Variable Speed Limit Control Research Articles

Reinforcement Learning-Based Dynamic Zone Placement Variable Speed Limit Control for Mixed Traffic Flows Using Speed Transition Matrices for State Estimation

Current transport infrastructure and traffic management systems are overburdened due to the increasing demand for road capacity, which often leads to congestion. Building more infrastructure is not always a practical strategy to increase road capacity. Therefore, services from Intelligent Transportation Systems (ITSs) are commonly applied to increase the level of service. The growth of connected and autonomous vehicles (CAVs) brings new opportunities to the traffic management system. One of those approaches is Variable Speed Limit (VSL) control, and in this paper a VSL based on Q-Learning (QL) using CAVs as mobile sensors and actuators in combination with Speed Transition Matrices (STMs) for state estimation is developed and examined. The proposed Dynamic STM-QL-VSL (STM-QL-DVSL) algorithm was evaluated in seven traffic scenarios with CAV penetration rates ranging from 10% to 100%. The proposed STM-QL-DVSL algorithm utilizes two sets of actions that include dynamic speed limit zone positions and computed speed limits. The proposed algorithm was compared to no control, rule-based VSL, and two STM-QL-VSL configurations with fixed VSL zones. The developed STM-QL-DVSL outperformed all other control strategies and improved measured macroscopic traffic parameters like Total Time Spent (TTS) and Mean Travel Time (MTT) by learning the control policy for each simulated scenario.

Optimal Differential Variable Speed Limit Control in a Connected and Autonomous Vehicle Environment for Freeway Off-Ramp Bottlenecks

When traffic congestion occurs on freeway off-ramp bottlenecks, the traffic state becomes complicated and changeable, which leads to increased vehicle travel time and decreased traffic safety and traffic efficiency. Variable speed limit (VSL) control is an effective method to improve traffic conditions, increase bottleneck throughput, improve traffic efficiency, and reduce emissions. Currently, there is an emerging trend of using connected and autonomous vehicle (CAV) technology to develop VSL control. This paper proposes an optimal differential variable speed limit (DVSL) control strategy under mixed CAVs and human-driven vehicles (HVs) environment for freeway off-ramp bottlenecks. The proposed DVSL control considers the characteristics of on-ramp, off-ramp, and mixed traffic flow (i.e., CAVs coexist with HVs). The proposed optimal DVSL control can describe and forecast the dynamics of traffic flow, and can set different speed limits across each lane with a multiple-objective function of total travel time (TTT) and total travel distance (TTD). A model predictive control (MPC) approach was utilized to optimize the DVSL control algorithm. The designed DVSL control was tested on a real-word freeway section with a simulated off-ramp bottleneck. The simulation results show that the proposed control strategy outperforms other existing methods in terms of improving the mobility of a freeway off-ramp bottleneck and maximizing the environmental benefits. Sensitivity analysis shows that the proposed control strategy can improve performance with the increase of the penetration rate (PR) of CAVs. The proposed methods form the basis of VSL control at off-ramp sections under mixed traffic environment.

Dynamic areas strategy design for variable speed limiting at fixed freeway bottlenecks

Variable speed limit (VSL) control of freeway traffic is expected to improve traffic mobility and safety, especially at bottlenecks. However, unstable input flow makes the VSL less effective when the number of speed limit areas is fixed. In this paper, we first analyze the speed limit effect of the fixed speed limit area, then strengthen the VSL control effect by changing the number of speed limit areas, and finally propose a dynamic speed limit area strategy. This strategy redefines the speed limit areas and divides them into three types: normally open speed limit area, auxiliary speed limit area, and buffer speed limit area, which takes into account the transmission capability of the road and alleviates the speed difference. The effects of the VSL control strategy are evaluated using a cell transmission model. The proposed strategy uses the predictive control method to optimize the speed limit value and the number of speed limit areas. Two scenarios that considered both stable and fluctuating traffic demands are evaluated. The results show that the dynamic speed limit areas strategy can improve the safety and efficiency of the freeway.

