Showing papers on "Traffic wave published in 2021"

On string stability of a mixed and heterogeneous traffic flow: A unifying modelling framework

Abstract: Urged by a close future perspective of a traffic flow made of a mix of human-driven vehicles and connected, automated vehicles (CAVs), research has recently focused at making the most of CAVs capabilities to mitigate the instability of the whole, i.e. mixed, traffic flow. In all works, however, either the two sub-flows are studied under a simplifying but unrealistic assumption of flow homogeneity, or drivers’ and vehicles heterogeneity is not correctly taken into account within each sub-flow. We show here that the only condition developed so far to study a car-following model string stability for a heterogeneous flow, is inaccurate. Therefore, we propose a methodology to model string stability that considers drivers’ and vehicles heterogeneity, which is the essence of a real traffic. Uncertain transfer functions are introduced to map the probability distributions of car-following model parameters into a L 2 stability measure of a mixed and heterogeneous traffic. Specifically, they allow us to move from the stability analysis of a car-following model, or of a controller, to the stability analysis of a traffic flow, as interpreted by that model, or controller. Eventually, several other theoretical contributions on stability analysis are given in the paper, aiming at reconciling approaches from different fields. Among these, a mathematical justification of the equivalence between the asymptotic stability of a closed-loop platoon system – which has been studied through the famous “traffic wave ansatz” on a ring-road – and the L 2 stability of an open-loop platoon system.

Delayed Lagrangian continuum models for on-board traffic prediction

Abstract: In this paper we build Lagrangian continuum traffic flow models that are able to utilize trajectory information transmitted between connected vehicles via vehicle-to-everything (V2X) connectivity. These models capture three important features of traffic flow: (i) the propagation of congestions in time, (ii) the propagation of congestions in space, (iii) the string instability (or stability) of traffic that is related to the amplification (or decay) of traffic waves. The proposed models have only three tunable parameters to capture these three features. One of these parameters is the time delay that models the actuator lag in vehicle dynamics, the reaction time of human drivers, and the communication and feedback delays of connected and automated vehicles. The proposed Lagrangian continuum traffic models with delays establish a framework for traffic prediction and control. On one hand, connected vehicles may use predictions about the future motion of neighboring vehicles or their own. On the other hand, the continuum nature of these models allows one to study the large-scale impact of connected vehicles on the traffic flow. This opens the path for Lagrangian (vehicle-based) traffic control that supplements existing Eulerian (location-based) traffic control techniques.

Distributed Expansion Planning of Electric Vehicle Dynamic Wireless Charging System in Coupled Power-Traffic Networks

Abstract: In this paper, a distributed planning method for EV dynamic wireless charging system (DWCS) is proposed, which determines the locations and the scales of DWCS to maximize the economic effects of wireless charging while meeting EV charging demands. The Nesterov’s model with multiple traffic patterns is adopted in the traffic network (TN) to solve the traffic assignment problem and the traffic wave theory is used to analyze the distribution of road traffic density. In power distribution network (PN), the effect of EV connection modes on the expansion cost of power lines is considered. A mixed-integer linear programming (MILP) model for the proposed planning problem of DWCS in coupled power-traffic networks (PTN) with PN and TN constraints is formulated and solved with MATLAB/CPLEX. The DWCS case studies are carried out to verify the effectiveness of the proposed distributed planning method in PTN.

Integrated Framework of Vehicle Dynamics, Instabilities, Energy Models, and Sparse Flow Smoothing Controllers

Abstract: This work presents an integrated framework of: vehicle dynamics models, with a particular attention to instabilities and traffic waves; vehicle energy models, with particular attention to accurate energy values for strongly unsteady driving profiles; and sparse Lagrangian controls via automated vehicles, with a focus on controls that can be executed via existing technology such as adaptive cruise control systems. This framework serves as a key building block in developing control strategies for human-in-the-loop traffic flow smoothing on real highways. In this contribution, we outline the fundamental merits of integrating vehicle dynamics and energy modeling into a single framework, and we demonstrate the energy impact of sparse flow smoothing controllers via simulation results.

