Cooperative control of high-speed trains for headway regulation: A self-triggered model predictive control based approach

The advanced train-to-train and train-to-ground communication technologies equipped in high-speed railways have the potential to allow trains to follow each with a steady headway and improve the safety and performance of the railway systems. A key enabler is a train control system that is able to respond to unforeseen disturbances in the system (e.g., incidents, train delays), and to adjust and coordinate the train headways and speeds. This paper proposes a multi-train cooperative control model based on the dynamic features during train longitude movement to adjust train following headway. In particular, our model simultaneously considers several practical constraints, e.g., train controller output constraints, safe train following distance, as well as communication delays and resources. Then, this control problem is solved through a rolling horizon approach by calculating the Riccati equation with Lagrangian multipliers. Due to the practical communication resource constraints and riding comfort requirement, we also improved the rolling horizon approach into a novel self-triggered model predictive control scheme to overcome these issues. Finally, two case studies are given through simulation experiments. The simulation results are analyzed which demonstrate the effectiveness of the proposed approach

Lyapunov Based Stability Analysis for Metro Lines

In this work a direct method to measure the stability of metro system lines with respect to a previously constructed time schedule is presented. For this purpose we first model saturation effects using a real time discrete space state representation and then apply a Lyapunov-based stability analysis considering time delays of trains as disturbances. As a result we have been able to define a new set of indexes that relate time delays with the validity of the actual time schedule when falling inside a particular ‘stability area’. Results obtained in a simulated environment show that the new stability indexes are able to evaluate quantitatively and qualitatively the effects of saturation in metro lines as well as predict the need for rescheduling Keywords: metro system, stability, planning, genetic algorithm, artificial intelligence. 1 Introduction The dynamics of metro line systems have been deeply studied by several researchers [1–5,7–10]. Most of these dynamical models are based on the Sasama and Ohkawa [9] linear model. It is well known that such kind of linear models, usually yield simple formulation, implementation and simulation. As a result, some dynamic traffic linear controllers [1–3,7] and real time simulators [3,4] have been proposed.In this work a direct method to measure the stability of metro system lines with respect to a previously constructed time schedule is presented. For this purpose we first model saturation effects using a real time discrete space state representation and then apply a Lyapunov-based stability analysis considering time delays of trains as disturbances. As a result we have been able to define a new set of indexes that relate time delays with the validity of the actual time schedule when falling inside a particular ‘stability area’. Results obtained in a simulated environment show that the new stability indexes are able to evaluate quantitatively and qualitatively the effects of saturation in metro lines as well as predict the need for rescheduling Keywords: metro system, stability, planning, genetic algorithm, artificial intelligence. 1 Introduction The dynamics of metro line systems have been deeply studied by several researchers [1–5,7–10]. Most of these dynamical models are based on the Sasama and Ohkawa [9] linear model. It is well known that such kind of linear models, usually yield simple formulation, implementation and simulation. As a result, some dynamic traffic linear controllers [1–3,7] and real time simulators [3,4] have been proposed

Research challenges on energy-efficient networking design

The networking research community has started looking into key questions on energy efficiency of communication networks. The European Commission activated under the FP7 the TREND Network of Excellence with the goal of establishing the integration of the EU research community in green networking with a long perspective to consolidate the European leadership in the field. TREND integrates the activities of major European players in networking, including manufacturers, operators, research centers, to quantitatively assess the energy demand of current and future telecom infrastructures, and to design energy-efficient, scalable and sustainable future networks. This paper describes the main results of the TREND research community and concludes with a roadmap describing the next steps for standardization, regulation agencies and research in both academia and industry.The research leading to these results has received funding from the EU 7th Framework Programme (FP7/2007–2013) under Grant Agreement No. 257740 (NoE TREND)

Optimal Pricing Effect on Equilibrium Behaviors of Delay-Sensitive Users in Cognitive Radio Networks

