Denise de Oliveira

Using cooperative mediation to coordinate traffic lights: a case study

Several approaches tackle the problem of reducing traffic jams. A class of these approaches deals with coordination of traffic lights in order to allow vehicles traveling in a given direction to pass an arterial without stopping at junctions. In short, classical approaches, which are mostly based on offline and centralized determination of the prioritized direction, are quite inflexible since they cannot cope with dynamic changes in the traffic volume. More flexible approaches have been proposed based on implicit coordination and implicit communication (e.g. derived from game theory and swarm intelligence). These have advantages as well as shortcomings. The present paper presents an approach based on cooperative mediation which is a compromise between totally autonomous coordination with implicit communication and the classical centralized solution. We use a distributed constraint optimization algorithm in a dynamic scenario, showing that the mediation is able to reduce the frequency of miscoordination.

ITSUMO: an Intelligent Transportation System for Urban Mobility

This paper presents an overview of ITSUMO, a microscopic traffic simulator based on cellular automata. The implementation uses agent technologies with a bottom-up philosophy in mind. We give an overview of the system and some details of its modules (data, simulation, driver and information/visualization).

A Swarm-Based Approach for Selection of Signal Plans in Urban Scenarios

This paper presents a swarm approach to the problem of synchronisation of traffic lights in order to reduce traffic jams in urban scenarios. Other approaches for reducing jams have been proposed. A classical one is to coordinate or synchronise traffic lights so that vehicles can traverse an arterial in one direction, with a specific speed, without stopping. Coordination here means that if appropriate signal plans are selected to run at the adjacent traffic lights, a “green wave” is built so that drivers do not have to stop at junctions. This approach works fine in traffic networks with defined traffic flow patterns like for instance morning flow towards downtown and its similar afternoon rush hour. However, in cities where these patterns are not clear, that approach may not be effective. This is clearly the case in big cities where the business centres are no longer located exclusively downtown.

To adapt or not to adapt: consequences of adapting driver and traffic light agents

One way to cope with the increasing traffic demand is to integrate standard solutions with more intelligent control measures. However, the result of possible interferences between intelligent control or information provision tools and other components of the overall traffic system is not easily predictable. This paper discusses the effects of integrating co-adaptive decision-making regarding route choices (by drivers) and control measures (by traffic lights). The motivation behind this is that optimization of traffic light control is starting to be integrated with navigation support for drivers. We use microscopic, agent-based modelling and simulation, in opposition to the classical network analysis, as this work focuses on the effect of local adaptation. In a scenario that exhibits features comparable to real-world networks, we evaluate different types of adaptation by drivers and by traffic lights, based on local perceptions. In order to compare the performance, we have also used a global level optimization method based on genetic algorithms.

A Swarm Based Approach for Task Allocation in Dynamic Agents Organizations

One of the well-studied issues in multi-agent systems is the standard action-selection and sequencing problem where a goal task can be performed in different ways, by different agents.Tasks have constraints while agents have different characteristics such as capacity, access to resources, motivations, etc. This class of problems has been tackled under different approaches. Moreover, in open, dynamic environments, agents must be able to adapt to the changing organizational goals, available resources, their relationships to another agents, and so on. This problem is a key one in multi-agent systems and relates to models of learning and adaptation, such as those observed among social insects. The present paper tackles the process of generating, adapting, and changing multiagent organization dynamically at system runtime, using a swarm inspired approach. This approach is used here mainly for task allocation with low need of pre-planning and specification, and no need of explicit coordination. The results of our approach and another quantitative one are compared here and it is shown that in dynamic domains, the agents adapt to changes in the organization, just as social insects do.

ITSUMO: an intelligent transportation system for urban mobility

It is well-known that big cities suffer from traffic congestion and all consequences that come with it. This is an especial problem in cities in developing countries where the public transportation system is not reliable and where the fleet of vehicles tend to be old thus increasing air pollution. There is no turnkey solution for this problem, but several improvements have been suggested in the field of urban and traffic management, provided an information system is built which can provide information to both the traffic experts and the user of the system. Such an information system has to incorporate features of an ITS and an ATIS. An underline assumption is that there is a simulation model to provide certain kinds of information in forecast. This paper discusses the model and implementation of such an information system which is based on a microscopic model of simulation and on cellular automata and is implemented using agent technologies and with a bottom-up philosophy in mind. We give here an overview of the project, the details of the modules (data, simulation, driver and information/visualization), as well as discuss an application of the simulation tool.

A Multiagent, Multiobjective Clustering Algorithm

This chapter presents MACC, a multi ant colony and multiobjective clustering algorithm that can handle distributed data, a typical necessity in scenarios involving many agents. This approach is based on independent ant colonies, each one trying to optimize one particular feature objective. The multiobjective clustering process is performed by combining the results of all colonies. Experimental evaluation shows that MACC is able to find better results than the case where colonies optimize a single objective separately.

Traffic lights control with adaptive group formation based on swarm intelligence

Several traffic control approaches address the problem of reducing traffic jams. A class of them deals with coordination of traffic lights to allow vehicles traveling in a given direction to pass an arterial without stopping. However, in cities where the business centers are no longer located exclusively downtown, this approach is not appropriate: simple offline optimization of the synchronization in one arterial alone cannot cope with changing traffic patterns.

The MAS-SOC Approach to Multi-agent Based Simulation

This paper presents the MAS-SOC approach to Multi-Agent Based Simulation. It integrates specific agent technologies for agent programming and communication, and includes a language we have designed for the specification of the environment to be shared by the agents in a simulation. A graphical interface is provided which helps the development of agent simulations (by managing libraries of simulation components and automatically generating appropriate source codes for the associated interpreters). In future improvements of this approach, we aim at including extra features that would favour the development of social simulations in particular, and to further improve the user interface so as to facilitate the access of social scientists to the design and implementation of multi-agent based simulations. In order to assess our platform for agent simulation, a case study on social aspects of the production and occupation of urban spaces is under development; this paper also briefly describes that social simulation and its preliminary results.

A swarm based approach to adapt the structural dimension of agents' organizations

One of the well studied issues in multi-agent systems is the standard action-selection problem where a goal task can be performed in different ways, by different agents. Also the sequence of these actions can influence the goal achievement or its quality. This class of problems has been tackled under different approaches. At the high-level coordination one, the specification of the organizational issues is crucial. However, in dynamic environments, agents must be able to adapt to the changing organizational goals, available resources, their relationships to the presence of another agents, and so on. This problem is a key one in multi-agent systems and relates to models of learning and adaptation, such as those observed among social insects. The present paper tackles the process of generating, adapting, and changing multi-agent organization dynamically at system runtime, using a swarm inspired approach. This approach is used here mainly for task allocation with low need of pre-planning and specification, and no need of explicit coordination. The results of our approach and another quantitative one are compared here and it is shown that in dynamic domains, the agents adapt to changes in the organization, just as social insects do.