Freeway ramp metering control made easy and efficient

International audience''Model-free'' control and the related ''intelligent'' proportional-integral (PI) controllers are successfully applied to freeway ramp metering control. Implementing moreover the corresponding control strategy is straightforward. Numerical simulations on the other hand need the identification of quite complex quantities like the free flow spêed and the critical density. This is achieved thanks to new estimation techniques where the differentiation of noisy signals plays a key rôle. Several excellent computer simulations are provided and analyzed

WZ-speed harmonizer: an optimized active traffic and demand management system with speed harmonization for work zones

Speed harmonization, known as Variable Speed Limit (VSL), is implemented through a number of Changeable Message Signs (CMSs) spaced out over a stretch of highway. The CMSs display advisory speeds that may change over time and space to regulate travel speed and arrival time to a highway bottleneck and consequently reduce congestion impacts. Usually speed harmonization studies determine optimal dynamic advisory speeds to minimize travel time and delay. Delay and travel time can be further minimized if optimal location and number of CMSs are determined as well. Determining dynamic advisory speeds depends on the number and locations of CMSs; thus these variables have to be optimized simultaneously. This study is the first study which simultaneously optimized 1) dynamic advisory speeds, 2) number of CMSs, and 3) location of the CMSs to minimize total travel time and a penalty function for the number of CMSs. This problem is important as it reduces maintenance and installation costs of CMSs and enhances effectiveness of speed harmonization to reduce congestion impacts such as delay. Solving this problem is challenging because it is a large scale Mixed Integer Nonlinear Program (MINLP). Determining traffic speed and extend of queue is critical to develop a speed harmonization scheme to mitigate congestion. Past studies have used first order model or second order model as a constraint in the optimization program to determine speed and extend of queue. This study compared the first order model and the second order model versus field data and showed that the second order model was better in estimating average queue length and maximum queue length. To use the second order mode, it is necessary to calibrate the model parameters, which are relaxation time (τ) and anticipation coefficient (ϑ). This study calibrated the parameters using work zone field data to minimize error in speed and queue length estimates. The calibration is a complex process since there might be many local optimal points returning parameter values that are not physically justifiable. To overcome this issue, this study proposed a new calibration procedure. The methodology detected the behavior of the second order model in the τ- ϑ space and determined a search direction and its boundaries to avoid stopping at local minima. Although the second order model returned acceptable average and maximum queue lengths, it returned slower queue propagation pattern and faster queue dissipation pattern than field data. Thus the two- ϑ model was proposed to more maturely reflect asymmetric queue propagation and dissipation. The modification considered two different anticipation coefficients for queue propagation (ϑ_p) and queue shrinkage (ϑ_s). The two- ϑ model was calibrated using field data and results showed that the ratio of ϑ_s/ϑ_p ranges from 1.86 to 2.6 and both of them are greater than single ϑ. The solutions for the optimization program mainly tackled integrality. One source of integrality is piecewise speed-density models. Previous studies have used single-regime (single piece) models, but these models are not generally sufficient to describe congested conditions. Thus this study included the piecewise models as constraints to enhance accuracy of traffic state prediction in congested conditions. A continuous transformation approach was proposed to eliminate relevant integer variables. The methodology was used to optimize speed harmonization for a 12.5-mile roadway and a 50-min analysis duration where locations of the CMSs were given. For this problem, the methodology eliminated 30,000 binary variables and solved the problem in 23 minutes when the program included roughly 158,000 variables and 186,000 constraints. The results showed that WZSH can reduce delay by 15.7% and maximum queue length by 37.5% compared to the no speed harmonization condition. Another source of integrality is binary variables to determine location and number of CMSs. To solve the problem, three solution methods were proposed: 1) Greedy algorithm, 2) Augmented-Cut and Branch (AC&B) method, and 3) Approximate Decomposition method. The three solution methods were compared using a benchmark problem and results showed that the three methods return very close objective function values (within 1%), but the Approximate Decomposition method requires less computational resources. In particular, the Approximate Decomposition method reduced the number of WZSH solution by factors of 14.1 and 6.6 compared with Greedy Algorithm and AC&B method, respectively

Adjoint-based optimization on a network of discretized scalar conservation law PDEs with applications to coordinated ramp metering

International audienceThe adjoint method provides a computationally efficient means of calculating the gradient for applications in constrained optimization. In this article, we consider a network of scalar conservation laws with general topology, whose behavior is modified by a set of control parameters in order to minimize a given objective function. After discretizing the corresponding partial differential equation models via the Godunov scheme, we detail the computation of the gradient of the discretized system with respect to the control parameters and show that the complexity of its computation scales linearly with the number of discrete state variables for networks of small vertex degree. The method is applied to solve the problem of coordinated ramp metering on freeway networks. Numerical simulations on the I15 freeway in California demonstrate an improvement in performance and running time compared to existing methods

