Ramp Metering Research Articles

Ramp Metering to Maximize Freeway Throughput Under Vehicle Safety Constraints

Ramp Metering to Maximize Freeway Throughput Under Vehicle Safety Constraints

Prediction of Queue Dissipation Time for Mixed Traffic Flows With Deep Learning

Queue dissipation has been extensively studied about traffic signalization, work zone operations, and ramp metering. Various methods for estimating the intersection’s queue length and dissipation time have been reported in the literature, including the use of car-following models with simulation, vehicle trajectories from GPS, shock-wave theory, statistical estimation from traffic flow patterns, and artificial neural networks (ANN). However, most of such methods cannot account for the impacts of interactions between different vehicle types and their spatial distributions in the queue length on the initial discharge time and the resulting total dissipation duration. As such, this study presents a system, named TrafficTalk, that applies a deep learning-based method to reliably capture the queue characteristics of mixed traffic flows, and produce a robust estimate of the dissipating duration for the design of the optimal signal plan. The proposed TrafficTalk, featuring the effectiveness in transforming video-imaged traffic conditions into vehicle density maps, has proved its performance under extensive field evaluations. For instance, compared with the benchmark model, XGBoost in the literature, it has reduced the MAPE from 25.8% to 10.4%., and from 31.3% to 10.4% if the queue discharging stream comprises motorcycles.

Lane Change Control Combined with Ramp Metering: A Strategy to Manage Delays at On-Ramp Merging Sections

Control measures at merging locations aimed at either the mainline traffic or on-ramp traffic do not lead to a fairness in the distribution of total delay across the two streams. This paper presents a control strategy of combining a lane change control with a ramp metering system at motorway merges. The control strategy presents the opportunity to control the delays incurred at the two traffic streams of the merge. An optimization problem is formulated for a multilane motorway with an on-ramp with the aim to minimize the total travel time of the system. The proposed strategy is tested using an incentive-based lane-specific traffic flow model. Results revealed a 17% reduction in the total travel time due to the proposed strategy. Moreover, it was shown that the distribution of delays over the mainline and on-ramp could be controlled via the proposed strategy. The performance of the combined control was also compared to the individual control measures. It was observed that the individual control measures (lane change only and ramp metering only) lead to high delays on either the mainline or on-ramp compared to the combined control, where the balance between the delay for the drivers on the mainline and on-ramp could be regulated. The combined lane change and ramp metering control presents opportunities for the road authorities to manage the total delay distribution across the two traffic streams.

Freeway ramp metering based on PSO-PID control.

Ramp metering on freeway is one of the effective methods to alleviate traffic congestion. This paper advances the field of freeway ramp metering by introducing an application to the on-ramp, capitalizing on the macro traffic follow theory and improved the freeway traffic flow. The Particle Swarm Optimization (PSO) based on Proportional Integral Derivative (PID) controller is further developed to single ramp metering as well as to optimize the PID parameters. The approach is applied to a case study of the Changyi Freeway(G5513) in Hunan, China. The simulation is conducted by applying the actual profile traffic data to PID controller to adjust the entering traffic flow on the freeway on-ramp. The results show that the PSO-PID controller tends to converge in about 80 minutes, and the density tends to be stable after 240 iterations. The system has smaller oscillation, more accurate adjustment of ramp regulation rate, and more ideal expected traffic flow density. The traffic congestion on mainline is effectively slowed down, traffic efficiency is improved, and travel time and cost are reduced. The nonlinear processing ability of PSO-PID controller overcomes the defects of the traditional manual closing ramp, and can be successfully applied in the field of intelligent ramp metering.

