Coordinated Ramp Metering Research Articles

Perimeter Control-Based Coordinated Ramp Metering with Linked Queue Management for Freeway Networks

This paper proposes a macroscopic fundamental diagram (MFD)-based perimeter control approach for coordinated ramp metering, aiming to minimize the total time spent (TTS) of a freeway network (stretch) with multiple bottlenecks. To address problems incurred from limited queue storage space and minimum green time at on-ramps, the proposed method extends previous approaches in two aspects. Firstly, a coordinated ramp control approach is proposed to ensure that the total metered on-ramp flow remains below the value determined by the MFD-based perimeter regulator, thereby preventing network over-saturation. Secondly, a novel queue management approach is developed to prevent on-ramp queue saturation and its associated side effects, for example, spillback or insufficient control actions. This approach determines the quasi-optimal flow of the next upstream (slave) on-ramp, based on the predicted queue of each (master) on-ramp suffering from excessive queues. The proposed perimeter controller was tested using SUMO, a microscopic simulator, on a realistic freeway network, and compared with other existing strategies, including HERO and perimeter controllers with other metered on-ramp flow distribution approaches. The simulation results suggest that the proposed strategy can ( i) effectively mitigate congestion and maintain capacity within the freeway network, resulting in reduced overall TTS, and ( ii) appropriately delay or prevent queue saturation at on-ramps.

Destination-Aware Coordinated Ramp Metering for Preventing Off-Ramp Queue Spillover and Mainstream Congestion

Destination-Aware Coordinated Ramp Metering for Preventing Off-Ramp Queue Spillover and Mainstream Congestion

Demonstration-guided deep reinforcement learning for coordinated ramp metering and perimeter control in large scale networks

Effective traffic control methods have great potential in alleviating network congestion. Particularly, in an urban network consisting of heterogeneous roads (e.g., freeways and urban roads), how to integrate and coordinate control policies on different roads is a critical issue in large-scale networks. This study addresses this question from two aspects: modeling and control. From the modeling aspect, we formulate the hybrid traffic modeling in heterogeneous networks with the Asymmetric Cell Transmission Model (ACTM) for freeways and the generalized bathtub model for urban roads. For the control aspect, this study considers two representative control approaches: ramp metering for freeways and perimeter control for urban roads, and we aim to develop a deep reinforcement learning (DRL)-based coordinated control framework for large-scale networks. However, there are two significant challenges in the coordinated control in large-scale networks with DRL methods: non-stationary environment and large search space. To address both issues, we incorporate the demonstration to guide the DRL method for better convergence by introducing the concept of “teacher” and “student” models. The teacher models are traditional controllers that provide control demonstrations. For instance, ALINEA and Gating are two representative feedback controllers for ramp metering and perimeter control which can be “teacher” models. The student models are DRL methods, which learn from teachers and aim to surpass the teachers’ performance. Additionally, we develop a parallel training scheme to accelerate the proposed DRL method. To validate the proposed framework, we conduct two case studies in a small-scale network and a real-world large-scale traffic network in Hong Kong. Numerical results show that the proposed DRL method outperforms demonstrators as well as DRL methods, and the coordinated control is more effective than just controlling ramps or perimeters respectively. The research outcome reveals the great potential of combining traditional controllers with DRL for coordinated control in large-scale networks.

Before–After Safety Evaluation of Coordinated Ramp Metering System Using Empirical Bayes Approach: A Case Study on I-80 in California

Before–After Safety Evaluation of Coordinated Ramp Metering System Using Empirical Bayes Approach: A Case Study on I-80 in California

A dynamic self-improving ramp metering algorithm based on multi-agent deep reinforcement learning

ABSTRACT We present a novel ramp metering algorithm that incorporates multi-agent deep reinforcement learning (DRL) techniques, which utilizes monitoring data from loop detectors. Our proposed approach employed a multi-agent DRL framework to generate optimized ramp metering schedules for each ramp meter in real-time, enhancing the operational efficiency of urban freeways with less investment. To simplify the implementation and training of the algorithm, we developed a simulation platform based on SUMO microscopic traffic simulator. We conducted a series of simulation experiments, including local and coordinated ramp metering scenarios with various traffic demands profiles. The simulation results indicate that the proposed DRL-based algorithm outperforms the state-of-the-practice ramp metering methods, considering a comprehensive evaluation index encompassing mainstream speed at the bottleneck and queue length on ramp. Additionally, the method exhibits robustness, scalability, and the potential for further improvement through online learning during implementation.