Differential Evolution Based Numerical Variable Speed Limit Control Method with a Non-Equilibrium Traffic Model

This paper introduces a numerical variable speed limit (VSL) control method on a motorway, modeled by the system of partial differential equations (PDEs) of a non- equilibrium continuum traffic model. The method consists of a macroscopic simulation (i.e., numerical solution of the system of PDEs of the continuum model), introduction of the solution-based cost function and numerical optimization with a differential evolution algorithm (DE). Due to the numerical solution scheme, the method enables application of a wide range of continuum traffic models without prior discretization of PDEs. In this way, the method overcomes the limitations of the basic continuum models and represents a step towards more accurate traffic modelling in control strategies. In this paper, we determine optimal variable speed limits with the DE algorithm on a motorway section modeled by the modified switching curve model, which is a non-equilibrium continuum model consistent with the three-phase traffic flow theory. The effectiveness of the determined variable speed limits is validated using microsimulations of the test section, which show promising reductions of queue lengths and number of stops.

A decentralized feedback approach for flow control in highway traffic networks

Finite-time optimal feedback control for highway traffic networks under information constraints is studied. The primary objective is to design a fully decentralized state-feedback controller with ramp metering and variable speed limit control actions, with no information exchange between local controllers, that ensures feasibility and improves a global performance index. The design of a decentralized feedback controller with a one-hop information structure is examined, wherein the optimum outflow rate from each segment of the network depends only on the state of that segment and the state of all other segments directly connected to the downstream junction of that segment. It is demonstrated that when cost/constraints can be modeled or approximated by piecewise-affine functions, the control law has a closed-form state-feedback realization. The decentralization is based on the truncation of non-local variables in local optimization problems. The resulting decentralized control scheme has a simple closed-form representation and is scalable to arbitrary large networks; moreover, we demonstrate that, under certain conditions, with respect to certain meaningful performance indexes, the performance loss due to decentralization is zero; namely, the centralized optimal controller has a decentralized realization with a one-hop information structure.

A new reinforcement learning-based variable speed limit control approach to improve traffic efficiency against freeway jam waves

Conventional reinforcement learning (RL) models of variable speed limit (VSL) control systems (and traffic control systems in general) cannot be trained in real traffic process because new control actions are usually explored randomly, which may result in high costs (delays) due to exploration and learning. For this reason, existing RL-based VSL control approaches need a traffic simulator for training. However, the performance of those approaches are dependent on the accuracy of the simulators. This paper proposes a new RL-based VSL control approach to overcome the aforementioned problems. The proposed VSL control approach is designed to improve traffic efficiency by using VSLs against freeway jam waves. It applies an iterative training framework, where the optimal control policy is updated by exploring new control actions both online and offline in each iteration. The explored control actions are evaluated in real traffic process, thus it avoids that the RL model learns only from a traffic simulator. The proposed VSL control approach is tested using a macroscopic traffic simulation model to represent real world traffic flow dynamics. By comparing with existing VSL control approaches, the proposed approach is demonstrated to have advantages in the following two aspects: (i) it alleviates the impact of model mismatch, which occurs in both model-based VSL control approaches and existing RL-based VSL control approaches, via replacing knowledge from the models by knowledge from the real process, and (ii) it significantly reduces the exploration and learning costs compared to existing RL-based VSL control approaches.

A Variable Speed Limit Control Based on Variable Cell Transmission Model in the Connecting Traffic Environment

In the conventional variable speed limit (VSL) strategy, the control area is fixed under confined conditions. With the facility of the roadside unit, the control area for the VSL will vary in real-time in the connected environment. This study proposed an extended model-based VSL controller to improve traffic efficiency in the connected environment. The controller was designed based on the scheme of model predictive control (MPC). In the controller, an extended cell transmission model (CTM) with variable-length cells was established. The cell length variation was introduced to describe the characteristics of the variable control area. The optimizer in MPC is an improved Genetic Algorithm. A numerical simulation was conducted to show how the method, with the cooperation of variable speed limit and variable control area, alleviates the shock waves. The performance of the method was compared with a conventional VSL without a variable control area. The results show that the extended model-based VSL controller reduces the total travel time by 14.57% compared with the conventional VSL controller. The compared results illustrate that the proposed VSL controller can effectively resolve shock waves produced by the incident.