Simultaneous downstream and upstream output-feedback stabilization of cascaded freeway traffic

In this paper, we develop boundary output feedback control laws for traffic flow on two cascaded freeway segments connected by a junction. The macroscopic traffic dynamics are governed by the Aw–Rascle–Zhang (ARZ) model in which two subsystems of second-order nonlinear partial differential equations (PDEs) describe the evolution of traffic density and velocity on each segment. Due to the change of road access at the junction, different equilibria are considered for the two connected segments. To suppress stop-and-go traffic oscillations on the cascaded roads, we consider a ramp metering that regulates the traffic flow rate entering from the on-ramp to the mainline freeway. Different control designs are proposed such that the output feedback stabilization is realized with either the ramp metering located at the middle junction or the outlet with only boundary measurements of flow rate and velocity. The control objective is to simultaneously stabilize the upstream and downstream traffic to a given spatially-uniform constant steady-state. The distinct actuation locations motivate our design of two different delay-robust full state feedback control laws. The proposed designs are based on the PDE backstepping methodology and guarantee the exponential stability of the under-actuated network of two systems of two hyperbolic PDEs. Collocated boundary observers are proposed to construct output feedback controllers. Numerical simulations are performed to validate the control designs. Stabilization performance of the two output feedback is compared and evaluated with Proportional Integral (PI) boundary feedback controllers. Robustness to delays is also investigated.

A Deep Reinforcement Learning Approach for Ramp Metering Based on Traffic Video Data

Ramp metering that uses traffic signals to regulate vehicle flows from the on-ramps has been widely implemented to improve vehicle mobility of the freeway. Previous studies generally update signal timings in real-time based on predefined traffic measurements collected by point detectors, such as traffic volumes and occupancies. Comparing with point detectors, traffic cameras—which have been increasingly deployed on road networks—could cover larger areas and provide more detailed traffic information. In this work, we propose a deep reinforcement learning (DRL) method to explore the potential of traffic video data in improving the efficiency of ramp metering. Vehicle locations are extracted from the traffic video frames and are reformed as position matrices. The proposed method takes the preprocessed video data as inputs and learns the optimal control strategies directly from the high-dimensional inputs. A series of simulation experiments based on real-world traffic data are conducted to evaluate the proposed approach. The results demonstrate that, in comparison with a state-of-the-practice method, the proposed DRL method results in (1) lower travel times in the mainline, (2) shorter vehicle queues at the on-ramp, and (3) higher traffic flows downstream of the merging area. The results suggest that the proposed method is able to extract useful information from the video data for better ramp metering controls.

Active traffic management strategies for expressways based on crash risk prediction of moving vehicle groups

Active traffic management (ATM) strategies are useful methods to reduce crash risk and improve safety on expressways. Although there are some studies on ATM strategies, few studies take the moving vehicle group as the object of analysis. Based on the crash risk prediction of moving vehicle groups in a connected vehicle (CV) environment, this study developed various ATM safety strategies, that is, variable speed limits (VSLs), ramp metering (RM), and coordinated VSL and RM (VSL-RM) strategies. VSLs were updated to minimize the crash risk of multiple moving vehicle groups in the next time interval, which is 1 min, and the updated speed limits were sent directly to the CVs in the moving vehicle group. The metering rate and RM opening time were determined using mainline occupancy, the crash risk of upcoming moving vehicle groups, and the predicted time at which moving vehicle groups arrived at the on-ramp. The VSL-RM strategy was used to simultaneously control and coordinate traffic flow on the mainline and ramps. These strategies were tested in a well-calibrated and validated micro-simulation network. The crash risk index and conflict count were utilized to evaluate the safety effects of these strategies. The results indicate that the ATM strategies improved the expressway safety benefits by 2.84–15.92%. The increase in CV penetration rate would promote the safety benefits of VSL and VSL-RM. Moreover, VSL-RM was superior to VSL and RM in reducing crash risk and conflict count.

Optimization of time‐varying feedback controller parameters for freeway networks

AbstractIn this work, a freeway traffic control scheme is proposed to regulate traffic in freeway networks via multi‐class ramp metering. Specifically, each controlled on‐ramp of the system adopts a feedback regulator of proportional‐integral type and is able to compute the traffic flows entering the freeway mainstream, differentiated for each class of vehicles, e.g., cars and trucks. Since a crucial aspect of proportional‐integral controllers is the tuning of parameters, in this work an optimization‐based procedure is devised to determine the controller parameters to be used in the different on‐ramps of the network, according to the detected traffic conditions. To this end, a set of representative traffic scenarios is defined and, for each scenario, an offline optimization procedure is applied to determine the optimal parameters. Then, in real time, a controller parameter selector identifies the scenario associated with the current system conditions and communicates to the local controllers the optimal parameters to be applied. The effectiveness of the proposed control scheme is tested in a simulative example and the main results are discussed in the paper.

