous information could lead to more severe consequences than the singular impact of any individual anomalous information. Furthermore, the duration of anomalous information directly affects the time it takes for traffic flow to return to normal.

A Sigmoid-Based Car-Following Model to Improve Acceleration Stability in Traffic Oscillation and Following Failure in Free Flow

A Sigmoid-Based Car-Following Model to Improve Acceleration Stability in Traffic Oscillation and Following Failure in Free Flow

Enhancing Car-Following Performance in Traffic Oscillations Using Expert Demonstration Reinforcement Learning

Enhancing Car-Following Performance in Traffic Oscillations Using Expert Demonstration Reinforcement Learning

A time-varying shockwave speed model for reconstructing trajectories on freeways using Lagrangian and Eulerian observations

Inference of detailed vehicle trajectories is crucial for applications such as traffic flow modeling, energy consumption estimation, and traffic flow optimization. Static sensors can provide only aggregated information, posing challenges in reconstructing individual vehicle trajectories. Shockwave theory is used to reproduce oscillations that occur between sensors. However, as the emerging of connected vehicles grows, probe data offers significant opportunities for more precise trajectory reconstruction. Existing methods rely on Eulerian observations (e.g., data from static sensors) and Lagrangian observations (e.g., data from connected vehicles) incorporating shockwave theory and car-following modeling. Despite these advancements, a prevalent issue lies in the static assignment of shockwave speed, which may not be able to reflect the traffic oscillations in a short time period caused by varying response times and vehicle dynamics. Moreover, driver dynamics while reconstructing the trajectories are ignored. In response, this paper proposes a novel framework that integrates Eulerian and Lagrangian observations for trajectory reconstruction on freeways. The approach introduces a calibration algorithm for time-varying shockwave speed. The shockwave speed calibrated by the CV is then utilized for trajectory reconstruction of other non-connected vehicles based on shockwave theory. Additionally, vehicle and driver dynamics are introduced to optimize the trajectory and estimate energy consumption by applying a vehicle movement model. The proposed method is evaluated using real-world datasets, demonstrating superior performance in terms of trajectory accuracy, reproducing traffic oscillations, and estimating energy consumption.

Enhancing vehicular platoon stability in the presence of communication Cyberattacks: A reliable longitudinal cooperative control strategy

This study proposes a reliable longitudinal distributed control strategy for connected and automated vehicles (CAVs) in the presence of communication cyberattacks. The proposed strategy is designed to utilize dependable measurements from onboard sensors to evaluate the reliability of vehicle-to-vehicle (V2V) information and enhance the platoon string stability in an integrated manner. Specifically, this paper proposes a real-time reliability evaluation mechanism for the ego vehicle based on sensing and communication information from multiple predecessors, employing an upper-tailed chi-squared test. The mechanism is then incorporated into a distributed multi-predecessor linear feedback controller to adaptively weigh the preceding vehicles’ information, ensuring robustness against cyberattacks and maintaining the string stability of the vehicular platoon. To better understand the whole framework, sufficient conditions of true string stability and pseudo string stability are theoretically derived, unveiling the disturbance amplification jointly impacted by measurement accuracy of onboard sensors, cyberattack severity in V2V communication, and control parameters. Numerical experiments are conducted to validate the controller across various types of communication cyberattacks. The results suggest that the proposed control strategy can significantly alleviate the impact of cyberattacks and simultaneously dampen traffic oscillations effectively.

The role of information dissemination in sustaining a stable and resilient traffic network under disruption

ABSTRACT A functioning roadway system is key to any society and economy. Traffic events such as maintenance and accidents reduce roadway capacity and can have immediate, large and lasting impact on traffic conditions. This paper investigates the role of information dissemination as a control tool for reducing the negative effects of a traffic event. It examines how timely information can prevent unnecessary route changes and counteract misinformation, restoring traffic equilibrium faster. Systems that recover equilibrium more rapidly are considered more resilient. A traffic flow evolution model is applied to analyze network behavior post-disruption, with a model parameter interpreted as an information dissemination penetration rate adjustable by operators. Numerical experiments investigate the effects of the penetration rate on travel times, revealing a critical rate for quickly re-establishing a new equilibrium. Beyond this rate, greater traffic oscillations can be expected. The study determines the best information penetration rate to maximize network resilience while ensuring stability.