Selection of the Speed Command Distance for Improved Performance of a Rule-Based VSL and Lane Change Control

Variable Speed Limit (VSL) control has been one of the most popular techniques with the potential of smoothing traffic flow, maximizing throughput at bottlenecks, and improving mobility and safety. Despite the substantial research efforts in the application of VSL control, few studies have looked into the effect of the VSL sign distance from the point of an accident or a bottleneck. In this paper, we show that this distance has a significant impact on the effectiveness and performance of VSL control. We propose a rule-based VSL strategy that matches the outflow of the upstream VSL zone with the bottleneck capacity based on a multi-section Cell Transmission Model (CTM). Then, we consider the distance of the upstream VSL zone as a control variable and perform a comprehensive analysis of its impact on the performance of the closed-loop traffic control system based on the multi-section CTM. We develop a lower bound that this distance needs to satisfy in order to guarantee homogeneous traffic density across sections and reduce bottleneck congestion. The bound is verified analytically and demonstrated using microscopic simulation of traffic on I-710 in Southern California. The simulations are used to quantify the benefits on mobility, safety and emissions obtained by selecting the upstream VSL zone distance to satisfy the analytical lower bound. The developed lower bound is a design tool which can be used to tune and improve the performance of VSL controllers.

Developing a Variable Speed Limit Control Strategy for Mixed Traffic Flow Based on Car-Following Collision Avoidance Theory

Variable speed limit (VSL) control is an effective technology to improve safety near freeway bottlenecks. This study aims to develop a control strategy for mixed traffic flow consisting of both human-driven vehicles (HDVs) and connected and automated vehicles (CAVs) based on collision avoidance theory. A microscopic simulation platform is first established, and four vehicle longitudinal dynamic models including Cruising model, Intelligent Driver Model (IDM), Adaptive Cruise Control model (ACC), Cooperative Cruise Control model (CACC) and one vehicle lateral dynamic model Minimizing Overall Braking Induced by Lane Changes model (MOBIL) are incorporated into the simulation platform. Then, a new VSL control strategy derived from collision avoidance theory is proposed for mixed traffic flow at the initial stage of CAVs’ popularization. Extensive simulation experiments are conducted, and surrogate safety measures and total travel time indicators are utilized to evaluate the safety and efficiency performances of the proposed VSL control. Results indicate that the proposed VSL control strategy can effectively improve the safety performance near freeway bottlenecks with an acceptable efficiency level.

Fault-Tolerant Control of Variable Speed Limits for Freeway Work Zone With Recurrent Sensor Faults

Congestion associated with freeway work zones can adversely affect mobility, safety, and sustainability. Variable speed limits (VSL) control has been widely studied to mitigate the congestion caused by lane closures at work zones. However, most VSL controllers are designed without consideration of traffic sensor faults, especially recurrent sensor faults (RSFs) that commonly exist in freeway transportation systems. Therefore, this study proposes an interacting multiple model approach with a pseudo-model set (IMMP) to achieve VSL control with fault tolerance to different types of RSFs. With the design of a traffic flow model, an adaptive model set is developed using likelihood estimation to reduce the associated computational complexity. To ensure reliable RSF diagnosis, state covariance adaption is proposed to compensate for potential discrepancies caused by improper model parameters. A pseudo-mode set is designed to provide accurate traffic state estimations for VSL control without the prerequisite of a good match between the model parameters and the extent of corresponding sensor failures. The proposed system is evaluated under a realistic work zone environment using the traffic simulator SUMO. The results demonstrate that the system can achieve reliable RSF diagnosis and consistent improvements in mobility, safety, and sustainability near a freeway work zone area despite RSFs.

