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

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. 2020 The Authors. Published by Elsevier B.V.All rights reserved.Scopu

Analysis of the Effects of Adaptive Ramp Metering on Measures of Efficiency with a Proposed Framework for Safety Evaluation

Adaptive ramp metering (ARM) is a widely popular intelligent transportation system (ITS) tool that boasts the ability to reduce congestion and streamline traffic flow during peak hour periods while maintaining a lower implementation cost than traditional methods such as freeway widening. This thesis explores the effectiveness of ARM implementation on an 18 mile segment of the Interstate 80 (I-80) corridor in the Bay Area residing in northern California. Smaller segments of this particular segment were analyzed to determine the effective length of ARM on efficiency at various lengths originating from a known bottleneck location. Efficiency values were also compared against a control segment of the Interstate 280 (I-280) in San Jose to provide a test site experiencing similar traffic congestion but without any ARM implementation. An Empirical Bayes analysis was conducted to provide the foundation of a safety evaluation of the ramp metering implementation and determine a counterfactual estimate of expected collisions had ARM implementation not occurred. It was found that the installation of the ramp meters did allow for some marginal increases in efficiency but may not be entirely associated with ARM implementation due to a variety of external factors as well as showing inconsistent behavior between analyzed segments. Regarding safety, the predictive model estimates 32.8 collisions to occur along a 0.5 mile segment within a three-year timeframe if ARM were not installed, which implies substantial improvements in safety conditions. However additional efficiency and safety data within the “after” period may be necessary to provide a more robust and conclusive evaluation as the ARM system is still relatively new

Development and evaluation of operational strategies for providing an integrated diamond interchange ramp-metering control system

Diamond interchanges and their associated ramps are where the surface street arterial system and the freeway system interface. Historically, these two elements of the system have been operated with little or no coordination between the two. Therefore, there is a lack of both analysis tools and operational strategies for considering them as an integrated system. One drawback of operating the ramp-metering system and the diamond interchange system in isolation is that traffic from the ramp, particularly if it is metered, can spill back into the diamond interchange, causing both congestion and safety concerns at the diamond interchange. While flushing the ramp queues by temporarily suspending ramp metering has been the primary strategy for preventing queue spillback, it can result in freeway system breakdown, which would affect the entire system's efficiency. The aim of this research was to develop operational strategies for managing an integrated diamond interchange ramp-metering system (IDIRMS). Enhanced modeling methodologies were developed for an IDIRMS. A computer model named DRIVE (Diamond Interchange and Ramp Metering Integration Via Evaluation) was developed, which was characterized as a mesoscopic simulation and analysis model. DRIVE incorporated the enhanced modeling methodologies developed in this study and could be used to perform system analysis for an IDIRMS given a set of system input parameters and variables. DRIVE was validated against a VISSIM microscopic simulation model, and general agreement was found between the two models. System operational characteristics were investigated using DRIVE to gain a better understanding of the system features. Integrated control strategies (ICS) were developed based on the two commonly used diamond interchange phasing schemes, basic three-phase and TTI four-phase. The ICS were evaluated using VISSIM microscopic simulation under three general traffic demand scenarios: low, medium, and high, as characterized by the volume-to-capacity ratios at the metered ramps. The results of the evaluation indicate that the integrated operations through an adaptive signal control system were most effective under the medium traffic demand scenario by preventing or delaying the onset of ramp-metering queue flush, thereby minimizing freeway breakdown and system delays

Evaluation of temporary ramp metering for work zone safety

Title from PDF of title page; abstract from research PDF (University of Missouri--Columbia, viewed on June 26, 2014).Ramp metering has been successfully implemented in many states and studies have documented its positive mobility and safety benefits. However, there have been no studies on the use of ramp metering for work zones. This thesis reports the results from the first deployment of temporary ramp meters in work zones in the United States. Temporary ramp meters were deployed at seven work zones in Missouri. Due to lack of crash data, this study uses video data to extract alternative safety measures such as driver compliance, merging behavior, speed differentials, lane changing, and braking maneuvers. This evaluation suggests that temporary ramp meters should only be deployed at work zone locations where there is potential for congestion and turned on only during periods of high congestion. In comparison to over 90% compliance rates of permanent ramp meters implemented in other states, field data showed compliance rates from 40.5% to 82.9% in temporary ramp meter. This suggested that non-compliance could be a major safety issue in the deployment of temporary ramp meters. The use of a three-section instead of a traditional two-section signal head used for ramp metering produced significantly higher compliance rates. This thesis then aggregated the data into groups to further analyze the effects of different factors such as platoons, commercial trucks, work zone type and work zone-ramp configuration. After analyzing general characteristics of mainline and ramp vehicle speed and speed differentials, this study then focused on findings for different comparison groups. The two comparison groups are "between two work zones" versus "before work zone" configuration and "left-lane closed" versus "right-lane closed" work zone type. Results indicated lower mean speeds of mainline and ramp vehicles and higher differentials when ramp metering was turned on. This is expected and again temporary ramp meters are recommended only where congestion occurs. Congestion will lead to lower mainline speeds thus lower speed differentials either with or without ramp metering. Finally, analysis of merging headways showed that temporary ramp meters were effective in separating platoons before vehicles merged into mainline. This produces more single-vehicle merging which requires shorter gaps and causes fewer impacts on the mainline traffic