Stop-and-Go: Exploring Backdoor Attacks on Deep Reinforcement Learning-Based Traffic Congestion Control Systems

Abstract: Recent work has shown that the introduction of autonomous vehicles (AVs) in traffic could help reduce traffic jams. Deep reinforcement learning methods demonstrate good performance in complex control problems, including autonomous vehicle control, and have been used in state-of-the-art AV controllers. However, deep neural networks (DNNs) render automated driving vulnerable to machine learning-based attacks. In this work, we explore the backdooring/trojanning of DRL-based AV controllers. We develop a trigger design methodology that is based on well-established principles of traffic physics. The malicious actions include vehicle deceleration and acceleration to cause stop-and-go traffic waves to emerge (congestion attacks) or AV acceleration resulting in the AV crashing into the vehicle in front (insurance attack). We test our attack on single-lane and two-lane circuits. Our experimental results show that the backdoored model does not compromise normal operation performance, with the maximum decrease in cumulative rewards being 1%. Still, it can be maliciously activated to cause a crash or congestion when the corresponding triggers appear.

Distributed Kuramoto Self-Synchronization of Vehicle Speed Trajectories in Traffic Networks

Abstract: This article presents a distributed synchronization strategy for connected and automated vehicles in traffic networks. The strategy considers vehicles traveling from one intersection to the next as waves. The phase angle and frequency of each wave map to its position and velocity, respectively. The goal is to synchronize traffic such that intersecting traffic waves are out of phase at every intersection. This ensures the safe collective navigation of intersections. Vehicles share their phase angles through the V2X infrastructure, and synchronize these angles using the Kuramoto equation. This is a classical model for the self-synchronization of coupled oscillators. The mapping between phase and location for vehicles on different roads is designed such that Kuramoto synchronization ensures safe intersection navigation. Each vehicle uses a constrained optimal control policy to achieve its desired target Kuramoto phase at the upcoming intersection. The overall outcome is a distributed traffic synchronization algorithm that simultaneously tackles two challenges traditionally addressed independently, namely: coordinating crossing at an individual intersection, and harmonizing traffic flow between adjacent intersections. Simulation studies highlight the positive impact of this strategy on fuel consumption and traffic delay time, compared to a network with traditional traffic light timing.

Incorporating Kinematic Wave Theory into a Deep Learning Method for High-Resolution Traffic Speed Estimation.

Abstract: We propose a kinematic wave based Deep Convolutional Neural Network (Deep CNN) to estimate high resolution traffic speed dynamics from sparse probe vehicle trajectories. To that end, we introduce two key approaches that allow us to incorporate kinematic wave theory principles to improve the robustness of existing learning-based estimation methods. First, we use an anisotropic traffic-based kernel for the CNN. This kernel is designed to explicitly take forward and backward traffic wave propagation characteristics into account during reconstruction in the space-time domain. Second, we use simulated data for training the CNN. This implicitly imposes physical constraints on the patterns learned by the CNN, providing an alternate, unrestricted way to integrate complex traffic behaviors into learning models. We present the speed fields estimated using the anisotropic kernel and highlight its advantages over its isotropic counterpart in terms of predicting shockwave dynamics. Furthermore, we test the transferability of the trained model to real traffic by using two datasets: the Next Generation Simulation (NGSIM) program and the Highway Drone (HighD) dataset. Finally, we present an ensemble version of the CNN that allows us to handle multiple (and unknown) probe vehicle penetration rates. The results demonstrate that anisotropic kernels can reduce model complexity while improving the correctness of the estimation, and that simulation-based training is a viable alternative to model fitting using real-world data. This suggests that exploiting prior traffic knowledge adds value to learning-based estimation methods, and that there is great potential in exploring broader approaches to do so.