A coordinated ramp metering framework based on heterogeneous causal inference

AbstractThe coordinated ramp metering strategy aims to enhance the traffic flow on freeways by integrating the effects of multiple on‐ramps. The success of this strategy depends heavily on the metering effects of each on‐ramp and the cooperation pattern. To ensure the effectiveness of coordination control, it is essential to design the coordinated strategy based on the causal effect of each ramp flow on the freeway bottleneck. Therefore, this study investigated the heterogeneous causal effects of ramp flow on freeway bottleneck and proposed a coordinated ramp metering framework based on these causal effects. Causal inference was conducted using loop detector data. The estimated heterogeneous causal effect, which varies across traffic conditions, measures the change of bottleneck occupancy resulting from a unit increase in each on‐ramp outflow. This information quantifies the importance of each ramp in real time and can be used to update the ramp weight and metering rate of each ramp controller. To comprehensively test the proposed coordination framework, two instances with different road networks were selected. In the first instance, limiting the on‐ramp flow downstream of the bottleneck was found to be helpful in mitigating the bottleneck. Compared with isolated ramp metering, the proposed algorithm reduced travel delay by 26.0% by considering the heterogeneous causal effects of on‐ramp flow both upstream and downstream of the freeway bottleneck. In the next instance, the proposed algorithm reduced travel delay by 5.8%, compared to a well‐performed feedback‐based coordinated ramp metering algorithm. The results of this study suggest that the use of heterogeneous causal inference is effective in improving the performance of coordinated ramp metering algorithms.

Combined Control of Freeway Traffic Involving Cooperative Adaptive Cruise Controlled and Human Driven Vehicles Using Feedback Control Through SUMO

In this study, we propose and test through micro-simulation a novel controller for the combined control of freeway traffic by adopting coordinated Ramp Metering (RM) and Variable Speed Limiting (VSL) strategies. In order to figure out the performance of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_\infty $ </tex-math></inline-formula> State Feedback Controller we have designed, field observations on a real freeway segment with four on-ramps and an off-ramp in the city of Istanbul are used to calibrate the Intelligent Driver Model at SUMO (Simulation of Urban MObility). Scenarios with varying penetration rates of vehicles with Cooperative Adaptive Cruise Control (CACC) in mixed traffic are simulated through SUMO specific to the cases of no control, only coordinated RM control, and the combined coordinated RM + VSL control. Performance of the controller we have proposed has been analyzed considering a number of measures on traffic flow dynamics and emissions exhausted. We define three critical levels for penetration rates of vehicles with CACC in freeway traffic.

A physics-informed reinforcement learning-based strategy for local and coordinated ramp metering

This paper proposes a physics-informed reinforcement learning(RL)-based ramp metering strategy, which trains the RL model using a combination of historic data and synthetic data generated from a traffic flow model. The optimal policy of the RL model is updated through an iterative training process, where in each iteration a new batch of historic data is collected and fed into the training data set. Such iterative training process can evaluate the control policy from reality rather than from a simulator, thus avoiding the RL model being trapped in an inaccurate training environment. The proposed strategy is applied to both local and coordinated ramp metering. Results from extensive microscopic simulation experiments demonstrate that the proposed strategy (i) significantly improves the traffic performance in terms of total time spent savings; (ii) outperforms classical feedback-based ramp metering strategies; and (iii) achieves higher improvements than an existing RL-based ramp metering strategy, which trains the RL model merely by a simulator. We also test the performance of two different learning algorithms in the simulation experiment, namely a conventional tabular approach and a batch-constrained deep RL approach. It is found that the deep RL approach is not as effective as the conventional tabular approach in the proposed strategy due to the limited amount of training data.