Robust Observability, Control, & Economics of Complex Cyber-Physical Networks

This dissertation deals with various aspects of cyber-physical system. As an example of cyber physical systems, we take transportation networks and solve various problems, namely: 1) Network Observability Problem, 2) Network Control Problem, and 3) Network Economics Problem. We have divided the dissertation into three parts which solve these three problems separately. First part of the dissertation presents a novel approach for studying the observability problem on a general network topology of a traffic network. We develop a new framework which investigates observability in terms of flow information on arcs and the routing information. Second part of the dissertation presents a feedback control design for a coordinated ramp metering problem for two consecutive on-ramps. We design a traffic allocation scheme for ramps based on Godunov’s numerical method and using distributed model. Third part of the dissertation presents a novel approach to model Vehicle Miles Traveled (VMT) dynamics and establish a methodology for designing an optimal VMT tax rate. An Optimal control problem is formulated by designing a cost function which aims to maximize the generated revenue while keeping the tax rate as low as possible. Using optimal control theory, a solution is provided to this problem. To the best knowledge of authors all the three problems have not been solved using the methods proposed in this dissertation, and hence they are a novel contribution to the field

National facilities study. Volume 2A: Facility Study Office on the National Wind Tunnel Complex

The Facility Study Office (FSO) has completed its assigned activities. The results of the FSO efforts, studies, and assessments are documented. An overview of the FSO activities as well as a general comparison of all concepts considered are provided. Detailed information is also provided for the selected concept, Concept D-Option 5. Only findings are presented. The FSO developed recommendations only as a consequence of assumptions for cost and schedule assessments

Ultralean combustion in general aviation piston engines

The role of ultralean combustion in achieving fuel economy in general aviation piston engines was investigated. The aircraft internal combustion engine was reviewed with regard to general aviation requirements, engine thermodynamics and systems. Factors affecting fuel economy such as those connected with an ideal leanout to near the gasoline lean flammability limit (ultralean operation) were analyzed. A Lycoming T10-541E engine was tested in that program (both in the test cell and in flight). Test results indicate that hydrogen addition is not necessary to operate the engine ultralean. A 17 percent improvement in fuel economy was demonstrated in flight with the Beechcraft Duke B60 by simply leaning the engine at constant cruiser power and adjusting the ignition for best timing. No detonation was encountered, and a 25,000 ft ceiling was available. Engine roughness was shown to be the limiting factor in the leanout

Electric Vehicle (EV)-Assisted Demand-Side Management in Smart Grid

While relieving the dependency on diminishing fossil fuels, Electric Vehicles (EVs) provide a promising opportunity to realise an eco-friendly and cost-effective means of transportation. However, the enormous electricity demand imposed by the wide-scale deployment of EVs can put power infrastructure under critical strain, potentially impacting the efficiency, resilience, and safety of the electric power supply. Interestingly, EVs are deferrable loads with flexible charging requirements, making them an ideal prospect for the optimisation of consumer demand for energy, referred to as demand-side management. Furthermore, with the recent introduction of Vehicle-to-Grid (V2G) technology, EVs are now able to act as residential battery systems, enabling EV customers to store energy and use them as backup power for homes or deliver back to the grid when required. Hence, this thesis studies Electric Vehicle (EV)-assisted demand-side management strategies to manage peak electricity demand, with the long-term objective of transforming to a fully EV-based transportation system without requiring major upgrades in existing grid infrastructure. Specifically, we look at ways to optimise residential EV charging and discharging for smart grid, while addressing numerous requirements from EV customer's perspective and power system's perspective. First, we develop an EV charge scheduling algorithm with the objective of tracking an arbitrary power profile. The design of the algorithm is inspired by water-filling theory in communication systems design, and the algorithm is applied to schedule EV charging following a day-ahead renewable power generation profile. Then we extend that algorithm by incorporating V2G operation to shape the load curve in residential communities via valley-filling and peak-shaving. In the proposed EV charge-discharge algorithm, EVs are distributedly coordinated by implementing a non-cooperative game. Our numerical simulation results demonstrate that the proposed algorithm is effective in flattening the load curve while satisfying all heterogeneous charge requirements across EVs. Next, we propose an algorithm for network-aware EV charging and discharging, with an emphasis on both EV customer economics and distribution network aspects. The core of the algorithm is a Quadratic Program (QP) that is formulated to minimise the operational costs accrued to EV customers while maintaining distribution feeder nodal voltage magnitudes within prescribed thresholds. By means of a receding horizon control approach, the algorithm implements the respective QP-based EV charge-discharge control sequences in near-real-time. Our simulation results demonstrate that the proposed algorithm offers significant reductions in operational costs associated with EV charging and discharging, while also mitigating under-voltage and over-voltage conditions arising from peak power flows and reverse power flows in the distribution network. Moreover, the proposed algorithm is shown to be robust to non-deterministic EV arrivals and departures. While the previous algorithm ensures a stable voltage profile across the entire distribution feeder, it is limited to balanced power distribution networks. Therefore, we next extend that algorithm to facilitate EV charging and discharging in unbalanced distribution networks. The proposed algorithm also supports distributed EV charging and discharging coordination, where EVs determine their charge-discharge profiles in parallel, using an Alternating Direction Method of Multipliers (ADMM)-based approach driven by peer-to-peer EV communication. Our simulation results confirm that the proposed distributed algorithm is computationally efficient when compared to its centralised counterpart. Moreover, the proposed algorithm is shown to be successful in terms of correcting any voltage violations stemming from non-EV load, as well as, satisfying all EV charge requirements without causing any voltage violations

