Service personalization is an important goal for any smart environment. Comfort systems may be adjusted in an automatic way when a given user is present, and multimedia devices may offer a music or movie catalog with favorite contents or may even pick one of them for the user. To achieve this goal, we propose a Service Oriented Architecture implementation based on multiagent systems. We specially take advantage of the mobility features of software agents. In particular, we have developed a hierarchical, agent-based solution intended to be applicable to different smart space scenarios, ranging from small environments, like smart homes or smart offices, to large smart spaces like cities. In this paper we describe the global architecture and focus on our approach to service personalization using mobile agents that follow the users as they move through different smart spaces.

Traffic jams in large cities, in addition to having a very high economic cost, cause an increase in emissions generated by vehicles over the same route being driven under normal conditions. In recent years, there has been a rapid evolution in the technologies applied to the field of autonomous vehicles. There are currently commercial solutions for assisted driving and semi-autonomous driving systems, with very favorable forecasts for reaching a completely autonomous vehicle scenario in the coming decades. This new environment generates opportunities and challenges to reduce congestion in scenarios with autonomous or semi-autonomous vehicles. This paper focuses on the automatic optimization of the passage of vehicles through intersections. The intersections are one of the most conflict-generating elements in a traffic network. This type of conflicts arises because the intersections must manage multiple traffic flows with different priorities and preferences, often leading to traffic jams. The problem has been addressed by proposing three mechanisms to model any type of intersection, to calculate the roads with fewer points of conflict between their inputs and outputs, and to optimize the arrival rate of vehicles using a Genetic Algorithm to achieve the maximum performance of the intersection. To validate this solution, a cellular automata simulator has been developed, which can be adapted to both autonomous and conventional vehicle scenarios and can provide realistic results when certain conditions are met. The results obtained have been compared with other traditional solutions (priority and traffic lights) using microscopic traffic simulations, and with those obtained in other studies showing the advantages of the proposed system. The proposed systems achieve a throughput improvement between 9.21% and 36.98% compared with the traditional solutions.

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Urban traffic routing has to deal with individual mobility and collective wellness considering citizens, multi-modal transport, and fleet traffic with conflicting interests such as electric vehicles, local distribution, public transport, and private vehicles. Different interests, goals, and regulations, suggest the development of new multi-objective routing mechanisms which may improve traffic flow. In this work, Traffic Weighted Multi-Maps (TWM) is presented as a novel traffic routing mechanism based on the strategical generation and distribution of complementary cost maps for traffic fleets, oriented towards the application of differentiated traffic planning and control policies. TWM is built upon a centralized control architecture, where a Traffic Management Center generates and distributes customized cost maps of the road network. These maps are used individually to calculate routes. In this research, we present the TWM theoretical model and experimental results based on microscopic simulations over a real city traffic network under multiple scenarios, including traffic incidents management. Experimental evaluation takes into account driver's adherence to the system and considers a multi-objective analysis both for the global network parameters (congestion, travel time, and route length) and for the subjective driving experience. Experimental results deliver performance improvements from 20% to 50%. TWM is fully compatible with existing traffic routing systems and has promising future evolution applying new algorithms, policies and network profiles.

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