Local Freeway Ramp Metering using Self-Adjusted Fuzzy Controller

A self-adjusted fuzzy local ramp metering strategy is proposed to keep the mainline traffic state and the on-ramp queue length at reasonable levels. The fuzzy ramp metering strategy (FRMS) takes the following variables as inputs: error between desired density and measured density, change-in-error and on-ramp queue length. On-ramp metering flow is decided by these variables. It is difficult to construct fuzzy rules for a three-dimension inputs fuzzy controller based on expert knowledge, so the proposed FRMS generates fuzzy control rules by an analytic expression with correction factors. The correction factors reflect the weights upon linguistic variables of inputs and can be regulated according to actual traffic state of mainline and on-ramp. The proposed FRMS not only simplifies the process of rules definition for a multi-dimension fuzzy controller, but also has function of self-adjusted control rules. To examine the proposed FRMS, a freeway stretch in Los Angeles is simulated with distributed models. The proposed FRMS is also compared with an existing T-S FRMS and PI-ALINEA in the simulation experiments which cover different on-ramp inflow scenarios. Simulation results show the proposed FRMS provides improved adaptation to various scenarios and superiority in striking a balance between the mainline and on-ramp performances

Computational Intelligence in Highway Management: A Review

Highway management systems are used to improve safety and driving comfort on highways by using control strategies and providing information and warnings to drivers. They use several strategies starting from speed and lane management, through incident detection and warning systems, ramp metering, weather information up to, for example, informing drivers about alternative roads. This paper provides a review of the existing approaches to highway management systems, particularly speed harmonization and ramp metering. It is focused only on modern and advanced approaches, such as soft computing, multi-agent methods and their interconnection. Its objective is to provide guidance in the wide field of highway management and to point out the most relevant recent activities which demonstrate that development in the field of highway management is still important and that the existing research exhibits potential for further enhancement

Literature Review of Advancements in Adaptive Ramp Metering

Over a period spanning more than 30 years, several ramp metering algorithms have been developed to improve the operation of freeways. Many of these algorithms were deployed in several regions of the world, and field evaluations have shown their significance to improve traffic conditions on freeways and ramps. Previous reviews of ramp metering algorithms focused more on the research outcomes and evaluations of traditional metering algorithms developed in the early stage of ramp metering research. The purpose of this paper is to cover the more recent developments in ramp metering in relation to the traditional metering strategies. Several local and coordinated ramp metering algorithms were reviewed. In summary, Asservissement Linéaire d’Entrée Autoroutière (ALINEA) was found to be the most widely deployed local ramp metering strategy. The algorithm is simple and implementation costs less than other strategies. It also guarantees the targeted performance goals provided that the on-ramp has sufficient storage. Several extensions were proposed in the literature to fine-tune its performance. Among the coordinated metering strategies, zone based metering is simple to implement and easy to re-configure. System-wide adaptive ramp metering (SWARM) algorithm is more sensitive to calibrate for accurate prediction of traffic states. Heuristic ramp-metering coordination (HERO) algorithm can be useful if both local and coordinated control are desired particularly if the local control is using ALINEA. Fuzzy logic based algorithms are gaining popularity because of the simplicity and the fast re-configuration capability. Advanced real time metering system (ARMS) seems theoretically promising because of its proactive nature to prevent congestion; however, its performance is highly dependent upon accurate predictions. Finally, some guidelines were proposed for future research to develop new proposals and to extend the existing algorithms for guaranteed performance solutions.Scopu

Prediction of Short-term Traffic Variables using Intelligent Swarm-based Neural Networks

This paper presents an innovative algorithm integrated with particle swarm optimization and artificial neural networks to develop short-term traffic flow predictors, which are intended to provide traffic flow forecasting information for traffic management in order to reduce traffic congestion and improve mobility of transportation. The proposed algorithm aims to address the issues of development of short-term traffic flow predictors which have not been addressed fully in the current literature namely that: a) strongly non-linear characteristics are unavoidable in traffic flow data; b) memory space for implementation of short-term traffic flow predictors is limited; c) specification of model structures for short-term traffic flow predictors which do not involve trial and error methods based on human expertise; d) adaptation to newly-captured, traffic flow data is required. The proposed algorithm was applied to forecast traffic flow conditions on a section of freeway in Western Australia, whose traffic flow information is newly-captured. These results clearly demonstrate the effectiveness of using the proposed algorithm for real-time traffic flow forecasting

A model-free control strategy for an experimental greenhouse with an application to fault accommodation

Writing down mathematical models of agricultural greenhouses and regulating them via advanced controllers are challenging tasks since strong perturbations, like meteorological variations, have to be taken into account. This is why we are developing here a new model-free control approach and the corresponding intelligent controllers, where the need of a good model disappears. This setting, which has been introduced quite recently and is easy to implement, is already successful in many engineering domains. Tests on a concrete greenhouse and comparisons with Boolean controllers are reported. They not only demonstrate an excellent climate control, where the reference may be modified in a straightforward way, but also an efficient fault accommodation with respect to the actuators

