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2016 | Book

Autonomic Road Transport Support Systems: An Introduction Read chapter Read first chapter

Autonomic Road Transport Support Systems

Editors: Thomas Leo McCluskey, Apostolos Kotsialos, Jörg P. Müller, Franziska Klügl, Omer Rana, René Schumann

Publisher: Springer International Publishing

Book Series : Autonomic Systems

Part of: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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About this book

The work on Autonomic Road Transport Support (ARTS) presented here aims at meeting the challenge of engineering autonomic behavior in Intelligent Transportation Systems (ITS) by fusing research from the disciplines of traffic engineering and autonomic computing. Ideas and techniques from leading edge artificial intelligence research have been adapted for ITS over the last 30 years. Examples include adaptive control embedded in real time traffic control systems, heuristic algorithms (e.g. in SAT-NAV systems), image processing and computer vision (e.g. in automated surveillance interpretation). Autonomic computing which is inspired from the biological example of the body’s autonomic nervous system is a more recent development. It allows for a more efficient management of heterogeneous distributed computing systems. In the area of computing, autonomic systems are endowed with a number of properties that are generally referred to as self-X properties, including self-configuration, self-healing, self-optimization, self-protection and more generally self-management. Some isolated examples of autonomic properties such as self-adaptation have found their way into ITS technology and have already proved beneficial. This edited volume provides a comprehensive introduction to Autonomic Road Transport Support (ARTS) and describes the development of ARTS systems. It starts out with the visions, opportunities and challenges, then presents the foundations of ARTS and the platforms and methods used and it closes with experiences from real-world applications and prototypes of emerging applications. This makes it suitable for researchers and practitioners in the fields of autonomic computing, traffic and transport management and engineering, AI, and software engineering. Graduate students will benefit from state-of-the-art description, the study of novel methods and the case studies provided.

