A modeling framework for urban traffic systems microscopic simulation

In this paper a modeling framework for urban traffic systems (UTS) is presented. The model, used for agent based micro-simulation, describes both the traffic network and dynamic entities, namely vehicles, traffic lights, and pedestrians. The framework allows defining systematically the necessary components and their behavior of a model oriented to event driven simulation, which can be executed in a distributed way. In the model, the vehicles are conceived as mobile agents with decision making capabilities that interact with the environment and other entities within the traffic network, performing diverse activities according to numerous situations arisen during the simulation. A multi-level Petri net based formalism, named n-LNS is used for describing the structure of the UTS and the components behavior. The first level describes the traffic network; the second level models the behavior of diverse road network users considered as agents, and the third level specifies detailed procedures performed by the agents, namely travel plans, tasks, etc.     

Opportunities for multiagent systems and multiagent reinforcement learning in traffic control

The increasing demand for mobility in our society poses various challenges to traffic engineering, computer science in general, and artificial intelligence and multiagent systems in particular. As it is often the case, it is not possible to provide additional capacity, so that a more efficient use of the available transportation infrastructure is necessary. This relates closely to multiagent systems as many problems in traffic management and control are inherently distributed. Also, many actors in a transportation system fit very well the concept of autonomous agents: the driver, the pedestrian, the traffic expert; in some cases, also the intersection and the traffic signal controller can be regarded as an autonomous agent. However, the “agentification” of a transportation system is associated with some challenging issues: the number of agents is high, typically agents are highly adaptive, they react to changes in the environment at individual level but cause an unpredictable collective pattern, and act in a highly coupled environment. Therefore, this domain poses many challenges for standard techniques from multiagent systems such as coordination and learning. This paper has two main objectives: (i) to present problems, methods, approaches and practices in traffic engineering (especially regarding traffic signal control); and (ii) to highlight open problems and challenges so that future research in multiagent systems can address them.     

A multiagent approach to managing air traffic flow

Intelligent air traffic flow management is one of the fundamental challenges facing the Federal Aviation Administration (FAA) today. FAA estimates put weather, routing decisions and airport condition induced delays at 1,682,700 h in 2007 (FAA OPSNET Data, US Department of Transportation website, ), resulting in a staggering economic loss of over $41 billion (Joint Economic Commission Majority Staff, Your flight has been delayed again, 2008). New solutions to the flow management are needed to accommodate the threefold increase in air traffic anticipated over the next two decades. Indeed, this is a complex problem where the interactions of changing conditions (e.g., weather), conflicting priorities (e.g., different airlines), limited resources (e.g., air traffic controllers) and heavy volume (e.g., over 40,000 flights over the US airspace) demand an adaptive and robust solution. In this paper we explore a multiagent algorithm where agents use reinforcement learning (RL) to reduce congestion through local actions. Each agent is associated with a fix (a specific location in 2D space) and has one of three actions: setting separation between airplanes, ordering ground delays or performing reroutes. We simulate air traffic using FACET which is an air traffic flow simulator developed at NASA and used extensively by the FAA and industry. Our FACET simulations on both artificial and real historical data from the Chicago and New York airspaces show that agents receiving personalized rewards reduce congestion by up to 80% over agents receiving a global reward and by up to 90% over a current industry approach (Monte Carlo estimation).     

A simulation framework for modeling urban freight operations impacts on traffic networks

This paper presents a traffic simulation framework to reproduce urban freight movements, particularly concerning double-parked delivery operations. Since freight movements affect traffic and vice versa, we propose a hybrid framework that simulates traffic phenomena macroscopically and, at the same time, allows tracking delivery vehicles along their routes throughout the entire simulation. The traffic simulation framework is based on the Lighthill–Whitham–Richards macroscopic model as well as the theory of bottlenecks. The traffic component of the model can be coupled with a generic parking model. Since a novel faster version of the Lax-Hopf Formula is used in the traffic simulation, the proposed framework can perform efficient simulations. Because of this hybrid nature, the framework is suitable for simulations of large scenarios and for evaluations of City Logistic measures to tackle the last-mile problem. We show this in the second part of the study with two different measures: shifting delivery operations to off-peak hours, and prohibiting deliveries on critical streets. While the benefits deriving from the first strategy are evident, the effects of the second one are less clear because of the complexity of network interactions.     

An augmented Petri Net approach for error recovery in manufacturing systems control

The construction of error recovery Petri subnets and similar representations have received considerable attention in the literature. Previous work has presented a multi-agent system representing various levels of control in a reconfigurable architecture. Agents pertaining to production, mediation, and error recovery within such an architecture were considered. Our focus here is on the workstation level of a hierarchy where the workstation has the capability for recovery from physical errors. The implications of error recovery tasks from the perspective of control are also discussed. The approach is based on integrating Petri subnet models within a general Petri Net model for a manufacturing system environment. In essence, the error recovery plan consists of a trajectory (Petri subnet) having the detailed recovery steps that are then incorporated into the workstation control logic. The logic is based on a Timed Petri Net model of the total production system. The Petri subset models consist of a sequence of steps required to reinstate the system back to a normal state. Once generated, the recovery subnet is incorporated into the Petri Net model of the original expected (error-free) model. Petri Net augmentations pertaining to various issues are discussed in detail throughout the paper. Issues include the implication of generated error recovery trajectories in the production activities, linking of production activity Net and the error recovery subnet, potential deadlocks, the role of resources, and part handling.     