Structural Properties of the Stability of Jamitons

Abstract: It is known that inhomogeneous second-order macroscopic traffic models can reproduce the phantom traffic jam phenomenon: whenever the sub-characteristic condition is violated, uniform traffic flow is unstable, and small perturbations grow into nonlinear traveling waves, called jamitons. In contrast, what is essentially unstudied is the question: which jamiton solutions are dynamically stable? To understand which stop-and-go traffic waves can arise through the dynamics of the model, this question is critical. This paper first presents a computational study demonstrating which types of jamitons do arise dynamically, and which do not. Then, a procedure is presented that characterizes the stability of jamitons. The study reveals that a critical component of this analysis is the proper treatment of the perturbations to the shocks, and of the neighborhood of the sonic points.

Adaptative Optimal Control of Nonlinear Systems Simulation to Support Hazardous Materials Traffic Management

Abstract: Cooperative adaptative cruise controls and variable speed limit are gaining notoriety in recent years to fulfill the aim of reducing traffic congestions. Improvements in the management of hazardous materials, combined with these technologies, can reduce damages caused by congestions, mitigate risks to human health, and avoid environmental leakage. Drivers’ behaviors are directly influenced by human beings' different reactions. Therefore, the variance in traffic produces shockwaves, rippling traffic waves, and a reduction of the traffic flow capacity. This paper aims to present adaptative optimal control of nonlinear systems simulation as a tool to prove the effects of the predictions according to drivers’ behavior. Furthermore, it supports the decision making of hazardous material traffic management. Traffic engineering and queuing theory are also addressed.

Simulating the effectiveness of wave dissipation by FollowerStopper autonomous vehicles

Abstract: The traffic jam phenomenon known as stop-and-go waves is commonplace on many roadways and is unavoidable due to the imperfect nature of human driving behaviour. Previous research by Stern et al. (2018) has demonstrated experimentally that a single Autonomous Vehicle (AV) can be harnessed to dissipate stop-and-go waves produced by 20 other passenger vehicles driving in a cycle. However, the experiment was conducted in an idealistic situation of a single-lane ring road with one AV; meaning no lane changing was possible and multiple AV interaction was left untested. To address these limitations, the AV driving behaviour used to achieve this, known as FollowerStopper (FS), was modelled for use in the Aimsun traffic simulation software and validated before further numerical experiments were conducted. The microsimulation scenarios were designed to observe how multiple AVs driven by the FS controller (referred to as FS-AVs in this paper) affect their traffic wave dissipation capability. Both single and double-lane ring roads experimental designs were used with the latter capturing the untested effect of lane changes. FS-AV was found to be less effective than originally documented in both cases. Multiple FS-AVs included within one lane were still able to improve traffic flow and dissipate the stop-and-go waves, although it is sensitive to the combination of different factors and any deviation away from the ideal condition is likely to produce worse-off traffic operations in terms of traffic flow. For double-lanes, human-driven vehicles (HDVs) would change lanes at heightened rates to occupy the larger gap the FS-AV needs for its dissipating strategy, causing it to further pullback and set off a chain reaction to upstream traffic. Stop-and-go waves had not been dissipated and decreased traffic performance in terms of flow, speed and delay time resulted. Our results also showed that the wave dampening effect of the FS-AV does not translate between the ring road and its linear equivalent. On the other hand, our preliminary simulation results using a real-life freeway model did not suggest multiple FS-AVs worsen traffic conditions. The contradicting results might be due to different traffic conditions such as vehicle density. Further research is required to investigate what factors could adversely affect FS-AV’s ability to dampen shockwaves and explore possible improvement to its driving algorithm.

Research on the impact of ship traffic flow on the restricted channel segment of the middle Yangtze River based on traffic wave theory

Abstract: On the basis of the influence of dry season on ship traffic flow, the gathering and dissipating process of ship traffic flow was researched with Greenshields linear flow—density relationship model, the intrinsic relationship between the ship traffic congestion state and traffic wave in the unclosed restricted channel segment was emphatically explored when the ship traffic flow in a tributary channel inflows, and the influence law of multiple traffic waves on the ship traffic flow characteristics in unclosed restricted segment is revealed. On this basis, the expressions of traffic wave speed and direction, dissipation time of queued ships and the number of ships affected were provided, and combined with Monte Carlo method, the ship traffic flow simulation model in the restricted channel segment was built. The simulation results show that in closed restricted channel segment the dissipation time of ships queued is mainly related to the ship traffic flow rate of segments A and C, and the total number of ships affected to the ship traffic flow rate of segment A. And in unclosed restricted channel segment, the dissipation time and the total number of ships affected are also determined by the meeting time of the traffic waves in addition to the ship traffic flow rate of segments. The research results can provide the theoretical support for further studying the ship traffic flow in unclosed restricted channel segment with multiple tributaries