Table of Contents

Frontmatter
Autonomic Road Transport Support Systems: An Introduction
Abstract
One of the most persistent problems that plague modern-day road transport facilities is the quality of service provided. Especially during rush hours, this expensive infrastructure does not operate at capacity nor does it provide the level of service required by its users. Congestion has become a problem with severe economic and environmental repercussions. Hence, efficient road traffic management is more important than ever.
Thomas Leo McCluskey, Apostolos Kotsialos, Jörg P. Müller, Franziska Klügl, Omer F. Rana, René Schumann
A Game Theory Model for Self-adapting Traffic Flows with Autonomous Navigation
Abstract
It is widely believed that road traffic as a whole self-adapts to the current situation to make travel times shorter, if the navigation devices exploit real-time traffic information. A novel theoretical approach to study this belief is the online routing game model. This chapter describes the model of online routing games in order to be able to determine how we can measure and prove the benefits of online real-time data in navigation systems. Three different notions of the benefit of online data and two classes of online routing games are defined. The class of simple naive online routing games represents the current commercial car navigation systems. Simple naive online routing games may have undesirable properties: stability is not guaranteed, single flow intensification may be possible and the worst case benefit of online data may be bigger than one, i.e. it may be a “price”. One of the approaches to avoid such problems of car navigation is intention propagation where agents share their intention and can forecast future travel times. The class of simple naive intention propagation online routing games represents the navigation systems that use shortest path planning based on forecast future travel times. In spite of exploiting intention propagation in online routing games, single flow intensification may be possible, the traffic may fluctuate and the worst case benefit may be bigger than one. These theoretical investigations point out issues that need to be solved by future research on decision strategies for self-adapting traffic flows with autonomous navigation.
László Z. Varga
Self-management in Urban Traffic Control: An Automated Planning Perspective
Abstract
Advanced urban traffic control (UTC) systems are often based on feedback algorithms. They use road traffic data which has been gathered from a couple of minutes to several years. For instance, current traffic control systems often operate on the basis of adaptive green phases and flexible coordination in road (sub)networks based on measured traffic conditions. However, these approaches are still not very efficient during unforeseen situations such as road incidents when changes in traffic are requested in a short time interval. For such anomalies, we argue that systems that can sense, interpret and deliberate with their actions and goals to be achieved are needed, taking into consideration continuous changes in state, required service level and environmental constraints. The requirement of such systems is that they can plan and act effectively after such deliberation, so that behaviourally they appear self-aware.This chapter focuses on the design of a generic architecture for autonomic UTC, to enable the network to manage itself both in normal operation and in unexpected scenarios. The reasoning and self-management aspects are implemented using automated planning techniques inspired by both the symbolic artificial intelligence and traditional control engineering. Preliminary test results of the plan generation phase of the architecture are considered and evaluated.
Falilat Jimoh, Thomas Leo McCluskey
An Experimental Review of Reinforcement Learning Algorithms for Adaptive Traffic Signal Control
Abstract
Urban traffic congestion has become a serious issue, and improving the flow of traffic through cities is critical for environmental, social and economic reasons. Improvements in Adaptive Traffic Signal Control (ATSC) have a pivotal role to play in the future development of Smart Cities and in the alleviation of traffic congestion. Here we describe an autonomic method for ATSC, namely, reinforcement learning (RL). This chapter presents a comprehensive review of the applications of RL to the traffic control problem to date, along with a case study that showcases our developing multi-agent traffic control architecture. Three different RL algorithms are presented and evaluated experimentally. We also look towards the future and discuss some important challenges that still need to be addressed in this field.
Patrick Mannion, Jim Duggan, Enda Howley
A Multiagent Approach to Modeling Autonomic Road Transport Support Systems
Abstract
In this chapter, we investigate a multiagent based approach to modeling autonomic features in urban traffic management. We provide a conceptual model of a traffic system comprising traffic participants modeled as locally autonomous agents, which act to optimize their operational and tactical decisions (e.g., route choice), and traffic management center(s) (TMC) which influence the traffic system according to dynamically selected policies. In this chapter, we focus on two autonomic features which emerge from the local decisions and actions of traffic participants and their interaction with the TMC and other vehicles: (1) Autonomic routing, in which we study how vehicle agents can individually adapt routing decisions based on local learning capabilities and traffic information communicated truthfully by a traffic management center; and (2) Autonomic grouping, i.e., collective decision-making of vehicles, which exchange route information and dynamically form and operate groups to drive in a convoy, thus aiming at higher speed and increased throughput. Communication is based on a (simulated) vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) protocols. Initial experiments are reported using a real-world traffic scenario modeled in the Aimsun software, which is extended by the decision logic of TMC and vehicles. The experiments indicate that autonomic routing and grouping can improve the performance of a traffic management network, even though negative effects such as unstable behavior can be observed in some cases.
