Ramp metering

Freeway ramp control has been successfully implemented since mid 60's, as an efficient and viable freeway management strategy. However, the effectiveness of any ramp control strategy is largely dependent on optimum parameter values which are preferably determined prior to deployment. This is certainly the case happening to the current Stratified Zone Metering (SZM) strategy deployed in the 260 miles freeway network of Minneapolis - St. Paul metropolitan area. In order to improve the performance of the SZM, which highly depends on the values of more than 20 parameters, this research first proposed a general methodology for site-specific performance optimization of ramp control strategies using a microscopic simulation environment, as an alternative to trial and error field experimentation, and implemented the methodology to the SZM. The testing results show that the new SZM control with site-specific optimum parameter values significantly improves the performance of freeway system compared with the original SZM strategy. Secondly, this research proposed a methodology to explore the common optimum parameter values for the current SZM strategy for the whole Twin Cities freeway system, in order to replace the site-specific optimum values which have minor practical value because of the difficulties in implementation and numerous time-consumption to search the site-specific optimum values for all the freeway sections. The common parameter values are identified applying the Response Surface Methodology (RSM) based on 4 specifically selected freeway sections which can represent all types of freeway sections in Minneapolis-St. Paul metropolitan area.

The evaluation results (done in Phase II) demonstrated that the SZM strategy was generally beneficial. However, they also revealed that freeway performance degraded by reducing the ramp delays. Therefore, it is desired to improve the effectiveness of the current SZM control. There are two objectives in this study. One objective is to improve the control logic of current SZM strategy. This is accomplished through an estimation algorithm for the refined minimum release rate. The simulation results indicate that the improved SZM strategy is very effective in postponing and decreasing freeway congestion while resulting in smoother freeway traffic flow compared to the SZM strategy. The second objective of this project is to improve the current queue size estimation. Depending on the counting error of queue and passage detectors, freeway ramps are classified into three different categories, and different methods are applied respectively for improved queue size estimation. The surveillance video data were recorded and used to verify the improvement of the proposed methods. The results indicate that the proposed methods can greatly improve the accuracy of queue size estimation compared with the current methodology. Also, the proposed method was evaluated by the micro-simulation. The simulation results indicate the performance of freeway mainline is significantly improved. And the total system performance is better than the original SZM control.

A new ramp metering strategy implemented on the Twin Cities freeway system to reduce ramp waiting times was evaluated through microsimulation of freeway activity. The study compared Stratified Ramp Metering strategy with the previous Zone Metering Strategy and with no control strategy. Comparison with Zone, which was designed to favor freeway flow, showed the new strategy succeeded in greatly reducing ramp delays and lines. When compared to the results of no control strategy, it reduces freeway travel time, increases freeway speed, smoothes the flow of traffic, and reduces the number of stops. However, travel time, fuel consumption and pollutant emissions are unpredictable under the newer system. Compared to no control strategy, such measures of effectiveness may improve or worsen depending on the freeway patterns and demand. Based on these findings, the researchers will seek improvements to the design of the Stratified Ramp Metering algorithm so as to factor in disruptive traffic patterns.

Traffic congestion has become an increasingly serious problem in many cities. Ramp metering, which maintains smooth freeway mainline flow by limiting vehicle entry at entrance ramps, has been proposed and implemented in a number of metropolitan areas in and outside the U.S. to mitigate freeway congestion. This study aims to develop both efficient and equitable freeway ramp control strategies. Traffic conditions with and without ramp metering are evaluated on several representative freeways in the Twin Cities with a comprehensive set of performance measures. A unified theory for ramp metering is proposed based on a linear programming model of freeway traffic dynamics. The most efficient ramp control algorithm is found to be also the least equitable one. A novel control objective, minimizing weighted or perceived travel time, is therefore proposed to balance efficiency and equity objectives of ramp metering. This research also develops a new family of applicable ramp metering strategies, which consider both efficiency and equity, and are demonstrated in a microscopic traffic simulator. Future studies should compare various traffic control methods under the analytical framework proposed in this report. Researchers should also pursue field experiments of the proposed multi-objective ramp control strategies.

This report covers the creation of a system for monitoring vehicles in highway on-ramp queues. The initial phase of the project attempted to use a blob tracking algorithm to perform the ramp monitoring. The current system uses optical flow information to create virtual features based on trends in the optical flow. These features are clustered to form vehicle objects. These objects update themselves based on their statistics and those of other features in the image. The system has difficulties tracking vehicles when they stop at ramp queues and when they significantly occlude each other. However, the system succeeds by detecting vehicles entering and exiting ramps and can record their motion statistics as they do so. Several experimental results from ramps in the Twin Cities are presented.

An adaptive procedure is presented to estimate the time-variant capacity at consecutive weaving areas in real time. The proposed procedure uses the volume/occupancy data commonly available from single loop detectors and estimates the maximum total volume that can enter a given freeway weaving segment through time. The behavior at several weaving sites with consecutive weaving segments were analyzed, using loop and video data as well as visual observation. The online identification process with a Kalman Filter reduces estimation errors by continuously updating the parameters of the underlying models with the most recently measured data. The test results with real data show that the proposed procedure can estimate the upper limit values of the mainline flow approaching given weaving segments with reasonable accuracy. This procedure addresses the effects of entrance ramp flows, which can be controlled through ramp metering, on the maximum possible mainline volume approaching weave areas. The procedure may be directly applicable in improving ramp metering operations, and in the development of better design of freeway weaving segments.

Ramp metering is one way to address freeway traffic congestion. This study employs the Traffic Management Laboratory (TRAMLAB) to evaluate the effectiveness of Minnesota Department of Transportation's control strategy in three Twin Cities freeway sections totaling approximately 65 miles. It develops a new traffic management concept for early detection of incident-prone traffic conditions and integrates it in order to smooth flow and prevent incident occurrence, thereby further reducing delays and improving safety. The project is part of a larger program which aims to develop the TRAMLAB as part of the ITS Laboratory at the University of Minnesota. Such an environment will contain state-of-the-art traffic simulation programs and allow the development of viable, intelligent, and automated traffic-flow simulation programs and simulation systems that can function as both operational and research tools.

This project involved a detailed review of coordinated metering algorithms currently operating in the United states and a simulation analysis to examine the performance of three algorithms that represent each coordination approach; the Denver incremental coordination, the Seattle Fuzzy metering and the Minnesota explicit section-wide coordination approaches. Researchers used a macroscopic simulation model with the same geometry and traffic demand conditions. Based on the analysis results, they developed alternative metering approaches by combining conventional zone-wide control with fuzzy coordination. They also developed two new alternative procedures to estimate bottleneck capacities in real time; an adaptive estimation method using Kalman Filter and a neural-network based approach that predicts traffic volume for a given mainline location using traffic data collected from upstream and downstream detectors. Both approaches were tested with the real data collected from the sample freeway sites. The preliminary test for alternative strategies using simulation with an example freeway in Minnesota showed promising results in terms of reducing congestion and increasing throughput on the mainline. Further testing and research is recommended.