Reachability Analysis for FollowerStopper: Safety Analysis and Experimental Results

Abstract: Motivated by earlier work and the developer of a new algorithm, the FollowerStopper, this article uses reachability analysis to verify the safety of the FollowerStopper algorithm, which is a controller designed for dampening stop-and-go traffic waves. With more than 1100 miles of driving data collected by our physical platform, we validate our analysis results by comparing it to human driving behaviors. The FollowerStopper controller has been demonstrated to dampen stop-and-go traffic waves at low speed, but previous analysis on its relative safety has been limited to upper and lower bounds of acceleration. To expand upon previous analysis, reachability analysis is used to investigate the safety at the speeds it was originally tested and also at higher speeds. Two formulations of safety analysis with different criteria are shown: distance-based and time headway-based. The FollowerStopper is considered safe with distance-based criterion. However, simulation results demonstrate that the FollowerStopper is not representative of human drivers - it follows too closely behind vehicles, specifically at a distance human would deem as unsafe. On the other hand, under the time headway-based safety analysis, the FollowerStopper is not considered safe anymore. A modified FollowerStopper is proposed to satisfy time-based safety criterion. Simulation results of the proposed FollowerStopper shows that its response represents human driver behavior better.

Traffic wave models for the urban road with a work zone

Abstract: The work zones greatly change the traffic wave characteristics on the urban road and further significantly influence the signal timing schemes. However, studies on the traffic wave under the influe...

Robust traffic wave damping via shared control

Abstract: The traffic wave damping problem in a circular single lane track is addressed and solved via a shared control technique which takes a model of the human drivers’ driving habits into consideration. A formal analysis shows that the effectiveness of the proposed shared controller does not depend on the parameters of the human driver’s model, which is an important property in the implementation of the shared controller, since these parameters are difficult to measure, and vary from one human driver to another and from one driving situation to another. In addition, the proposed control law is robust: the stop-and-go wave can be dampened and there is no collisions among vehicles even if there is noise on the information each vehicle receives from the higher level traffic control center. A comparison between performances of the vehicles with and without the proposed control scheme demonstrates the robustness and the effectiveness of the shared control solution.

The Control of Over-saturation at the Critical Intersection Based on the Improved SEFP Method

Abstract: We have proposed the SEFP (Same Entrance Full-Pass) method in our previous research work in order to avoid the congestion at the key spot in the regional road network. The SEFP method, which can make all vehicles going to the critical intersection pass the stop-line with no stop during each release phase, reducing the vehicle delays and stops greatly. While the green time is underused in this method and the vehicle throughput at the critical intersection can be further increased. On this basis, we propose the improved SEFP method, which can formulate signal offsets control schemes at the upstream intersections by means of traffic wave theory, guaranteeing all the vehicles leave the critical intersection at saturated flow speed. In the meantime, the closure control is adopted at upstream intersections timely in light of the queuing length on the critical intersection lanes, avoiding the spill-outs effectively. This new method can improve the traffic throughput of the critical intersection while decrease the vehicle delays and stops, preventing the critical intersection from traffic over-saturation effectively. The simulation results of an actual critical intersection in Mianyang city demonstrate the validity and feasibility of the improved SEFP method.