Maksims Fiosins, Bernhard Friedrich, Jana Görmer, Dirk Mattfeld, Jörg P. Müller, Hugues Tchouankem
A Self-Optimization Traffic Model by Multilevel Formalism
Abstract
This chapter illustrates the ideas of multilevel system theory in the design of a traffic control system that embodies self-optimization properties. These ideas are described in terms of multilevel optimization problems. A simulation example is provided, explaining the methodology of multilevel optimization. The example shows the optimal control evaluation of both traffic arguments: the split of the green light and the duration of the traffic light cycle by two optimization problems. The self-optimization properties are achieved by the extension of the control variables space by an increase of goal functions and a set of requirements towards the control process. The extension is achieved by the integration of optimization problems, which are interconnected by their parameters and arguments. The multilevel theory is proposed as a primary candidate to integrate different self-optimization functionalities. The application of this formalism in transportation systems will give the ground for quantitative formalization of control processes in autonomic traffic control systems.
Todor Stoilov, Krasimira Stoilova
An Organic Computing Approach to Resilient Traffic Management
Abstract
Growing cities and the increasing number of vehicles per inhabitant lead to a higher volume of traffic in urban road networks. As space is limited and the extension of existing road infrastructure is expensive, the construction of new roads is not always an option. Therefore, it is necessary to optimise the urban road network to reduce the negative effects of traffic, for example, pollution emission and fuel consumption. Urban road networks are characterised by their large number of signalised intersections. Until now, the optimisation of these signalisations is mostly done manually through traffic engineers. As urban traffic demands tend to change constantly, it is almost impossible to foresee all runtime situations at design time. Hence, an approach is needed that is able to react adaptively at runtime to optimise signalisations of intersections according to the monitored situation. The resilient traffic management system offers a decentralised approach with communicating intersections, which are able to adapt their signalisation dynamically at runtime and establish progressive signal systems (PSS) to optimise traffic flows and the number of stops per vehicle.
Matthias Sommer, Sven Tomforde, Jörg Hähner
Autonomic Systems Design for ITS Applications: Modelling and Route Guidance
Abstract
This chapter discusses a systems design approach inspired from the autonomic nervous system for intelligent transportation system (ITS) applications. This is done not with reference to the employed computing system but with reference to the requirements of traffic engineering applications. It is argued that the design and development of autonomic traffic management systems must identify the control loop that needs to be endowed with autonomic properties and subsequently use this framework for defining a desired set of self-∗ properties. A macroscopic network modelling application is considered for showing how autonomic system design can be used for defining and obtaining self-∗ properties, with particular emphasis given on self-optimisation. The interpretation of policies followed by network operators regarding route guidance is also discussed from the perspective of autonomic ITS.
Apostolos Kotsialos, Adam Poole
Simulation Testbed for Autonomic Demand-Responsive Mobility Systems
Abstract
In this chapter, we describe an open-source simulation testbed for emerging autonomic mobility systems, in which transport vehicles and other resources are automatically managed to serve a dynamically changing transport demand. The testbed is designed for testing and evaluation of various planning, coordination and resource allocation mechanisms for the control and management of autonomic mobility systems. It supports all stages of the experimentation process, from the implementation of tested control mechanisms and the definition of experiment scenarios through simulation execution up to the analysis and interpretation of the results. The testbed aims to accelerate the development of control mechanisms for autonomic mobility systems and to facilitate their mutual comparison using well-defined benchmark scenarios. We also demonstrate how it can be used to select the most suitable control mechanism for a specific use case and to approximate operational costs and initial investments needed to deploy a specific autonomic mobility system.
Michal Čertický, Michal Jakob, Radek Píbil
Multi-Agent Traffic Simulation for Development and Validation of Autonomic Car-to-Car Systems
Abstract