A novel traffic conflict risk measure considering the effect of vehicle weight

Introduction: Vehicle weight is deterministic to the impact force in collision, and thus the injury risk of vehicle occupants. In China, involvement of heavy vehicles in overall and fatal crashes are prevalent, even though heavy vehicles only constitute a small proportion of overall registered motor vehicles. However, vehicle weight is rarely considered in the existing traffic conflict risk prediction and assessment models because of the unavailability of required data. Method: Novel risk indicators for the diagnosis of traffic conflict risk map, considering the effect of vehicle weight, are proposed, with the advantage of comprehensive traffic flow characteristics and vehicle weight data using Weigh-in-Motion (WIM) technique. Weight-incorporated risk level (WRL) and weight integrated risk level (WIRL) are established to quantify the traffic conflict risk, at an instant and over a specified time period, respectively, by extending the conventional traffic conflict risk measures including time-to-collision (TTC) and modified potential collision energy (PCE). Then, a microscopic traffic simulation model is adopted to estimate the traffic conflict risk map along a highway segment that has partial lane closure. The traffic conflict risk performances, between the risk indicators with and without considering the vehicle weight, are compared. Results: The traffic conflict risks estimated using conventional risk indicators without considering the vehicle weight are generally lower than that based on WRL and WIRL. The difference is more profound when the proportion of heavy vehicles in the traffic stream increases. Conclusions: The finding is indicative to remedial engineering measures including variable message sign, speed limit, and ramp metering that can mitigate the real-time crash risks on highways, especially in adverse environmental and weather conditions, with due consideration of vehicle composition and crash worthiness of vehicles.

Multi-Class Freeway Congestion and Emission Based on Robust Dynamic Multi-Objective Optimization

In order to solve the problem of traffic congestion and emission optimization of urban multi-class expressways, a robust dynamic nondominated sorting multi-objective genetic algorithm DFCM-RDNSGA-III based on density fuzzy c-means clustering method is proposed in this paper. Considering the three performance indicators of travel time, ramp queue and traffic emissions, the ramp metering and variable speed limit control schemes of an expressway are optimized to improve the main road and ramp traffic congestion, therefore achieving energy conservation and emission reduction. In the VISSIM simulation environment, a multi-on-ramp and multi-off-ramp road network is built to verify the performance of the algorithm. The results show that, compared with the existing algorithm NSGA-III, the DFCM-RDNSGA-III algorithm proposed in this paper can provide better ramp metering and variable speed limit control schemes in the process of road network peak formation and dissipation. In addition, the traffic congestion of expressways can be improved and energy conservation as well as emission reduction can also be realized.

Ramp Metering with Microscopic Gap Detection Algorithm Design and Empirical Acceleration Verification

Freeway on-ramp areas are susceptible to traffic congestion during peak hours. To delay or prevent the onset of congestion, ramp metering can be applied. A Ramp Metering Installation (RMI) controls the inflow from the on-ramp to the main line so that the total flow can be kept just below capacity. Current ramp metering algorithms apply macroscopic traffic characteristics, which do not entirely prevent inefficient merging behavior from occurring. This paper presents a microscopic ramp metering approach based on gap detection in the right-hand lane of the main line. As preparation for the analyses, trajectory data were collected, by which the mean and standard deviation of driver accelerations were calculated. Simulation, including driver acceleration, is used to test the ramp metering controller. Overall, it shows travel-time savings compared with no-control and compared with existing macroscopic ramp metering systems. Especially during periods of very high main line demand, the microscopic control approach is able to achieve additional travel-time savings. This way, the proposed algorithm can contribute to more efficient road usage and shorter travel times.

