Many real-world decision problems are inherently multi-objective in nature and concern multiple actors, making multi-objective multi-agent systems a key domain to study. We argue that trade-offs between conflicting objective functions should be analysed on the basis of the utility that these trade-offs have for the users of a system. We develop a new taxonomy which classifies multi-objective multi-agent decision making settings, on the basis of the reward structures and utility functions. We analyse which solution concepts apply to the different settings in our taxonomy, which allows us to offer a structured view of the field and identify promising directions for future research.

Multi-Objective Multi-Agent Decision Making: A Utility-based Analysis and Survey

The number of vehicles on the roads has increased proportionally over the last couple of years and this number is likely to rise due to the increase in population growth and the number of vehicles that are manufacturing every day. This high traffic density leads to several problems, from which effectively disseminating the emergency messages is a major concern. Keeping in view the dynamic characteristics of VANETs, significant challenges are faced in disseminating the message across the network. The major challenges are the broadcast storm problem, hidden node problem and the packet collision. Many studies have been performed to devise an effective and reliable mechanism for disseminating emergency messages in a Vehicular ad-Hoc Network (VANET). Researchers have proposed different models to tackle various types of scenarios for emergency message dissemination. This paper not only reviews some recent contributions to emergency message dissemination in vehicular networks but also discusses various proposed methods based on Intelligent Transportation System (ITS), Internet of Things (IoT), Priority messaging, Clustering approach, Software Defined Network (SDN) and Fog Computing. We have also tried to explore the latest developments in emergency message dissemination using 5G networks.

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Emergency Message Dissemination in Vehicular Networks: A Review

Recently, development in intelligent transportation systems (ITS) requires the input of various kinds of data in real-time and from multiple sources, which imposes additional research and application challenges. Ongoing studies on Data Fusion (DF) have produced significant improvement in ITS and manifested an enormous impact on its growth. This paper reviews the implementation of DF methods in ITS to facilitate traffic flow analysis (TFA) and solutions that entail the prediction of various traffic variables such as driving behavior, travel time, speed, density, incident, and traffic flow. It attempts to identify and discuss real-time and multi-sensor data sources that are used for various traffic domains, including road/highway management, traffic states estimation, and traffic controller optimization. Moreover, it attempts to associate abstractions of data level fusion, feature level fusion, and decision level fusion on DF methods to better understand the role of DF in TFA and ITS. Consequently, the main objective of this paper is to review DF methods used for real-time and multi-sensor (heterogeneous) TFA studies. The review outcomes are (i) a guideline of constructing DF methods which involve preprocessing, filtering, decision, and evaluation as core steps, (ii) a description of the recent DF algorithms or methods that adopt real-time and multi-sensor sources data and the impact of these data sources on the improvement of TFA, (iii) an examination of the testing and evaluation methodologies and the popular datasets and (iv) an identification of several research gaps, some current challenges, and new research trends.

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Review of Data Fusion Methods for Real-Time and Multi-Sensor Traffic Flow Analysis

This paper capitalizes on the increasingly high relevance gained by data-intensive technologies in the development of intelligent transportation system, which calls for the progressive adoption of adaptive, self-learning methods for solving modeling, simulation, and optimization problems. In this regard, certain mechanisms and processes observed in nature, including the animal brain, have proved themselves to excel not only in terms of efficiently capturing time-evolving stimuli, but also at undertaking complex tasks by virtue of mechanisms that can be extrapolated to computer algorithms and methods. This paper comprehensively reviews the state-of-the-art around the application of bioinspired methods to the challenges arising in the broad field of intelligent transportation system (ITS). This systematic survey is complemented by an initiatory taxonomic introduction to bioinspired computational intelligence, along with the basics of its constituent techniques. A focus is placed on which research niches are still unexplored by the community in different ITS subareas. The open issues and research directions for the practical implementation of ITS endowed with bioinspired computational intelligence are also discussed in detail.

Bioinspired Computational Intelligence and Transportation Systems: A Long Road Ahead

The majority of multi-agent system (MAS) implementations aim to optimise agents' policies with respect to a single objective, despite the fact that many real-world problem domains are inherently multi-objective in nature. Multi-objective multi-agent systems (MOMAS) explicitly consider the possible trade-offs between conflicting objective functions. We argue that, in MOMAS, such compromises should be analysed on the basis of the utility that these compromises have for the users of a system. As is standard in multi-objective optimisation, we model the user utility using utility functions that map value or return vectors to scalar values. This approach naturally leads to two different optimisation criteria: expected scalarised returns (ESR) and scalarised expected returns (SER). We develop a new taxonomy which classifies multi-objective multi-agent decision making settings, on the basis of the reward structures, and which and how utility functions are applied. This allows us to offer a structured view of the field, to clearly delineate the current state-of-the-art in multi-objective multi-agent decision making approaches and to identify promising directions for future research. Starting from the execution phase, in which the selected policies are applied and the utility for the users is attained, we analyse which solution concepts apply to the different settings in our taxonomy. Furthermore, we define and discuss these solution concepts under both ESR and SER optimisation criteria. We conclude with a summary of our main findings and a discussion of many promising future research directions in multi-objective multi-agent systems.

Intelligent traffic management can be considered one of the most promising solutions to contemporary traffic problems The traffic in transportation associated with emergency conditions including psychiatric improvement in transport network, allocation of variable traffic flows, reducing the number of the crowded traffic roads and paths as well as its negative effects (such as delays, waiting time, stress of driver, noise and air pollution, and blocking the assistive devices) This research has been used by new multi-agent systems to manage traffic On the one hand, the proposed algorithm includes traffic flow improvement in emergency conditions until it is considered as real-time traffic information and in other hand, by preventing the increase the volume of a paths have effects in the reduce of crowded traffic situations at a specific time (for example, providing the proposed paths in the shortest time) In this article, the integrated environment is including JACK software for having a virtual agent behavior simulation In general, we can use the different simulation form in a distribution network to display the crowded and traffic In this article, the JACK software is used for having the explicit capabilities and supporting the common of this software in modelling the multi-agent systems, such as agents, design, event and capabilities In addition, designing and analyzing of this interaction is simplest than the existed designs in JACK software As a result of the proposed model, it seems reasonable that the proposed approach is different than previous works in each case On the one hand, there are modeling system in the different tasks as intelligent agents dependent to present the simple and effective road network In this case, it may correct and changes the actions of driver in emergency conditions by the concept of agent cooperation for achieving the common target

Ali Kamankesh

Papers

An Approach to Intelligent Traffic Management System Using a Multi-agent System