In this chapter, we present the concept of an integrated multi-agent simulation platform to support the development and validation of autonomic cooperative car-to-car systems. The simulation allows to validate the car-to-car coordination strategies in various traffic scenarios in variable technology penetration levels (i.e. mixing different strategies) and user acceptance of such system as an external observer and/or as a part of the traffic (human in the loop with intelligent cooperative guidance system). The platform combines features of realistic driving simulation, traffic simulation with flexible level of detail and AI controlled vehicles. The principal idea of the platform is to allow the development and study of complex autonomic distributed car-to-car systems for vehicles coordination. The platform provides a development environment and a tool chain that is necessary for the validation of such complex systems. Autonomic car-to-car systems are based on coordination mechanisms between agents, where an agent represents a reasoning unit of a single vehicle. The road traffic is modelled as a multi-agent system of cooperative agents. The interaction between the agents brings autonomic properties into the emerged system (e.g. the traffic adapts to a blockage of a lane and vehicles merge into a second lane). The system also exhibits autonomic properties from a single user perspective. The driver approaches the system in a form of a driver assistance system—we can refer it as an autonomic driver assistance system. The driver is interacting only with the assistance system via a human-machine interface (HMI). The autonomic driver assistance system is hiding the complexity of multi-agent interactions from the user. The related agent of the single vehicle is responsible for an interaction with other agents in the system without any user’s intervention.
Martin Schaefer, Jiří Vokřínek, Daniele Pinotti, Fabio Tango
Performance Maintenance of ARTS Systems
Finding and Managing Performance Deterioration by Undesired System Adaptation
Abstract
Autonomic Road Transportation Support (ARTS) systems will operate with adaptive system behavior to make them self-managing. In this chapter, we discuss methods for analyzing the performance of these kinds of systems. The performance of these systems not only depends on the state of the environment but also on the adaptation the system has made so far. Analyzing the performance can hardly be done analytically; we have to rely on empirical studies. A structured way for implementing such analysis of adaptive systems, like ARTS, is presented. Furthermore, we discuss different approaches and provide examples how such problems could be encountered during the design of ARTS systems.
René Schumann
Learning-Based Control Algorithm for Ramp Metering
Abstract
Significant slowdowns in road traffic induced by increased traffic demand cause breakdowns and, consequently, congestion on roads. On urban highways, these congestion problems are most noticeable near on-ramps. To resolve traffic congestion on urban highways, it is necessary to apply new traffic control approaches like ramp metering, variable speed limit control (VSLC), etc. Today’s cooperative ramp metering algorithms adjust the metering rate for every on-ramp according to the overall traffic state on the highway and can establish additional cooperation with other traffic control subsystems. To avoid some problems of usability and effectiveness of today’s complex highway control systems, an approach based on autonomic properties (self-learning, self-adaptation, etc.) is proposed in this chapter. A new cooperative control method based on an adaptive neuro-fuzzy inference system is described. It can establish cooperation between VSLC and ramp metering. The new solution is tested using the CTMSIM macroscopic highway traffic simulator and Zagreb bypass as test model.
Martin Gregurić, Edouard Ivanjko, Sadko Mandžuka
An Autonomic Methodology for Embedding Self-tuning Competence in Online Traffic Control Systems
Abstract
Recent advances in technology, control and computer science play a key role towards the design and deployment of the next generation of intelligent transportation systems (ITS). The architecture of such complex systems is crucial to include supporting algorithms that can embody autonomic properties within the existing ITS strategies. This chapter presents a recently developed adaptive optimization algorithm that combines methodologies from the fields of traffic engineering, automatic control, optimization and machine learning in order to embed self-tuning properties in traffic control systems. The derived adaptive fine-tuning (AFT) algorithm comprises an autonomic tool that can be used in online ITS applications of various types, in order to optimize their performance by automatically fine-tuning the system’s design parameters. The algorithm has been evaluated in simulation experiments, examining its ability and efficiency to fine-tune in real time the design parameters of a number of traffic control systems, including signal control for urban road networks. Field results are in progress for the urban network of Chania, Greece, as well as for energy-efficient building control. Some promising preliminary field results for the traffic control problem of Chania are presented here.
Anastasios Kouvelas, Diamantis Manolis, Elias Kosmatopoulos, Ioannis Papamichail, Markos Papageorgiou
Electric Vehicles in Road Transport and Electric Power Networks
Abstract