Corridor-Wise Eco-Friendly Cooperative Ramp Management System for Connected and Automated Vehicles

Safety, mobility, and environmental sustainability are three fundamental issues that our transportation system has been confronting for decades. Intelligent transportation systems (ITS) aim to address these problems by leveraging disruptive technologies, such as connected and automated vehicles (CAVs). The cooperative potential of CAVs enable more efficient maneuvers and operation of a group of vehicles, or even the entire traffic system. In addition, CAVs may couple with other emerging technologies such as electrification to boost overall system performance and to further mitigate the aforementioned issues. In this study, we propose a hierarchical eco-friendly cooperative ramp management system, where macroscopically, a stratified ramp metering algorithm, is deployed to coordinate all of the ramp inflow rates along a corridor according to the real-time traffic condition; microscopically, a model predictive control (MPC)-based algorithm is designed for the detailed speed control of individual CAVs. Using the shared information from CAVs, the proposed ramp management system can smooth traffic flow, improve system mobility, and decrease the energy consumption of the network. Moreover, traffic simulation has been conducted using PTV VISSIM under various congestion levels for vehicles with different powertrain types, i.e., an internal combustion engine and an electric motor. Compared to conventional ramp metering, the proposed ramp management system may improve mobility by 48.6–56.7% and save energy by 24.0–35.1%. Compared to no control scenarios, savings in travel time and energy consumption are in the ranges of 79.4–89.1% and 0.8–2.5%, respectively.

Ramp metering control under stochastic capacity in a connected environment: A dynamic bargaining game theory approach

This paper presents a dynamic predictive and cooperative ramp metering approach that considers stochastic breakdowns at merging bottlenecks. A stochastic microscopic model is used to estimate traffic state parameters based on speed, location, and travel time information from connected vehicles. Traffic state predictions are obtained on a lane by lane basis using an adaptive Kalman filter (AKF) that fuses fixed detector measurements with the model; the AKF then produces multiple step ahead predictions. The ramp metering problem in this paper is modeled as a stochastic distributed model predictive control (SDMPC) approach. The SDMPC problem is solved based on a bargaining game approach where each controller, a player in the game, receives traffic state and control decision information from other controllers to solve the local optimization problem based on expected local costs and constraints. The performance of the proposed model is evaluated for three aspects of efficiency: short-term and long-term equity and effectiveness compared to multiple control scenarios. The outcomes indicate that the proposed cooperative model with stochastic capacity considerations outperforms the deterministic capacity-based models in regard to effectiveness and equity properties. However, the centralized approach performs slightly better in respect to system-wide efficiency.

Design and Development of Traffic Control System using Image Processing

Traffic congestion is now a big issue. Although it seems to penetrate throughout the world, urban towns are the ones which are most effected. And it is expanding in nature that it is necessary to understand the density of roads in real time to better regulate signals and efficient management of transport. Various traffic congestions, such as limited capacity, unrestricted demand, huge Red Light waits might occur. While insufficient capacity and unlimited demand are somehow interconnected, their delay in lighting is difficult to encode and not traffic dependant. The necessity to simulate and optimise traffic controls therefore arises in order to better meet this growing demand. The traffic management of information, ramp metering, and updates in real-time has been frequently used in recent years for image processing and monitoring systems. An image processing can also be used for the traffic density estimation. This research describes the approach for the computation of real-time traffic density by image processing for using live picture feed from cameras. It focuses also on the algorithm for the transmission of traffic signals on the road according to the density of vehicles and therefore aims to reduce road congestion, which reduces the number of accidents.

A supervised switching-mode observer of traffic state and parameters and application to adaptive ramp metering

Traffic state observers derived from the cell transmission model (CTM) are vulnerable to incorrect information and time variation of traffic flow parameters, in particular the critical density, known as the issue of mismodelling, which can cause erroneous mode switching of CTM-based observers to undermine estimation and control. This issue cannot be completely overcome, even if traffic flow parameters are augmented into state vector. We propose coupling a supervisor to the standard CTM-EKF observer to completely resolve this issue. Simulation shows that the proposed supervised observer can switch modes in accordance with actual situations and generate quality estimates of both traffic state and parameters. The supervised observer is then integrated with ramp metering control. Simulation shows that, in an environment of time-varying traffic flow parameters, the supervised observer-based ramp metering control system considerably outperforms an ordinary observer-based ramp metering control system, which only updates traffic state in real time.

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.