Assessing the Impact of CAV Driving Strategies on Mixed Traffic on the Ring Road and Freeway

The increasing traffic congestion has led to several negative consequences, with traffic oscillation being a major contributor to the problem. To mitigate traffic waves, the impact of the connected automated vehicles (CAVs) equipped with adaptive cruise control (ACC), FollowerStopper (FS), and jam-absorption driving (JAD) strategies on circular and linear scenarios have been evaluated. The manual vehicle is the intelligent driver model (IDM) and human driver model (HDM), respectively. The results suggest that on the ring road, the traffic performance of mixed traffic improves gradually with the increase of the proportion of CAVs under the ACC. Moreover, the traffic performance for the JAD strategy does not improve infinitely with the increase in the number of CAVs. Conversely, the FS strategy suppresses traffic waves at the cost of reducing traffic flow, and more CAVs are not beneficial for mixed traffic. It is interesting to note that under optimal performance in these three strategies, the FS strategy has the lowest number of CAVs, while the ACC strategy has the highest number of CAVs. For the linear road, it demonstrates that the JAD strategy exhibits a superior performance compared to the ACC. However, the FS strategy cannot dissipate traffic waves due to an insufficient buffer gap. Different models have varying effects on different strategies.

Car-following model based on spatial expectation effect in connected vehicle environment: modeling, stability analysis and identification

In driving situations, if drivers have advanced access to downstream traffic conditions ahead of time, they may modify their driving behavior accordingly. This proactive approach can help reduce frequent acceleration and deceleration of vehicles and minimize traffic congestion. This paper considers the average headway of downstream vehicles to predict the traffic conditions of downstream roads. Based on the full velocity difference (FVD) model, we propose a car-following model that incorporates spatial expectation effect (SEFVD). Firstly, a linear stability analysis is performed on the SEFVD model, and the results indicate that the critical value of the SEFVD model is relatively small, and the stabilization region increases gradually with the number of vehicles downstream. Secondly, employing perturbation amplitudes on a virtual circular road, a numerical simulation of the SEFVD model is carried out and compared with the FVD model. The results demonstrate that the SEFVD model has the ability to suppress the formation of traffic oscillations and stabilize vehicle formation more quickly. Simulation results for the 11 cars starting from a traffic signal indicate that the delay time and kinematic wave speed of the SEFVD model are within reasonable limits. To evaluate the changes in energy use, we also carried out energy consumption experiments. The experimental findings show that the SEFVD model uses less energy compared to the FVD model. Finally, the parameters of the models are calibrated and verified by using NGSIM data. The total calibration error of the SEFVD model (m = 2) decreases by 42.97% compared to the FVD model, and the total validation error decreases by 29.3%. The results show that the proposed model can fit the actual data well, and the fitting error is smaller.

Model-data-driven control for human-leading vehicle platoon

This paper proposes a model-data-driven control method for a human-leading vehicle platoon, comprising a human-driven vehicle (HDV) as the leader and connected automated vehicles (CAVs) as followers. Initially, a representative trajectory of HDVs is constructed using principal component analysis and the K-means clustering algorithm, which is utilized as training dataset. Subsequently, we propose a novel platooning method, named deep reinforcement learning with model-based guidance (DRLMG). The output of model predictive control (MPC) is integrated into the input state and reward function of the deep reinforcement learning (DRL) algorithm. The DRL algorithm benefits from guidance provided by MPC, leading to more optimal decision-making. To ensure safety and stability, a safety filter is designed using control barrier function and the control Lyapunov function. Simulation experiments with real-world driving data show that DRLMG outperforms MPC, reducing speed error, spacing error, and acceleration change rate by 17.9%, 53.7%, and 47.1%, respectively. In comparison to pure DRL, DRLMG increases spacing error by 6.5% but reduces speed error by 15.4% and acceleration change rate by 14.3%. The proposed method enhances DRL’s generalization capability, dampens traffic oscillations caused by the leading HDV, and guarantees driving safety and stability.