An end-to-end data driven model for coordinated ramp metering systems

An end-to-end data driven model for coordinated ramp metering systems

A varying parameter multi-class second-order macroscopic traffic flow model for coordinated ramp metering with global and local environmental objectives

This paper presents the design of a coordinated ramp metering strategy based on a newly developed multi-class second-order macroscopic traffic flow model coupled with a pollutant emissions model. The approach followed for obtaining the parameters of the multi-class model is based on sigmoid surfaces spanning over traffic density and composition. An optimal control problem is formulated for designing an environmentally sustainable, efficient and equitable coordinated ramp metering strategy. In addition to the global network-wide objective of total emissions minimisation, two different types of environmental constraints are introduced for addressing local requirements. The resulting static large-scale optimisation problem is solved using the Differential Evolution algorithm. A small case study is provided analysing the multi-class flow model and the strategy’s application.

Results from Phase I of a Pilot Study on Dynamic Ramp Metering in Houston

Traffic in Houston, U.S., has continued growing over the past decade. The Houston District of the Texas Department of Transportation (TxDOT) recently began a pilot study to evaluate a dynamic ramp metering system. The project is aimed to convert ramp metering from local control to system-wide dynamic operation. In Phase I of the project, major control parameters and different metering strategies were tested and evaluated in simulation and field settings for a study corridor installed with six ramp meters. The study identified a base metering plan that overall worked well for sites without restrictive queue conditions. This base plan was that average speed of 50 mph or lower in the right-most two mainlanes will call for metering at a constant metering rate of 850 vehicles per hour for at least 4 min, and that queue occupancy of 50% or higher will call for meter shutdown for at least 1 min. Ramp metering coordinated with the downstream intersection performed well by accommodating diverted traffic caused by ramp metering. When operating ramp meters in a group, metering the immediately upstream meter performed best compared with metering further upstream meters. It is evident that ramp metering caused traffic diversion to the frontage road and also caused reduced queue-jumping behaviors on the frontage road at ramp meters with an immediately upstream exit ramp. The coordinated ramp metering strategy can potentially generate a benefit/cost ratio of 117:1 compared with local metering in the District.

Freeway Traffic Control in Presence of Capacity Drop

Capacity drop at congested freeway bottlenecks is well known with a lot of field observations. This paper studies coordinated ramp metering (RM) and mainstream traffic flow control (MTFC) as well as their integration (RM+MTFC) for freeway traffic, with particular attention to effects of capacity drop on the performance of traffic control measures. Via mathematical analysis and comprehensive simulation studies under an optimal control framework, the work has revealed a capacity-drop-related mechanism that MTFC and ramp metering are based on to take effects, and obtained a number of important conclusions: (1) applications of any control measure (RM, MTFC, or RM+MTFC) in freeways are justified by the existence of capacity drop in field; (2) an appropriate usage of the control measures can effectively prevent the activation of potential bottlenecks on freeways and hence avoid capacity drop; (3) any control measure is beneficial for a large majority of the driver population in a freeway network if it can manage to increase the accumulated total network exit flow; (4) it is a common misconception that ramp metering would simply transfer traffic loads from the freeway mainstream to on-ramps. The work has also highlighted the strengths, weaknesses, and applicability of ramp metering and MTFC.

Queue Discharge at Freeway On-Ramps Using Coordinated Operation of a Ramp Meter and an Upstream Traffic Signal

Abstract Ramp metering is an effective way of maintaining optimum traffic conditions and mitigating congestion on freeways. Several strategies for ramp metering exist in the literature. They are typically based on the freeway traffic parameters as control inputs to the ramp control logic. The ramp signal can be controlled in two ways, i.e., locally controlled (isolated ramp control) and coordinated ramp control. Coordinated ramp control refers to the ramp metering strategies in which several ramp meters connected to the freeway segment are dynamically controlled by considering traffic flows along all ramps. Coordinated ramp metering can play a vital role in freeway congestion mitigation on the ramps as well as normalize the traffic flow over the freeway. In this study, an alternate coordinated metering scheme that uses the state of the upstream traffic signal on arterial as the control input to the ramp meter is proposed. The proposed method aims to prevent long queues on the ramp with limited storage by taking feedback from the upstream traffic signal on the arterial, especially when the ramp has a small storage area for vehicles. Simulation results show a significant reduction in the queue length over the ramp using the proposed scheme. Additionally, the proposed scheme also benefits the arterial traffic.