Evaluating the effect of ramp metering on freeway safety using real-time traffic data

Ramp metering relieves traffic congestion, reduces delay, and maintains the capacity flow on freeways. Due to its operational mechanism, ramp metering can also improve freeway safety. While the operational benefits of ramp metering have extensively been quantified, research on its safety effects is sparse. This study focused on evaluating the effects of ramp metering on the safety performance of the freeway mainline. It developed a crash risk prediction model for segments downstream of the entrance ramps when ramp metering is activated. The study was based on a corridor with system-wide ramp metering along I-95 in Miami, Florida. Real-time traffic, crash, and ramp metering operations data collected from 2016 to 2018 were used in the analysis. The study adopted a matched crash and non-crash case approach to evaluate the crash risk when ramp meters were activated and deactivated. A penalized logistic regression model was developed using a bootstrap resampling technique to estimate the effects of ramp metering activation and select important variables that could predict crash risk when ramp meters were activated. Results indicated that ramp metering improves safety along the freeway corridor by reducing the crash risk downstream of the entrance ramps. During ramp metering activation, the crash risk on segments downstream of the entrance ramps 5 min later can be predicted using the difference in the average lane speeds between upstream and downstream detectors, the average traffic volume in the lanes at the downstream and upstream detectors, and the coefficient of variation of speed between lanes in the upstream detectors. Also, the coefficient of variation of occupancy downstream could predict the crash risk 15 min later. The study results could be used by transportation agencies when evaluating the deployment of ramp meters. Moreover, the developed crash risk prediction model could be used in real-time to help agencies identify the increased crash risk and provide appropriate warning information to the upstream traffic.

Evaluation of Freeway Traffic Management and Control Measures Based on SUMO

With the rapid increase of freeway traffic volume, traffic congestion has become a normal phenomenon, through scientific freeway management and control measures, the traffic efficiency of the whole road can be effectively improved and the congestion problem can be alleviated. Therefore, this paper takes Shaoxing section of Shanghai-Hangzhou-Ningbo Feeway as the research object, puts forward three active traffic control measures, and uses the traffic simulation software SUMO to simulate and evaluate the effect of the freeway control measures. The results show that emergency lane control measure, ramp metering control measure, and variable speed limit control measure can effectively reduce the delay of freeway in the congested section, and improve road capacity. In this paper, the study of freeway management and control measures under congestion conditions, provides relevant decision support and theoretical guidance for freeway management.

A Fluid Model of a Traffic Network with Information Feedback and Onramp Controls

Unlimited access to a motorway network can, in overloaded conditions, cause a loss of throughput. Ramp metering, by controlling access to the motorway at onramps, can help avoid this loss of throughput. The queues that form at onramps are dependent on the metering rates chosen at the onramps, and these choices affect how the capacities of different motorway sections are shared amongst competing flows. In this paper we perform an analytical study of a fluid, or differential equation, model of a linear network topology with onramp queues. The model allows for adaptive arrivals, in the sense that the rate at which external traffic enters the queue at an onramp can depend on the current perceived delay in that queue. The model also includes a ramp metering policy which uses global onramp queue length information to determine the rate at which traffic enters the motorway from each onramp. This ramp metering policy minimizes the maximum delay over all onramps and produces equal delay times over many onramps. The paper characterizes both the dynamics and the equilibrium behavior of the system under this policy. While we consider an idealized model that leaves out many practical details, an aim of the paper is to develop analytical methods that yield interesting qualitative insights and might be adapted to more general contexts. The paper can be considered as a step in developing an analytical approach towards studying more complex network topologies and incorporating other model features.

Variable Speed Limit and Ramp Metering Control of Highway Networks Using Lax-Hopf Method: A Mixed Integer Linear Programming Approach

This paper presents a novel optimization formulation to solve the problem of variable speed limit control on road networks modeled by the Lighthill-Whitham-Richards (LWR) partial differential equation. It also presents some mathematical rules that allow for a reduction in the size and computational time of the optimization problem. Using the analytical solutions to the LWR model, an optimization problem is formulated for the variable speed limit and ramp metering control of traffic on highway networks using the Lax-Hopf algorithm. The resulting problem, which is non-linear in the decision variables, is transformed into a Mixed Integer Linear Program. An example is presented to show the effectiveness of the approach, including its application to a real-world highway network with multiple ramp connections. The possibility of linear relaxation of integer variables in the problem is also considered. Lastly, the method is compared to a classical Link Transmission Model formulation of the variable speed limit control problem.