Exploration on relation between vehicle oscillation type and platoon oscillation evolution based on multi-scenario field experiment

In order to explore the categorization of vehicle oscillation and the relationship between individual vehicle oscillation and platoon oscillation in traffic flow, this paper conducts field experiments with both human driving vehicles(HDVs) and autonomous vehicles(AVs). The objective is to utilize experimental data to categorize and quantify traffic oscillation, delve into the impact of different degrees of vehicle oscillation on platoon oscillation, identify patterns between vehicle oscillation types and platoon oscillation evolution, and study the influence of AVs on platoon oscillation. The paper commences with car-following experiments involving HDVs and AVs, sets the platoon into three scenarios of HDV’s platoon, AV’s platoon, and mixed platoon experimental datasets. Subsequently, nine oscillation features reflecting the oscillation characteristics of vehicle traveling, such as volatility and trend, etc., are extracted from the experimental datasets. Then the CLS deep clustering model is constructed, trained, and outputs high-dimensional features. The model's effectiveness is assessed using both clustering error and deep learning error simultaneously. Finally, the paper analyzes the classification results of the experimental dataset and explores the relationship between vehicle oscillation type and platoon oscillation. The research finding indicates that the oscillation classification effectiveness of the CLS model is significantly superior to other algorithms. Using the CLS model, vehicle oscillations are categorized into four most suitable types: I, II, III, and IV, representing slight oscillation, general oscillation, relatively serious oscillation, and severe oscillation respectively. In HDVs’ platoon, as the vehicle oscillation type increases, the following vehicle does not fully adhere to the front vehicle, the amplitude trend of the vehicle changes, leading to oscillation in the following vehicle platoon. In the AVs’ platoon, AVs suppress the backward propagation of oscillations in the platoon, and the oscillation types are all type I, indicating mild oscillation. In the mixed platoon, it is observed that AVs, through swift perception of the state of preceding vehicle, promptly respond to adjust their own driving condition to alleviate platoon oscillation. The higher the proportion of AVs in the platoon, the stronger the inhibitory effect on platoon oscillation.

Disturbances and safety analysis of linear adaptive cruise control for cut-in scenarios: A theoretical framework

Although adaptive cruise control (ACC) has been widely analyzed in the car-following process, the way it reacts to cut-in maneuvers has been largely ignored. The cut-in maneuvers may trigger multiple disturbances to traffic, resulting in traffic oscillations and corresponding rear-end crashes. Hence, this study provides a theoretical analysis framework to model the disturbance evolution for ACC under cut-in scenarios. Specifically, we first derive the general ACC dynamic evolution based on the widely adopted ACC (i.e., linear feedback control) by applying Caley-Hamilton theorem. Given that, two representative cut-in scenarios are designed to comprehensively understand the impact of cut-in vehicle behavior as well as control parameters on the safety and stability of the ACC system. Enable by the interpretability nature of ACC dynamic analytical solution, necessary and sufficient conditions for overshoot and potential safety risks are derived. The proposed framework is further applied to analyze the cut-in disturbance evolution of commercial ACC systems using field-test data and calibrated control parameters, by which the probabilistic safety and stability condition is provided. Through the above efforts, the framework is instrumental in robust ACC design under cut-in scenarios.

On the string stability of neural network-based car-following models: A generic analysis framework

String stability plays a crucial role in regulating traffic flow, as traffic oscillation can be triggered by string instability in the car-following (CF) behavior. Although studies over the past decades have provided various methods for analyzing string stability of analytical CF models, no studies have focused on neural network (NN) based CF models despite the fact that these models have exhibited remarkable performance in learning realistic driving behavior in the recent literature. This paper fills this gap by proposing a generic theoretical framework for analyzing the string stability of NN-based CF models through an Estimation-Approximation-Derivation-Calculation process (referred to as EADC framework). Within the framework, we first estimate the steady states of NN-based CF models by solving the corresponding optimization model and obtain the smooth approximation of the NN-based models for linearization, based on which the transfer function is constructed. We then derive the general stability criteria for two commonly used classes of NN in CF modeling, i.e., feedforward NN-based CF models with basic input and recurrent NN-based CF models with multi-step historical information. The string stability is thus obtained by calculating the partial derivatives through the automatic differentiation method. As two case studies, we apply the proposed stability analysis framework on two typical NN-based CF models, the Mo-MLP model (Mo et al., 2021) and the Huang-LSTM model (Huang et al., 2018), and obtained the complete consistency between the theoretical results and the simulation results for both models, which demonstrates the soundness of the proposed EADC framework. Moreover, we discuss the applicability of the proposed EADC stability analysis framework in the emerging era of connected and autonomous vehicles and artificial intelligence.

