Engineering Agent Systems for Decision Support

This paper discusses how agent technology can be applied to the design of advanced Information Systems for Decision Support. In particular, it describes the different steps and models that are necessary to engineer Decision Support Systems based on a multiagent architecture. The approach is illustrated by a case study in the traffic management domain

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

A multi-agent Framework for dynamic traffic management Considering Priority Link

To favor emergency vehicles, promote collective modes of transport in Moroccan cities, we propose in this paper a control system to manage traffic at signalized intersections with priority links in urban settings. This system combines multi-agent technology and fuzzy logic to regulate traffic flows. The traffic system flow is divided into two types of vehicles; priority and regular vehicles. The regular vehicles can use only the regular links, while the priority vehicles may use both priority and the regular links. This approach aims to favor emergency vehicles and promote collective modes of transport, it acts on the traffic light phases length and order to control all traffic flows. We proposed a decentralized system of regulation based on real-time monitoring to develop a local inter-section state, and intelligent coordination between neighboring intersections to build an overview of the traffic state. The regulation and prioritization decisions are made through cooperation, communication, and coordination between different agents. The performance of the proposed system is investigated and instantiated in ANYLOGIC simulator, using a section of the Marrakesh road network that contains priority links. The results indicate that the designed system can significantly develop the efficiency of the traffic regulation system

A Distributed and Privacy-Aware Speed Advisory System for Optimising Conventional and Electric Vehicles Networks

One of the key ideas to make Intelligent Transportation Systems (ITS) work effectively is to deploy advanced communication and cooperative control technologies among the vehicles and road infrastructures. In this spirit, we propose a consensus-based distributed speed advisory system that optimally determines a recommended common speed for a given area in order that the group emissions, or group battery consumptions, are minimised. Our algorithms achieve this in a privacy-aware manner; namely, individual vehicles do not reveal in-vehicle information to other vehicles or to infrastructure. A mobility simulator is used to illustrate the efficacy of the algorithm, and hardware-in-the-loop tests involving a real vehicle are given to illustrate user acceptability and ease of the deployment.Comment: This is a journal paper based on the conference paper "Highway speed limits, optimised consensus, and intelligent speed advisory systems" presented at the 3rd International Conference on Connected Vehicles and Expo (ICCVE 2014) in November 2014. This is the revised version of the paper recently submitted to the IEEE Transactions on Intelligent Transportation Systems for publicatio