Hierarchical ramp metering in freeways: An aggregated modeling and control approach

This paper develops a model-based hierarchical control method for coordinated ramp metering on freeway networks with multiple bottlenecks and on- and off-ramps. The controller consists of two levels where at the upper level, a Model Predictive Control (MPC) approach is developed to optimize total network travel time by manipulating total inflow from on-ramps to the freeway network. The lower level controller distributes the optimal total inflows to each on-ramp of the freeway based on local traffic state feedback. The control method is based on a parsimonious aggregated traffic model that relates the freeway total outflow to the number of vehicles on the freeway sections.Studies on aggregated traffic modeling of networks have shown the existence of a well-defined and low-scatter Macroscopic Fundamental Diagram (MFD) for urban networks. The MFD links network aggregated flow and density (accumulation). However, the MFD of freeway networks typically exhibits high scatter and hysteresis loops that challenge the control performance of MFD-based controllers for freeways. This paper addresses these challenges by modelling the effect of density heterogeneity along the freeway and capacity drop on characteristics of freeway MFD using field traffic data. In addition, we introduce a model to predict the evolution of density heterogeneity that is essential to reproduce the dynamics of freeway MFD accurately. The proposed model is integrated as the prediction model of the MPC in the hierarchical control method.The proposed coordinated ramp metering method shows desirable performance to reduce the vehicles total time spent and eliminate congestion. The control approach is compared with other coordinated ramp metering controllers based on the MPC framework with different traffic prediction models (e.g.CTM and METANET). The outcomes of numerical experiments highlight that the MFD-based hierarchical controller (i) is better able to overcome the modeling mismatch between the prediction model and the plant (process model) in the MPC framework and (ii) requires less computation effort than other nonlinear controllers.

Local and coordinated ramp metering within the unifying framework of an adaptive control scheme

This study develops and proposes a unifying control scheme to address the ramp and mainline metering problems arising in freeways. A distinguishing novelty of the proposed approach is that it can inherently be applied both at local and coordinated levels. The proposed strategy is based on a recently developed nonlinear adaptive control scheme, consisting of a nominal feedback law in conjunction with a nonlinear observer, which aims to estimate some unknown system variables. The present study provides insights into and demonstrates the properties and the performance of the proposed methodology, which is tested for realistic traffic scenarios using a macroscopic traffic flow simulator as a surrogate for potential field application. Comparison tests have been performed with other control strategies proposed in the literature and employed already in the field.

Extracting Origin-Destination with Vehicle Trajectory Data and Applying to Coordinated Ramp Metering

Ramp metering is an effective measure to alleviate freeway congestion. Traditional methods were mostly based on fixed-sensor data, by which origin-destination (OD) patterns cannot be directly collected. Nowadays, trajectory data are available to track vehicle movements. OD patterns can be estimated with weaker assumptions and hence closer to reality. Ramp metering can be improved with this advantage. This paper extracts OD patterns with historical trajectory data. A validation test is proposed to guarantee the sample representativeness of vehicle trajectories and then implement coordinated ramp metering based on the contribution of on-ramp traffic to downstream bottleneck sections. The contribution is determined by the OD patterns. Simulation experiments are conducted under real-life scenarios. Results show that ramp metering with trajectory data increases the throughput by another 4% compared with traditional fixed-sensor data. The advantage is more significant under heavier traffic demand, where traditional control can hardly relieve the situation; in contrast, our control manages to make congestion dissipate earlier and even prevent its forming in some sections. Penetration of trajectory data influences control effects. The minimum required penetration of 4.0% is determined by a t-test and the Pearson correlation coefficient. When penetration is less than the minimum, the correlation between the estimation and the truth significantly drops, OD estimation tends to be unreliable, and control performance becomes more sensitive. The proposed approach is effective in recurrent freeway congestion with steady OD patterns. It is ready for practice and the analysis supports the real-world application.