Developing dynamic speed limit strategies for mixed traffic flow to reduce collision risks at freeway bottlenecks

Connected and automated vehicles (CAVs) have the great potential to improve traffic flow because of their particular characteristics of connectivity and automation. This study aims to develop CAV control strategies based on car-following speed balance, which are defined as dynamic speed limit (DSL) strategies, and examine their performances on reducing freeway collision risks via microscopic simulations. The core idea of DSL strategy is to command the CAVs to slow down actively before reaching the bottlenecks, and form moving barriers to guide the following human driven vehicles to passively decelerate. Three DSL strategies are first developed for CAVs based on vehicle dynamics principles, and the influences of various position distribution patterns of CAVs on three strategies are compared in the one-lane scenario. Then, the DSL strategy with the best performance is selected based on simulation experiments, and a conventional variable speed limit control is used to compare the performance of our proposed methods. Finally, the DSL strategy based on Min control is tested in the two-lane, three-lane and four-lane scenarios to verify the effectiveness. Simulation results indicate that: (1) three DSL strategies based on CAVs can significantlyreduce collision risks when CAVs reach a certain proportion; (2) the uniform distribution of CAVs can maximize the effect of the moving barriers; (3) DSL strategy based on Min control is negatively affected by lane-changing behaviors, but still works well in the multi-lane scenario.

Optimal control method of freeway based on tollbooths lane configuration and variable speed limit control

There are tollbooths on some freeway ramps, and toll lanes can be divided into Electronic Toll Collection (ETC), Manual Toll Collection system (MTC), ETC/MTC hybrid toll lanes and closed. The traffic capacity of different lanes is different. Different lane combinations are set to adjust the traffic flow of the ramp into the main line. Therefore, this strategy can be regarded as a kind of ramp metering (RM). This paper presents an optimal control method for freeways based on tollbooths lanes configuration and variable speed limit (VSL) control. Firstly, cellular automata model and cell transmission model are used to describe the operation of toll plazas and main line traffic flow respectively. Furthermore, the freeway control methods are constructed by using tollbooths RM (TB-RM) and TB-RM combined with main line VSL control respectively to improve the freeway traffic operation efficiency. Finally, the proposed method is simulated experimentally. Results from simulation experiments demonstrate that the proposed method (i) significantly improves the traffic performance in terms of total time spent savings; (ii) observably enhances driving safety.

Investigation on variable speed limit control strategy of expressway under adverse weather conditions

In order to effectively improve the capacity of expressways under adverse weather conditions (rainy, foggy and snowy) and reduce the potential safety hazards of expressways, a variable speed limit control strategy is proposed. First, the impact of road alignment on traffic capacity is considered, and road traffic capacity reduction coefficients under different bends and slopes are added so that the cell transmission model and a dynamic traffic flow model can be established; Secondly, the maximum speed limit value under adverse weather conditions is determined according to the influence of rainfall intensity under rainy weather conditions, visibility under foggy weather conditions, and road adhesion coefficient under snowy weather conditions on traffic capacity; Finally, an adaptive genetic algorithm is employed to solve the function that satisfies the safety and efficiency of traffic operation. The results show that the variable speed limit control strategy under adverse weather conditions effectively improves the road capacity, reduces the driving risk of vehicles, and alleviates traffic congestion.

Operational and safety impacts of integrated variable speed limit with dynamic hard shoulder running

The combination of various Intelligent Transportation System (ITS) strategies generally has manifold effects, much greater than when strategies are implemented individually. This research introduces a novel dynamic control strategy, which includes the dynamic use of a Model Predictive Control (MPC) based Variable Speed Limit (VSL) integrated with the dynamic use of Hard Shoulder Running (HSR). Both VSL and HSR are proactively triggered to allow for necessary control measures to be taken to delay and possibly avoid the formation of a bottleneck. For traffic prediction, a modified METANET model is developed which takes into consideration the complex nature of driver’s behavior along with driver’s compliance, capacity drop and posted speed limits. The modified METANET model is shown to be more efficient than conventional macroscopic prediction models in detecting traffic congestions. This MPC based strategy was tested on a section of Deerfoot Trail, Calgary, Alberta using an exclusively developed integrated VISSIM-COM-MATLAB interface. The results from this study suggested that the integrated VSL and HSR control strategy results in a 21.09% increase in average speed and 33.44%. in vehicle-throughput. Furthermore, there was a noticeable reduction in the average travel time by 39.98% and in the total number of stops, by 32.43%. Importantly, the safety analysis performed using Surrogate Safety Assessment Model (SSAM) revealed a notable reduction in collisions, by 29.73%.

