During peak hours in urban areas, unpredictable traffic congestion caused by en route events (e.g., vehicle crashes) increases drivers' travel time and, more seriously, decreases their travel time reliability. In this paper, an original and highly practical vehicle rerouting system, which is called Next Road Rerouting (NRR), is proposed to aid drivers in making the most appropriate next road choice to avoid unexpected congestions. In particular, this heuristic rerouting decision is made upon a cost function that takes into account the driver's destination and local traffic conditions. In addition, the newly designed multiagent system architecture of NRR allows the positive rerouting impacts on local traffic to be disseminated to a larger area through the natural traffic flow propagation within connected local areas. The simulation results based on both synthetic and realistic urban scenarios demonstrate that, compared with the existing solutions, NRR can achieve a lower average travel time while guaranteeing a higher travel time reliability in the face of unexpected congestion. The impacts of NRR on the travel time of both rerouted and nonrerouted vehicles are also assessed, and the corresponding results reveal its higher practicability.

New communication technologies can enable Web users to access personalised information "anytime, anywhereö. However, the network environments allowing this "anytime, anywhereö access may have widely varying performance characteristics such as bandwidth, level of congestion, mobility support, and cost of transmission. It is unrealistic to expect that the quality of delivery of the same content can be maintained in this variable environment, but rather an effort must be made to fit the content served to the current delivery conditions, thus ensuring high Quality of Experience (QoE) to the users. This paper introduces an end-user QoE-aware adaptive hypermedia framework that extends the adaptation functionality of adaptive hypermedia systems with a fine-grained content-based adaptation mechanism. The proposed mechanism attempts to take into account multiple factors affecting QoE in relation to the delivery of Web content. Various simulation tests investigate the performance improvements provided by this mechanism, in a home-like, low bit rate operational environment, in terms of access time per page, aggregate access time per browsing session and quantity of transmitted information.

This paper presents the framework for a QoS-aware adaptive Web-based system (QoSAS) that adapts Web content based on both user-perceived-QoS and user interests related to the Web content. The architecture of the QoSAS includes a novel perceived performance model (PPM) that models user-perceived performance features via different QoS metrics and suggests content constraints in order to improve the perceived performance. The PPM stereotype-based mathematical model is presented in detail as well as preliminary simulation test results. These results demonstrate that the PPM helps the QoSAS to improve the user satisfaction.