Coordinated Ramp Metering Based on Real-Time OD Information

Coordinated Ramp Metering Based on Real-Time OD Information

Optimal Control for Reducing Congestion and Improving Safety in Freeway Systems

The efficiency of freeway systems is often hindered by recurrent and non-recurrent congestion. The occurrence of these events is due not only to the high number of vehicles trying to use a shared infrastructure, but also by phenomena that temporarily deteriorate the capacity of the infrastructure itself. Among them, road accidents are considered as one of the primary causes of non-recurrent congestion. At the same time, several studies also identify traffic breakdowns as events leading to vehicle crashes. A specific goal of this research is to develop a global safety index that quantifies the expected number of crashes as a function of the current traffic state in the freeway system. Moreover, on the basis of this new index and the performance indicator generally adopted to evaluate the traffic delay, a coordinated ramp metering scheme is proposed jointly considering the reduction of travel times for the drivers and the improvement of safety in the freeway system. The control strategy is sought by defining a nonlinear optimal control problem with constrained control variables, solved by applying a specific gradient-based algorithm. The simulation analysis investigates the multi-objective nature of the problem assessing whether and to what extent the two components of the cost criterion are conflicting objectives.

A Study on the Merits for Coordinated Use of Ramp Metering and Variable Speed Limit Traffic Control Systems

Aim:The aim of this research was to investigate the merits for further improvements of traffic operation on freeways and expressways through coordinated use of Ramp Metering and Variable Speed limit (VSL) control systems.Methods:In this research, the widely used ALINEA Ramp Metering strategy was coordinated with a rule-based VSL strategy so that the total flow entered from the upstream freeway and entry ramp is maintained below the merge downstream capacity. The developed algorithm was then examined on a freeway network comprising two merge and one diverge sections, using VISSIM microscopic simulation model. The performance of the simulated network was examined under three scenarios namely, No-control, Ramp Metering only and Ramp Metering plus VSL controls. The network performance under each scenario was then assessed and compared using three measures of performance namely, average travel time, overall delay and freeway throughput. The ANOVA test was used to analyze and compare the impacts of specified scenarios.Results:The results indicated that the best performance is achieved under coordinated Ramp Metering plus VSL scenario as it produced a significantly better performance in comparison with the other two scenarios.Conclusion:The results can be attributed to the synergistic effects of coordinated and integrated use of these control systems on the freeway network and therefore, coordination of such systems is recommended.

Proportional-Derivative (PD) Controller for Heuristic Rule-based Motorway Coordinated Ramp Meters

This study proposes a new control method for rule-based motorway coordinated ramp metering. Coordinated ramp metering makes use of the network-wide measurements and allows multiple meters to participate in the control action to prevent or delay the onset of congestion on motorways. An essential component is a controller to dynamically adjust the level of contribution to coordination of each participating meter. The new control method builds on the concept of proportional-integral-derivative (PID) controller. A comprehensive sensitivity analysis is carried out to formulate the controller structure and coefficients. The final structure takes the form of PD controller with P-term as the main controller and D-term as supplementary to accelerate response speed and to improve the control stability. The PD controller is embedded in a rule-based coordinated ramp metering strategy for performance evaluation. A simulation study demonstrates the effectiveness of the PD controller. The coordinated control improves the mainstream traffic condition by reducing 60% and 8.4% of average traffic delay time over the base case assuming no metering and the local metering control scenario, respectively. The overall traffic travel time also decreased by the coordinated control by 25.1% and 2.0% over the base case and local metering, respectively. The enhanced mainstream traffic flow is achieved by balanced utilization of local meters and on-ramp spaces.