Boundary Feedback Stabilization of Freeway Traffic Networks: ISS Control and Experiments

The boundary feedback control of networks of freeway traffic is considered in this article by means of the partial differential equation (PDE)-based techniques. The control and measurements are all located at the boundaries of each link. We have established the boundary control model for the system, a linear hyperbolic system of balance laws, which includes not only the traffic flow dynamics of the network described by combining the linearized Aw&#x2013;Rascle&#x2013;Zhang (ARZ) traffic flow model of each link but also the integrated on-ramping metering and variable speed limit control modeled as the boundary condition. As the traffic demand of the network is fluctuated, the boundary input-to-state stability (ISS) controller is designed to suppress the disturbance and regulate the traffic flow into the boundedness regions of the desired states. Based on a novel Lyapunov function, some sufficient conditions in terms of the matrix inequalities are derived for the ISS boundary stabilization in the <inline-formula> <tex-math notation="LaTeX">${L}^{2}$ </tex-math></inline-formula>-norm. The numerical simulation is given to illustrate the effectiveness of the developed boundary feedback control. Moreover, an interesting traffic experiment is carried out by using the traffic simulation software, AIMSUN, and some specific traffic data are collected and analyzed to verify the feasibility and effectiveness of the boundary control.

Impact of Deep Reinforcement Learning on Variable Speed Limit strategies in connected vehicles environments

The Variable Speed Limit (VSL) control is considered in the context of connected vehicles acting as moving sensors, while their obedience to speed limit is enforced by a mandatory Intelligent Speed Adaptation (ISA) system. The objective of this study is to extend spatially static differential VSL control by introducing novel VSL strategies that are based on spatially dynamic speed limit zones. The spatial configuration of speed limit zones requires a novel traffic state representation based on the set of consecutive matrices which encode each vehicle position and speed within the controlled motorway during the control time step. The actions for all proposed VSL strategies are computed by the same Deep Reinforcement Learning (DRL) approach based on the Deep Deterministic Policy Gradient (DDPG) architecture. The DDPG learning models contain integration of Convolution and Long Short-Term Memory (LSTM) known as ConvLSTM layers, along with the Convolution and Fully Connected layers. Thus, those models can learn complex spatio-temporal traffic dynamics based on proposed traffic state representation. The results show that proposed VSL strategies have achieved higher overall motorway throughput compared to one which is based on the static speed limit zones and baseline cases (no-control and Simple Proportional Speed Controller algorithm). Simultaneously, they achieved a minimal increase in the number of aggressive braking, while the average headway is increased. All proposed VSL control approaches are simulated and analyzed by using a synthetic microscopic motorway model and characteristic traffic scenario for urban motorways.

Variable Speed Limit Control with Dynamic Location of Message Signs for the Internet-of-Vehicles Environment

The control position for traditional variable speed limits (VSL) is generally fixed. However, when congested traffic flow reaches the variable message sign, the effect of VSL on easing congestion will be reduced. It is, therefore, necessary to dynamically set the location of the variable message sign while implementing dynamic traffic control. Combined with the emerging Internet of Vehicles environment, this paper designs a variable-length cell transmission model to describe the uneven uniformity of traffic and to calculate the speed limits position of dynamic VSL. Then combined with the VSL control, an objective optimization function is established, with the travel time being the object. By numerical analysis and simulation, the sensitivity of the dynamic transmission model is verified under different traffic conditions. The results show that the proposed model can change the length of the cell and better describe the uneven distribution of the crowded tuple. The location of variable message signs can be set dynamically, and it can significantly alleviate the congested area. In addition, the new dynamic variable message sign control method can shorten travel times and improve traffic efficiency.