This paper studies price-based spectrum access control in cognitive radio networks, which characterizes network operators' service provisions to delay-sensitive secondary users (SUs) via pricing strategies. Based on the two paradigms of shared-use and exclusive-use dynamic spectrum access (DSA), we examine three network scenarios corresponding to three types of secondary markets. In the first monopoly market with one operator using opportunistic shared-use DSA, we study the operator's pricing effect on the equilibrium behaviors of self-optimizing SUs in a queueing system. %This queue represents the congestion of the multiple SUs sharing the operator's single \ON-\OFF channel that models the primary users (PUs) traffic. We provide a queueing delay analysis with the general distributions of the SU service time and PU traffic using the renewal theory. In terms of SUs, we show that there exists a unique Nash equilibrium in a non-cooperative game where SUs are players employing individual optimal strategies. We also provide a sufficient condition and iterative algorithms for equilibrium convergence. In terms of operators, two pricing mechanisms are proposed with different goals: revenue maximization and social welfare maximization. In the second monopoly market, an operator exploiting exclusive-use DSA has many channels that will be allocated separately to each entering SU. We also analyze the pricing effect on the equilibrium behaviors of the SUs and the revenue-optimal and socially-optimal pricing strategies of the operator in this market. In the third duopoly market, we study a price competition between two operators employing shared-use and exclusive-use DSA, respectively, as a two-stage Stackelberg game. Using a backward induction method, we show that there exists a unique equilibrium for this game and investigate the equilibrium convergence.Comment: 30 pages, one column, double spac

Traffic modeling and state feedback control for metro lines

Abstract-This paper deals with traffic modeling and control design for high-frequency metro lines. A complete discrete-event traffic model pointing out the natural instability of metro lines is presented. The traffic stability properties are analyzed and easyto-implement state feedback traffic control algorithms are designed, which guarantee the system stability. Simulations illustrate the methodology. I

Why Does Public Transport Not Arrive on Time? The Pervasiveness of Equal Headway Instability

BACKGROUND: The equal headway instability phenomenon is pervasive in public transport systems. This instability is characterized by an aggregation of vehicles that causes inefficient service. While equal headway instability is common, it has not been studied independently of a particular scenario. However, the phenomenon is apparent in many transport systems and can be modeled and rectified in abstraction. METHODOLOGY: We present a multi-agent simulation where a default method with no restrictions always leads to unstable headways. We discuss two methods that attempt to achieve equal headways, called minimum and maximum. Since one parameter of the methods depends on the passenger density, adaptive versions--where the relevant parameter is adjusted automatically--are also put forward. Our results show that the adaptive maximum method improves significantly over the default method. The model and simulation give insights of the interplay between transport design and passenger behavior. Finally, we provide technological and social suggestions for engineers and passengers to help achieve equal headways and thus reduce delays. CONCLUSIONS: The equal headway instability phenomenon can be avoided with the suggested technological and social measures

Control of Energy Storage

Energy storage can provide numerous beneficial services and cost savings within the electricity grid, especially when facing future challenges like renewable and electric vehicle (EV) integration. Public bodies, private companies and individuals are deploying storage facilities for several purposes, including arbitrage, grid support, renewable generation, and demand-side management. Storage deployment can therefore yield benefits like reduced frequency fluctuation, better asset utilisation and more predictable power profiles. Such uses of energy storage can reduce the cost of energy, reduce the strain on the grid, reduce the environmental impact of energy use, and prepare the network for future challenges. This Special Issue of Energies explore the latest developments in the control of energy storage in support of the wider energy network, and focus on the control of storage rather than the storage technology itself

Adaptive Railway Traffic Control using Approximate Dynamic Programming

Railway networks around the world have become challenging to operate in recent decades, with a mixture of track layouts running several different classes of trains with varying operational speeds. This complexity has come about as a result of the sustained increase in passenger numbers where in many countries railways are now more popular than ever before as means of commuting to cities. To address operational challenges, governments and railway undertakings are encouraging development of intelligent and digital transport systems to regulate and optimise train operations in real-time to increase capacity and customer satisfaction by improved usage of existing railway infrastructure. Accordingly, this thesis presents an adaptive railway traffic control system for realtime operations based on a data-based approximate dynamic programming (ADP) approach with integrated reinforcement learning (RL). By assessing requirements and opportunities, the controller aims to reduce delays resulting from trains that entered a control area behind schedule by re-scheduling control plans in real-time at critical locations in a timely manner. The present data-based approach depends on an approximation to the value function of dynamic programming after optimisation from a specified state, which is estimated dynamically from operational experience using RL techniques. By using this approximation, ADP avoids extensive explicit evaluation of performance and so reduces the computational burden substantially. In this thesis, formulations of the approximation function and variants of the RL learning techniques used to estimate it are explored. Evaluation of this controller shows considerable improvements in delays by comparison with current industry practices