Selection of Significant On-Road Sensor Data for Short-Term Traffic Flow Forecasting Using the Taguchi Method

Over the past two decades, neural networks have been applied to develop short-term traffic flow predictors. The past traffic flow data, captured by on-road sensors, is used as input patterns of neural networks to forecast future traffic flow conditions. The amount of input patterns captured by the on-road sensors is usually huge, but not all input patterns are useful when trying to predict the future traffic flow. The inclusion of useless input patterns is not effective to developing neural network models. Therefore, the selection of appropriate input patterns, which are significant for short-term traffic flow forecasting, is essential. This can be conducted by setting an appropriate configuration of input nodes of the neural network; however, this is usually conducted by trial and error. In this paper, the Taguchi method, which is a robust and systematic optimization approach for designing reliable and high-quality models, is proposed for the purpose of determining an appropriate neural network configuration, in terms of input nodes, in order to capture useful input patterns for traffic flow forecasting. The effectiveness of the Taguchi method is demonstrated by a case study, which aims to develop a short-term traffic flow predictor based on past traffic flow data captured by on-road sensors located on a Western Australia freeway. Three advantages of using the Taguchi method were demonstrated: 1) short-term traffic flow predictors with high accuracy can be designed; 2) the development time for short-term traffic flow predictors is reasonable; and 3) the accuracy of short-term traffic flow predictors is robust with respect to the initial settings of the neural network parameters during the learning phase

Design and Evaluate Coordinated Ramp Metering Strategies for Utah Freeways

MPC-641During the past few decades, ramp metering control has been widely implemented in many U.S. states, including Utah. Numerous studies and applications have demonstrated that ramp metering control is an effective strategy to reduce overall freeway congestion by managing the amount of traffic entering the freeway. Ramp metering controllers can be implemented as coordinated or uncoordinated systems. Currently, Utah freeway on-ramps are operated in an uncoordinated way. Despite improvements to the operational efficiency of mainline flows, uncoordinated ramp metering will inevitably create additional delays to the ramp flows. Therefore, this project aims to assist the Utah Department of Transportation (UDOT) in deploying coordinated ramp metering systems and evaluating the performance of deployed systems. First, we leverage a method to identify existing freeway bottlenecks using current UDOT datasets, including PeMs and ClearGuide. Based on this, we select the site that may benefit from coordinated ramp metering from those determined locations. A VISSIM model is then developed for this selected corridor and the VISSIM model is calibrated based on collected traffic flow data. We apply the calibrated VISSIM model to conduct simulations to evaluate system performance under different freeway mainline congestion levels. Finally, the calibrated VISSIM model is leveraged to evaluate the coordinated ramp metering strategy of the bottleneck algorithm from both operational and safety aspects

On Learning based Parameter Calibration and Ramp Metering of freeway Traffic Systems

Ph.DDOCTOR OF PHILOSOPH

Data Analytic Approach to Support the Activation of Special Signal Timing Plans in Response to Congestion

Improving arterial network performance has become a major challenge that is significantly influenced by signal timing control. In recent years, transportation agencies have begun focusing on Active Arterial Management Program (AAM) strategies to manage the performance of arterial streets under the flagship of Transportation Systems Management & Operations (TSM&O) initiatives. The activation of special traffic signal plans during non-recurrent events is an essential component of AAM and can provide significant benefits in managing congestion. Events such as surges in demands or lane blockages can create queue spillbacks, even during off-peak periods resulting in delays and spillbacks to upstream intersections. To address this issue, some transportation agencies have started implementing processes to change the signal timing in real time based on traffic signal engineer/expert observations of incident and traffic conditions at the intersections upstream and downstream of congested locations. This dissertation develops methods to automate and enhance such decisions made at traffic management centers. First, a method is developed to learn from experts’ decisions by utilizing a combination of Recursive Partitioning and Regression Decision Tree (RPART) and Fuzzy Rule-Based System (FRBS) to deal with the vagueness and uncertainty of human decisions. This study demonstrates the effectiveness of this method in selecting plans to reduce congestion during non-recurrent events. However, the method can only recommend the changes in green time to the movement affected by the incident and does not give an optimized solution that considers all movements. Thus, there was a need to extend the method to decide how the reduction of green times should be distributed to other movements at the intersection. Considering the above, this dissertation further develops a method to derive optimized signal timing plans during non-recurrent congestion that considers the operations of the critical direction impacted by the incident, the overall corridor, as well as the critical intersection movement performance. The prerequisite of optimizing the signal plans is the accurate measurements of traffic flow conditions and turning movement counts. It is also important to calibrate any utilized simulation and optimization models to replicate the field traffic states according to field traffic conditions and local driver behaviors. This study evaluates the identified special signal-timing plan based on both the optimization and the DT and FRBS approaches. Although the DT and FRBS model outputs are able to reduce the existing queue and improve all other performance measures, the evaluation results show that the special signal timing plan obtained from the optimization method produced better performance compared to the DT and FRBS approaches for all of the evaluated non-recurrent conditions. However, there are opportunities to combine both approaches for the best selection of signal plans