Electric vehicle (EV) market penetration is expected to increase in the next few years. Transport electrification will affect both the road transport and the electric power network, as EV charging will be influenced by events that take place on the road network (such as congestion, weather, etc.) which subsequently have an impact on the potential load imposed on an electricity grid (based on where EV charging takes place). An EV is therefore seen as a link between transport and energy systems, and their interdependencies are important. In this chapter an EV is modeled as an autonomous agent with a set of predefined high-level goals (such as traveling from origin to destination). Algorithms for the routing and charging procedures of EVs are presented. A multi-agent simulation is carried out, based on a number of scenarios, which demonstrates interactions between transport and energy systems, showing how an EV agent is able to adapt its behavior based on changes within each of these systems.
Charalampos Marmaras, Erotokritos Xydas, Liana M. Cipcigan, Omer Rana, Franziska Klügl
Traffic Signal Control with Autonomic Features
Abstract
Inspired by diverse organic systems, autonomic computing is a rapidly growing field in computing science. To highlight this advancement, this chapter summarises the autonomic features utilised in a traffic signal control in the form of an operational control system, not simply a simulation study. In addition, the real-time simulation is used to refine the raw sensor data into a comprehensive picture of the traffic situation. We apply the multi-agent approach both for controlling the signals and for modelling the prevailing traffic situation. In contrast to most traffic signal control studies, the basic agent is one signal (head) also referred to as a signal group. The multi-agent process occurs between individual signal agents, which have autonomy to negotiate their timing, phasing, and priorities, limited only by the traffic safety requirements. The key contribution of this chapter lies not in a single method but rather in a combination of methods with autonomic properties. This unique combination involves a real-time microsimulation together with a signal group control and fuzzy logic supported by self-calibration and self-optimisation. The findings here are based on multiple research projects conducted at the Helsinki University of Technology (now Aalto University). Furthermore, we outline the basic concepts, methods, and some of the results. For detailed results and setup of experiments, we refer to the previous publications of the authors.
Iisakki Kosonen, Xiaoliang Ma
TIMIPLAN: A Tool for Transportation Tasks
Abstract
Multi-modal transportation is a logistics problem in which a set of goods has to be transported to different places, with the combination of at least two modes of transport, without a change of container for the goods. In such tasks, in many cases, the decisions are inefficiently made by human operators. Human operators receive plenty of information from several and varied sources, and thus they suffer from information overload. To solve efficiently the multi-modal transportation problem, the management cannot rely only on the experience of the human operators. A prospective way to tackle the complexity of the problem for multi-modal transportation is to apply the concept of autonomic behavior. The goal of this chapter is to describe timiplan, a software tool that solves multi-modal transportation problems developed in cooperation with the Spanish company Acciona Transmediterránea. The tool includes a solver that combines linear programming (LP) with automated planning (AP) techniques. To facilitate its integration in the company, the application follows a mixed-initiative approach allowing the users to modify the plans provided by the planning module. The system also integrates an execution component that monitors the execution, keeping track of failures and replanning if necessary. Thus, timiplan showcases some of the needed autonomic objectives for self-management in future software applied to road transport software system.
Javier García, Álvaro Torralba, José E. Florez, Daniel Borrajo, Carlos Linares López, Ángel García-Olaya
Applying the PAUSETA Protocol in Traffic Management Plans
Abstract
The application of a traffic management plan (TMP) currently involves a manual negotiation between some agencies. Human operators from these agencies must negotiate with each other about who has to give each of the resources required by the TMP. This chapter proposes to use a new protocol, the Progressive Adaptive User Selection Environment by Type Agreements (PAUSETA) protocol, to support the autonomous deploying of a TMP. This protocol relies on a distributed combinatorial auction that preserves confidentiality and legal competences in the use of each resource in each agency. PAUSETA addresses the resources required by a TMP by type, that is, in the natural way that is given in a TMP, and it exhibits some self-* properties of an autonomic system. This protocol has been applied to a real traffic scenario to deal with a traffic incident. The analysis of the simulations performed show that PAUSETA provides results closely related to the ones obtained by a centralized system, thereby avoiding issues with regard to confidentiality about data or legal competences.
Miguel Prades-Farrón, Luis A. García, Vicente R. Tomás
Backmatter
Metadata
Title
Autonomic Road Transport Support Systems
Editors
Thomas Leo McCluskey
Apostolos Kotsialos
Jörg P. Müller
Franziska Klügl
Omer Rana
René Schumann
Copyright Year
2016
Electronic ISBN
978-3-319-25808-9
Print ISBN
978-3-319-25806-5
DOI
https://doi.org/10.1007/978-3-319-25808-9

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