基于Petri网的实时多智能体系统建模

给出基于Petri网的实时多Agent系统建模方法,它通过Petri网建立由接口模块、目标模块、计划模块、调度模块、知识库模块、环境模块、内部模块和控制模块组成的实时Agent模型,抽象和清晰地描述出实时Agent内部和外部特征。     

A genetic approach for adaptive multiagent control in heterarchical manufacturing systems

In this paper, we apply genetic algorithms to adapt the decision strategies of autonomous controllers in a part-driven heterarchical manufacturing system. The control agents use pre-assigned decision rules only for a limited amount of time, and obey a rule replacement policy propagating the most successful rules to the subsequent populations of concurrently operating agents. The twofold objective of this approach is to automatically optimize the performance of the control system during the steady-state unperturbed conditions of the manufacturing floor, and to improve the reactions of the agents to unforeseen disturbances (e.g., failures, shortages of materials) by adapting their decision strategies. Results on a detailed discrete event model of a multiagent heterarchical manufacturing system confirm the effectiveness of the approach.     

Detailed traffic animation for urban road networks

We present a new agent-based system for detailed traffic animation on urban arterial networks with diverse junctions like signalized crossing, merging and weaving areas. To control the motion of traffic for visualization and animation purposes, we utilize the popular follow-the-leader method to simulate various vehicle types and intelligent driving styles. We also introduce a continuous lane-changing model to imitate the vehicle’s decision-making process and dynamic interactions with neighboring vehicles. By applying our approach in several typical urban traffic scenarios, we demonstrate that our system can well visualize vehicles’ behaviors in a realistic manner on complex road networks and generate immersive traffic flow animations with smooth accelerating strategies and flexible lane changes.     

A fast network partition method for large-scale urban traffic networks

In order to control the large-scale urban traffic network through hierarchical or decentralized methods, it is necessary to exploit a network partition method, which should be both effective in extracting subnetworks and fast to compute. In this paper, a new approach to calculate the correlation degree, which determines the desire for interconnection between two adjacent intersections, is first proposed. It is used as a weight of a link in an urban traffic network, which considers both the physical characteristics and the dynamic traffic information of the link. Then, a fast network division approach by optimizing the modularity, which is a criterion to distinguish the quality of the partition results, is applied to identify the subnetworks for large-scale urban traffic networks. Finally, an application to a specified urban traffic network is investigated using the proposed algorithm. The results show that it is an effective and efficient method for partitioning urban traffic networks automatically in real world.     

A hybrid model for real time simulation of urban traffic

In this paper a hybrid model is presented to predict the behavior of traffic in urban area. Intersections and on/off-ramps are modeled by Colored Timed Petri Nets while road links are modeled by a stochastic discrete time model. It is assumed that vehicle routings at each intersection are stochastic. A Particle Filter algorithm based on the hybrid model is developed to estimate the system state. The validation of the model by using real data is discussed. The limited computational effort required makes the model suitable to be used in simulation-based control schemes for urban traffic.     

混合Petri网的流体随机Petri网模型

混合Petri网和流体随机Petri网作为混合系统的建模方法,其内在的建模机制和分析方法不尽相同,而且这两种机制远未发展成熟.分析并建立它们之间相互转换的方法有助于机制本身的发展完善,并利用对方的建模原语和分析方法对系统进行多角度的分析.提出了一阶混合Petri网转换成流体随机Petri网的形式化方法及转换后变迁合并的方法,并给出了转换和合并方法的正确性证明,最后结合实例简要阐述了这两种方法的实现过程.     

Intelligent control for urban traffic systems

This paper proposes a hierarchically intelligent control procedure to resolve certain aspects of the urban traffic management problem. Three levels of the hierarchical system: the linguistic organization, the coordinator, and the on-line controller are responsible, respectively, for the management of traffic in city, neighborhood, and single intersection. Traffic situations are partitioned into five different categories, ranging from sparse to immobile traffic. For each category (except the immobile traffic which is not probed in this paper) a suitable control scheme is introduced. The control at the lowest level, single intersection, will be exerted by optimizing an assigned subgoal. Each coordination level will manage the interaction among the single intersections within its jurisdiction. A learning algorithm is incorporated at this level in order to reduce considerably the need for extensive a priori numerical computations.     

A new approach to consensus problems in discrete-time multiagent systems with time-delays

In this paper, consensus problems in discrete-time multiagent systems with time-invariant delays are considered. In order to characterize the structures of communication topologies, the concept of "pre-leader-follower" decomposition is introduced. Then, a necessary and sufficient condition for state consensus is established. By this method, consensus problems in networks with a single time-delay, as well as with multiple time-delays, are studied, and some necessary and sufficient conditions for solvability of consensus problems are obtained.     