A Planning Tool for Active Traffic Management Combining Microsimulation and Dynamic Traffic Assignment (FHWA 0-6859-1)

0-6859Active traffic management (ATM) strategies have been considered as a tool for congestion mitigation in the last few decades. They rely on real-time traffic observations to regulate the flow of traffic. This research focuses on developing tools for evaluating the effectiveness of ATM strategies for freeway corridors. The research efforts can be categorized into two parts. The first part performs a detailed microsimulation analysis for four ATM strategies commenting on their effectiveness under cases of recurring and non-recurring congestion and develops a hybrid microsimulation-DTA (dynamic traffic assignment) model to capture the combined microscopic and network-level impacts of an ATM strategy. The second part develops spreadsheet tools which are useful to predict effectiveness of an ATM strategy under different levels of data availability. Ramp metering, variable speed limits, and hard shoulder running are found effective on the Williamson County test network, whereas dynamic ramp control and freeway arterial coordinated operations do not lead to any significant improvement. The authors also find that ATM strategies can improve the performance over a corridor while simultaneously reducing the performance of frontage roads due to spillover effects. The authors' findings also indicate that a hybrid microsimulation-DTA model is useful for an accurate analysis. However, based on the network characteristics, changes in route choice patterns may/may not be significant. The regression models used in the spreadsheet tool in the second part provide a good fit to the simulation results and thus can be used as an initial tool for testing effectiveness of ATM strategies during planning stage

Dynamic multi-ramp metering control with simultaneous perturbation stochastic approximation (SPSA)

Ramp metering was proven to be a viable form of freeway traffic control strategy, which could eliminate, or at least reduce, freeway congestion. In this study, the development of ramp metering control strategies, models, and constraints (e.g., meter locations, ramp storage capacities, lower and upper bounds of ramp metering rates) are discussed in detail. The pre-timed and demand/capacity metering control strategies were first evaluated, while the potential metered ramps were determined. A Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm is proposed to dynamically optimize multiple-ramp metering control by maximizing the total throughput subject to a number of constraints. The ramp metering rates subject to dynamic traffic conditions and capacity constraints are considered as decision variables in the SPSA algorithm. Based on the collected geometric and traffic data, a CORSIM model was developed to simulate traffic operation for the study site. The potential benefit of the dynamic multi-ramp metering control model under time varying traffic condition was simulated and evaluated. The increased total throughput and reduced total delay were observed, while the traffic conditions suitable for implementing ramp metering control were suggested. The developed dynamic multi-ramp metering control with SPSA algorithm has demonstrated its effectiveness to improve freeway operation

AN INTEGRATED CONTROL MODEL FOR FREEWAY INTERCHANGES

This dissertation proposes an integrated control framework to deal with traffic congestion at freeway interchanges. In the neighborhood of freeway interchanges, there are six potential problems that could cause severe congestion, namely lane-blockage, link-blockage, green time starvation, on-ramp queue spillback to the upstream arterial, off-ramp queue spillback to the upstream freeway segments, and freeway mainline queue spillback to the upstream interchange. The congestion problem around freeway interchanges cannot be solved separately either on the freeways or on the arterials side. To eliminate this congestion, we should balance the delays of freeways and arterials and improve the overall system performance instead of individual subsystem performance. This dissertation proposes an integrated framework which handles interchange congestion according to its severity level with different models. These models can generate effective control strategies to achieve near optimal system performance by balancing the freeway and arterial delays. The following key contributions were made in this dissertation: 1. Formulated the lane-blockage problem between the movements of an arterial intersection approach as an linear program with the proposed sub-cell concept, and proposed an arterial signal optimization model under oversaturated traffic conditions; 2. Formulated the traffic dynamics of a freeway segment with cell-transmission concept, while considering the exit queue effects on its neighboring through lane traffic with the proposed capacity model, which is able to take the lateral friction into account; 3. Developed an integrated control model for multiple freeway interchanges, which can capture the off-ramp spillback, freeway mainline spillback, and arterial lane and link blockage simultaneously; 4. Explored the effectiveness of different solution algorithms (GA, SA, and SA-GA) for the proposed integrated control models, and conducted a statistical goodness check for the proposed algorithms, which has demonstrated the advantages of the proposed model; 5. Conducted intensive numerical experiments for the proposed control models, and compared the performance of the optimized signal timings from the proposed models with those from Transyt-7F by CORSIM simulations. These comparisons have demonstrated the advantages of the proposed models, especially under oversaturated traffic conditions

Dual-State Kalman Filter Forecasting and Control Theory Applications for Proactive Ramp Metering