Safety Effects of Connected and Automated Vehicle-Based Variable Speed Limit Control near Freeway Bottlenecks considering Driver’s Heterogeneity

A control strategy using variable speed limit (VSL) is a proven solution to reduce freeway collision risks and improve safety. However, the heterogeneity of human drivers restricts the effectiveness of traditional VSL controls, which may be made up by recent advanced technologies of connected and automated vehicles (CAVs). This study aims to propose a CAV-based VSL control system to address the limitations caused by human drivers on VSL control’s effectiveness. First, the heterogeneity of human drivers is analyzed, and its impact on the safety performance of VSL is examined. Specifically, a microscopic simulation platform is established, and two vehicle dynamic models developed for CAVs and human-driven vehicles (HDVs) are incorporated into the simulation platform. Based on a widely utilized surrogate safety measurement, time-to-collision, its derivative metrics are applied to evaluate collision risks, and the total travel time is used to assess operational efficiency. Extensive simulations are conducted to examine the performance of the proposed CAV-VSL system. The results indicate the following: (1) the heterogeneity of human drivers negatively affects the performance of the VSL; (2) the performance of the proposed control system in a mixed flow can be improved by advanced wireless communication technology; (3) CAVs are able to implement the VSL control strategy effectively resulting in the proactive reduction of the heterogeneity.

Influence of Variable Speed Limit Control on Fuel and Electric Energy Consumption, and Exhaust Gas Emissions in Mixed Traffic Flows

Modern urban mobility needs new solutions to resolve high-complexity demands on urban traffic-control systems, including reducing congestion, fuel and energy consumption, and exhaust gas emissions. One example is urban motorways as key segments of the urban traffic network that do not achieve a satisfactory level of service to serve the increasing traffic demand. Another complex need arises by introducing the connected and autonomous vehicles (CAVs) and accompanying additional challenges that modern control systems must cope with. This study addresses the problem of decreasing the negative environmental aspects of traffic, which includes reducing congestion, fuel and energy consumption, and exhaust gas emissions. We applied a variable speed limit (VSL) based on Q-Learning that utilizes electric CAVs as speed-limit actuators in the control loop. The Q-Learning algorithm was combined with the two-step temporal difference target to increase the algorithm’s effectiveness for learning the VSL control policy for mixed traffic flows. We analyzed two different optimization criteria: total time spent on all vehicles in the traffic network and total energy consumption. Various mixed traffic flow scenarios were addressed with varying CAV penetration rates, and the obtained results were compared with a baseline no-control scenario and a rule-based VSL. The data about vehicle-emission class and the share of gasoline and diesel human-driven vehicles were taken from the actual data from the Croatian Bureau of Statistics. The obtained results show that Q-Learning-based VSL can learn the control policy and improve the macroscopic traffic parameters and total energy consumption and can reduce exhaust gas emissions for different electric CAV penetration rates. The results are most apparent in cases with low CAV penetration rates. Additionally, the results indicate that for the analyzed traffic demand, the increase in the CAV penetration rate alleviates the need to impose VSL control on an urban motorway.

A deep reinforcement learning-based intelligent intervention framework for real-time proactive road safety management

We propose a variable speed limit (VSL) system for improving the safety of urban expressways in real time. The system has two main functions: monitoring traffic data and then using the data to assess crash risk through a real-time crash prediction model (RTCPM). When the risk is high, the system triggers VSL control to restore traffic conditions to normal. The study addresses several weaknesses in existing VSL-based real-time safety interventions. Existing models are not widely applicable due to varying detector spacing among different freeways, and even within a study area. Therefore, with the existing detector spacing as an input, a cell transmission model (CTM) is used to simulate traffic states for the desired cell size. A dynamic Bayesian network (DBN) is used for modeling in the RTCPM. The proposed CTM model is then modified to allow VSL control. Whereas existing studies selected various VSL strategies from a predefined list, we employ a deep Q-network, which is a reinforcement learning-based machine learning algorithm, for the VSL control. Two busy segments of the Tokyo Metropolitan Expressway were used as the study area. After several iterations, our proposed real-time system reduced the crash risk by 19%.