A platform-independent metamodel for multiagent systems

Various agent-oriented methodologies and metamodels exist to design and develop multiagent systems (MAS) in an abstract manner. Frequently, these frameworks specialise on particular parts of the MAS and only few works have been invested to derive a common standardisation. This limits the impact of agent-related systems in commercial applications. In this paper, we present a metamodel for agent systems that abstracts from existing agent-oriented methodologies, programming languages, and platforms and could thus be considered as platform-independent. This metamodel defines the abstract syntax of a proposed domain-specific modelling language for MAS that is currently under development and provides furthermore the base to generate code out of the generated designs. This is done by applying the principles of model-driven development (MDD) and providing two model transformations that allow transforming the generated models into textual code that can be executed with JACK and JADE.     

基于Petri网表示的嵌入式系统模型化简规则(英文)

为了提高基于Petri网表示的嵌入式模型(PRES+)验证的效率,对模型进行了保性变换,给出了一组关于PRES+模型的化简规则,这些化简规则在原模型和简化模型之间保持完全等价关系。对两个系统模型的化简结果进一步说明了这些化简规则的有效性。     

An alternative approach for modelling and simulation of traffic data: artificial neural networks

It is assumed that there is a complicated relationship between the driver characteristics and involvement in traffic accidents. It is quite difficult to simulate the effects of these driver characteristics into the traffic accidents. The artificial neural networks (ANN) approach is proposed for training-predicting the database in this paper since it is a more flexible and assumption-free methodology. The networks are organised in different architectures and the results have been compared in order to determine the best fitting one. Finally, the best possible architecture is selected for a better representation of the survey data and the prediction of accident percentage. The predictions about the outputs for the inputs which are not used in the training of the ANN provide information about the drivers which cannot be reached in the database. The predictions are highly satisfactory and the ANNs have been found to be reliable processing systems for modelling and simulation in the traffic data assessments.     

一种新型Petri Net:双层变迁定时Petri Net

嵌入式系统的设计尤其是复杂嵌入式系统的设计，需要对系统的稳定性、可靠性等进行分析，进而对系统进行设计优化。这首先需要对系统建立适当的模型进行仿真分析，该文在深入分析现有Petri Net的基础上，构建了一种适合于嵌入式系统建模的新型Petri Net。     

一种扩展了价格信息的Petri网

提出一种扩展了价格信息的Petri网——价格Petri网，讨论了相应的分析方法和应用前景。主要工作是：为Petri网的变迁引入价格参数，并用价格变迁系统给出价格Petri网的语义；对价格Petri网进行了可达性分析并讨论了最小成本可达问题的可判定性；最后用价格Petri网建立一个业务流程的成本模型。结论是：为Petri网扩展价     

Modular fuzzy-reinforcement learning approach with internal model capabilities for multiagent systems.

To date, many researchers have proposed various methods to improve the learning ability in multiagent systems. However, most of these studies are not appropriate to more complex multiagent learning problems because the state space of each learning agent grows exponentially in terms of the number of partners present in the environment. Modeling other learning agents present in the domain as part of the state of the environment is not a realistic approach. In this paper, we combine advantages of the modular approach, fuzzy logic and the internal model in a single novel multiagent system architecture. The architecture is based on a fuzzy modular approach whose rule base is partitioned into several different modules. Each module deals with a particular agent in the environment and maps the input fuzzy sets to the action Q-values; these represent the state space of each learning module and the action space, respectively. Each module also uses an internal model table to estimate actions of the other agents. Finally, we investigate the integration of a parallel update method with the proposed architecture. Experimental results obtained on two different environments of a well-known pursuit domain show the effectiveness and robustness of the proposed multiagent architecture and learning approach.     

A swarm intelligent method for traffic light scheduling: application to real urban traffic networks

Traffic lights play an important role nowadays for solving complex and serious urban traffic problems. How to optimize the schedule of hundreds of traffic lights has become a challenging and exciting problem. This paper proposes an inner and outer cellular automaton mechanism combined with particle swarm optimization (IOCA-PSO) method to achieve a dynamic and real-time optimization scheduling of urban traffic lights. The IOCA-PSO method includes the inner cellular model (ICM), the outer cellular model (OCM), and the fitness function. Our work can be divided into following parts: (1) Concise basic transition rules and affiliated transition rules are proposed in ICM, which can help the proposed phase cycle planning (PCP) algorithm achieve a globally sophisticated scheduling and offer effective solutions for different traffic problems; (2) Benefited from the combination of cellular automaton (CA) and particle swarm optimization (PSO), the proposed inner and outer cellular PSO (IOPSO) algorithm in OCM offers a strong search ability to find out the optimal timing control; (3) The proposed fitness function can evaluate and conduct the optimization of traffic lights’ scheduling dynamically for different aims by adjusting parameters. Extensive experiments show that, compared with the PSO method, the genetic algorithm method and the RANDOM method in real cases, IOCA-PSO presents distinct improvements under different traffic conditions, which shows a high adaptability of the proposed method in urban traffic network scales under different traffic flow states, intersection numbers, and vehicle numbers.