Deterioration of freeway traffic flow condition due to bottlenecks can be ameliorated with ramp metering. A challenge in ramp metering is that it is not possible to process data in real-time and use the output in a control algorithm. This is due to the fact that by the time processing is completed and a control measure applied, the traffic state will have changed. A solution to this problem is to forecast the traffic state and implement a control measure based on the forecast. A dual-state Kalman filter was used to forecast traffic data at two locations on a freeway (I-84). A Kalman filter is an optimal recursive data processing algorithm; predictions are based on only the previous time-step’s prediction and all previous data do not need to be stored and reprocessed with new measurements. A coordinated feedback ramp metering control logic was implemented. The closed-loop system seeks to control the traffic density on the mainline while minimizing on-ramp queues through weighting functions. The integration of the Kalman filter with the ramp meter control logic accomplishes the ramp meter algorithmic scheme in which is proactive to changes in freeway conditions by controlling a forecasted state. In this closed-loop framework, real-time forecasts are produced with a continuously updated prediction that minimizes errors and recursively improves with each successive measurement. MATLAB was used to model the closed-loop control system as well as modify the input output constraints to evaluate and tune controller performance

Exploratory Analysis of Ramp Metering on Efficiency, and Safety of Freeways Using Microsimulation

The microscopic Verkehr In Städten – SIMulations Model (VISSIM) stochastic simulator program was used to explore the effectiveness of ramp metering on efficiency, Level of Service, and safety of freeways. Three different geometric configurations of ramp-freeway junctions were evaluated using different traffic volume conditions of the ramp and the freeway. Different signal timing scenarios were designed for the different traffic volume and geometric configuration scenarios. Calibration process was conducted for the collected traffic data that were obtained from cameras and detectors. Two-hundred-eighty models were built and run to explore the effectiveness of the performance and safety of the ramp meters on freeways. Average speed and average travel time of the vehicles passing a 3,000-ft long freeway segment were used as measures of effectiveness of the freeway efficiency evaluation. Average density in the ramp influence area was used to obtain the freeway level of service as a measure of effectiveness of the freeway capacity evaluation. Frequency, types, and severity of vehicle conflicts, which occurred on the 3,000-ft freeway segment, were used as measures of effectiveness of the freeway safety evaluation. The Surrogate Safety Assessment Model (SSAM) program, which was developed by the Federal Highway Administration (FHWA), was used to find the frequency and types of vehicle conflicts, while the severity of vehicle conflicts was separated by a designed method that was retrieved from the previous literature studies. Minitab statistical software was used for some tests such as normality test to determine the appropriate number of samples, and F-tests. A sensitivity analysis was also conducted for better understanding the effectiveness of two assumption changes on the results that were obtained from running the models. The assumptions were car following headway in the ramp influence area and traffic composition on the freeway. The findings of the study provided different results related to the different geometric configurations, signal timing designs, and traffic volumes. Ramp metering at the Type I geometric configuration provided positive effects on the efficiency and safety of the freeway when using the two designed signal timing scenarios when the freeway traffic volume was equal to or greater than 1,250 vehicle per hour per lane (vphpl) and the ramp traffic volume was equal to or greater than 800 vphpl. Ramp metering provided negative effects on the efficiency and safety of the freeway when using it for the Type II geometric configuration. In the geometric configuration of Type III, ramp metering using the signal timing of 2 seconds green and 4 seconds red provided the best efficiency and safety increases when the freeway traffic volume was equal to or greater than 1,250 vphpl and the ramp traffic volume was equal to or greater than 800 vphpl. Conclusively, ramp metering increases efficiency and improves safety of freeways only at specific situations regarding geometric configuration of the ramp-freeway junction type, traffic volume of the freeway and the ramp, and the designed traffic signal of the ramp meters

A self-learning motorway traffic control system for ramp metering

Self-learning systems have attracted increasing attention in the ramp metering domain in recent years. These systems are based on reinforcement learning (RL) and can learn to control motorway traffic adaptively. However, RL-based ramp metering systems are still in their early stages and have shown limitations regarding their design and evaluation. This research aims to develop a new RL-based system (known as RAS) for ramp metering to overcome these limitations. A general framework for designing a RL-based system is proposed in this research. It contains the definition of three RL elements in a ramp metering scenario and a system structure which brings together all modules to accomplish the reinforcement learning process. Under this framework, two control algorithms for both single- and multi-objective problems are developed. In addition, to evaluate the proposed system, a software platform combining the new system and a traffic flow model is developed in the research. Based on the platform developed, a systematic evaluation is carried out through a series of simulation-based experiments. By comparing with a widely used control strategy, ALINEA, the proposed system, RAS, has shown its effectiveness in learning the optimal control actions for different control objectives in both hypothetical and real motorway networks. It is found that RAS outperforms ALINEA on improving traffic efficiency in the situation with severe congestion and on maintaining user equity when multiple on-ramps are included in the motorway network. Moreover, this research has been extended to use indirect learning technology to deal with incident-induced congestion. Tests for this extension to the work are carried out based on the platform developed and a commercial software package, AIMSUN, which have shown the potential of the extended system in tackling incident-induced congestion