NASA Tech Briefs, December 1988

This month's technical section includes forecasts for 1989 and beyond by NASA experts in the following fields: Integrated Circuits; Communications; Computational Fluid Dynamics; Ceramics; Image Processing; Sensors; Dynamic Power; Superconductivity; Artificial Intelligence; and Flow Cytometry. The quotes provide a brief overview of emerging trends, and describe inventions and innovations being developed by NASA, other government agencies, and private industry that could make a significant impact in coming years. A second bonus feature in this month's issue is the expanded subject index that begins on page 98. The index contains cross-referenced listings for all technical briefs appearing in NASA Tech Briefs during 1988

A cooperative advanced driver assistance and safety system for connected and automated vehicles

Konfliktsituationen mit mehreren Beteiligten sind für Fahrzeugführer und konventionelle Fahrerassistenz- und Sicherheitssysteme durch ihre hohe Komplexität schwer beherrschbar. So geschehen viele Unfälle auf den Straßen dieser Welt, die durch gemeinschaftlich abgestimmte Fahrmanöver verhindert oder in ihren Unfallfolgen gemindert werden könnten. Die vorliegende Arbeit adressiert dieses Potenzial und beschäftigt sich mit der Entwicklung und prototypischen Umsetzung eines fahrzeugübergreifenden kooperativen Fahrerassistenz- und Sicherheitssystems, welches mehrere Fahrzeuge über eine funkbasierte Kommunikation miteinander verbindet, sowie unfallfreie Lösungen berechnet und durchführt. In diesem Zusammenhang werden drei Forschungsfragen aufgestellt, die eine Definition von kooperativem Verhalten, eine Methode zur Koordination der anfallenden Aufgaben (Aufgabenkoordination) und eine Methode zur gemeinsamen Fahrmanöverplanung (Fahrmanöverkoordination) adressieren. Der Stand der Wissenschaft und Technik bezüglich der Forschungsfragen wird mithilfe einer systematischen Literaturstudie ermittelt, die für den Leser in einem Überblick dargestellt und hinsichtlich einer möglichen Beantwortung der Forschungsfragen ausgewertet wird. Es zeigt sich, dass die drei Forschungsfragen mit ihren Anforderungen bislang unbeantwortet sind. Zur Definition von kooperativem Verhalten werden Eigenschaften von diesem aufgezeigt, die in notwendige und hinreichende Bedingungen überführt werden. Mit der zusätzlichen Berücksichtigung von Reziprozität ergibt sich eine Definition von kooperativem Verhalten, welche durch die Steigerung des Gesamtnutzens die Unterscheidung zwischen unkooperativem Verhalten auf der einen Seite und rational-kooperativem, altruistisch-kooperativem bzw. egoistisch-kooperativem Verhalten auf der anderen Seite ermöglicht. Ein Vergleich mit den aus dem Stand der Technik bekannten Definitionen zeigt den Neuigkeitswert der entwickelten Definition. In ausgewählten Situationen wird die Definition in Simulationen angewandt.Critical situations involving multiple vehicles are rarely controlled by the associated drivers. This is one reason for the remaining number of accidents which could possibly be prevented or at least mitigated with jointly planned and conducted driving maneuvers. This potential is addressed in the dissertation at hand by developing a prototypical cooperative driver assistance and safety system coordinating multiple vehicles cooperatively using vehicle-to-vehicle-communication. In this context, three research questions reflect challenges on the road towards such a system. The research questions deal with defining a cooperative behavior, creating a method allowing to allocate coordinative tasks (task coordination), and generating a method enabling to plan joint cooperative maneuvers (maneuver coordination). Regarding the proposed research questions, a systematic literature review reveals the state-of-the-art which is first presented in an overview and afterwards used to derive open issues. The result is that the three research questions remain relevant and unanswered. In order to define cooperative behavior, properties are identified and categorized in sufficient and necessary conditions. An additional consideration of reciprocity enables the derivation of a definition of cooperative behavior which aims to increase the total utility. Cooperative behavior may further be separated into rational-cooperative, altruistic-cooperative, and egoistic-cooperative behavior. A comparison with known definitions of the state-of-the-art demonstrates the innovation of the novel definition, which is applied in chosen situations