Mitigating oscillations of mixed traffic flows at a signalized intersection: A multiagent trajectory optimization approach based on oscillation prediction

To mitigate the traffic oscillations generated when traffic flow encounters a red light at a signalized intersection, many studies have used reinforcement learning to control connected and automated vehicles (CAVs) trajectories to avoid oscillations. These methods, which are effective in mitigating oscillations in a pure CAV environment, perform poorly when applied to mixed traffic streams, due to the fact that most of the methods ignore that human vehicles (HVs) in mixed traffic flows stop at red lights and trigger oscillations, resulting in significant “trajectory fluctuation” phenomena in some CAVs, which will lead to poor effectiveness in mitigating oscillations.To address this problem, We propose a method that combines oscillation prediction and multiagent trajectory optimization to mitigate the oscillations generated by HVs. First, we predict the oscillation caused by HVs stopping during a red light, and after that, we use the Newell model to predict the propagation range of the oscillation. According to whether the HV ahead produces oscillation or not, we classify CAVs into two types of agents and design the control scheme separately. The first type of CAVs are immediately adjacent to the oscillation, and we use a jam-absorption driving (JAD) strategy to treat them as absorbing vehicles to absorb the oscillation. While the second type of CAVs are vehicles other than the first type of CAVs, we use the deep reinforcement learning (DRL) method to control them to arrive at the intersection within the predicted green light time. We tested the method in a mixed traffic flow simulation environment. Experimental results show that this method can effectively suppress the phenomenon of “trajectory fluctuations” under different CAV penetration rates, achieving the goal of effectively mitigating oscillations thus significantly improving traffic efficiency and reducing vehicle fuel consumption. Meanwhile, the method also has the advantages of fast convergence during training and high stability during testing compared with traditional reinforcement learning methods.

Stay in your lane: Density fluctuations in multi-lane traffic

When a new vehicle joins a lane, those behind may have to temporarily slow to accommodate them. Changing lane can be forced due to lane drops or junctions, but may also take place spontaneously at discretion of drivers, and recent studies have found that traffic jams and traffic oscillations can form even without such bottlenecks. Understanding how lane changing behaviour affects traffic flow is important for learning how to design roads and control traffic more effectively. Here, we present a stochastic model of spontaneous lane changing which exhibits a reduction in the overall flow of traffic. By examining the average flow rate both analytically and through simulations we find a definitive slow down of vehicles due to random switching between lanes. This results in the fundamental diagram depending on the rate of lane switching. By extending the model to three-lane traffic we find a larger impact on the flow of the middle lane compared to the side lanes.

A Bounded Rationality-Aware Car-Following Strategy for Alleviating Cut-In Events and Traffic Disturbances in Traffic Oscillations

A Bounded Rationality-Aware Car-Following Strategy for Alleviating Cut-In Events and Traffic Disturbances in Traffic Oscillations

Augmented Mixed Vehicular Platoon Control With Dense Communication Reinforcement Learning for Traffic Oscillation Alleviation

Augmented Mixed Vehicular Platoon Control With Dense Communication Reinforcement Learning for Traffic Oscillation Alleviation

Risk-informed longitudinal control in autonomous vehicles: A safety potential field modeling approach

The advent of autonomous vehicles (AVs) has complicated traffic flow and raised safety concerns, there is a pressing need to enhance traffic efficiency alongside ensuring robust safety. This paper proposes a dynamic longitudinal control strategy for AVs, based on real-time accident risk assessments. Data from traffic flow, road characteristics, and weather conditions are analyzed using a random forest algorithm, producing a real-time accident risk identification model through a support vector machine. In addition, the Perceived Risk Field Model (PRFM), a car-following model calibrated using the NGSIM dataset, considers vehicle distance, velocity, relative velocity, and acceleration. These models underpin our proposed longitudinal control strategies, tailored to variable accident risk levels. Under the normal risk control strategy, the PRFM outperforms the Intelligent Driver Model (IDM) and the Cooperative Adaptive Cruise Control Model (CACC) in stability and driving comfort. For high accident risk scenarios, our approach shows improved collision avoidance and traffic oscillation mitigation. Simulations indicate that the high accident risk control strategy offers better safety and stability than the normal risk strategy. Furthermore, traffic flow diagrams suggest an increase in road capacity as AV penetration rates rise, pointing to the promising efficiency of our proposed solution. This research provides a robust framework for AV operation, ensuring optimal traffic safety and efficiency.