Safer adaptive cruise control for traffic wave dampening

Abstract: This project aims to develop an adaptive cruise controller for vehicles at low speeds in stop-and-go traffic. Current adaptive cruise controllers can use RADAR sensors to follow a vehicle at high speeds (greater than 18 mph), but reach their limits if the lead vehicle's velocity dips below threshold, requiring the driver of the host vehicle to resume control over the car's speed. Some cruise controllers adapt to stop-and-go traffic, but these are mostly experimental and have yet to see widespread commercial implementation. These experimental models often have issues because of their limited data; consequently, the acceleration and deceleration can be jarring and uncomfortable to passengers. In contrast, because of our reliable sensor data, and the sensor configuration unique to the CAT Vehicle, our cruise controller will be capable of following cars at low speeds and functioning continuously, even when the car is stopped.

Courteous Behavior of Automated Vehicles at Unsignalized Intersections via Reinforcement Learning.

Abstract: The transition from today's mostly human-driven traffic to a purely automated one will be a gradual evolution, with the effect that we will likely experience mixed traffic in the near future. Connected and automated vehicles can benefit human-driven ones and the whole traffic system in different ways, for example by improving collision avoidance and reducing traffic waves. Many studies have been carried out to improve intersection management, a significant bottleneck in traffic, with intelligent traffic signals or exclusively automated vehicles. However, the problem of how to improve mixed traffic at unsignalized intersections has received less attention. In this paper, we propose a novel approach to optimizing traffic flow at intersections in mixed traffic situations using deep reinforcement learning. Our reinforcement learning agent learns a policy for a centralized controller to let connected autonomous vehicles at unsignalized intersections give up their right of way and yield to other vehicles to optimize traffic flow. We implemented our approach and tested it in the traffic simulator SUMO based on simulated and real traffic data. The experimental evaluation demonstrates that our method significantly improves traffic flow through unsignalized intersections in mixed traffic settings and also provides better performance on a wide range of traffic situations compared to the state-of-the-art traffic signal controller for the corresponding signalized intersection.

Trajectory data-based signal control period division method

Abstract: A trajectory data-based signal control period division method, comprising: obtaining, on the basis of a traffic wave theory, a Greenshields linear model and a fundamental relationship of three parameters, i.e. flow, density and speed, a relationship between a queuing wave speed and the flow (S1); overlapping trajectory data of the same intersection during the same periods in several days, so as to obtain input data (S2); obtaining, on the basis of the input data, a speed threshold value division method and a kinetic equation, the queuing wave speed (S3); and clustering the queuing wave speed, and performing signal control period division on the basis of the relationship between the queuing wave speed and the flow (S4). The signal control period division method not only improves the operating efficiency and safety level of a signal control intersection, but also saves the installation and maintenance cost of a fixed detector.

Research and empirical Analysis of Traffic flow Modeling based on fluid Mechanics

Abstract: In this paper, the overtaking lane change flow is introduced, and the corresponding traffic flow continuity equation is established. Through the differential analysis of traffic flow parameters, the motion differential equation of road traffic flow is established. Compared with fluid mechanics, according to Newton's second law, the simple calculation of traffic pressure, viscous resistance coefficient and viscous resistance is put forward, and the interference from downstream traffic wave is defined as the viscosity of traffic flow, and the difference between wave velocity and maximum wave speed is the coefficient of viscous resistance along the way. the viscous resistance along the route is proportional to the length of the lane, the rate of change of flow along the direction of traffic flow and the coefficient of viscous resistance along the route. The model can obtain the relationship between traffic flow parameters. The simulation example shows that the model can reflect the basic characteristics of traffic flow.

Integrated Framework of Vehicle Dynamics, Instabilities, Energy Models, and Sparse Flow Smoothing Controllers

Abstract: This work presents an integrated framework of: vehicle dynamics models, with a particular attention to instabilities and traffic waves; vehicle energy models, with particular attention to accurate energy values for strongly unsteady driving profiles; and sparse Lagrangian controls via automated vehicles, with a focus on controls that can be executed via existing technology such as adaptive cruise control systems. This framework serves as a key building block in developing control strategies for human-in-the-loop traffic flow smoothing on real highways. In this contribution, we outline the fundamental merits of integrating vehicle dynamics and energy modeling into a single framework, and we demonstrate the energy impact of sparse flow smoothing controllers via simulation results.