A Context-Aware Development Framework for Building Self-Adaptive Mobile Software for Public Transport Systems Rahnama, Hossein ; Aubry, Xavier ; Sadeghian, Alireza ; FerwornI, Alexander
Publication details: Bryssel ITS in daily life: 16th world congress and exhibition on intelligent transport systems and services, Stockholm 21-25 September 2009. Paper, 2009Description: 8 sSubject(s): Bibl.nr: VTI P1835:16 [World]Location: Abstract: This paper describes design, modeling and implementation of a self-adaptive and context-aware unified solution for the Paris Metro Authority (RATP). By using the system, passengers can be directed, assisted and notified during the course of their journey based on their profiles. Automatic adaptation and delivery of context-based mobile services such as voice and visual information for passengers with hearing and sight problems along with real-time monitoring and provisioning of assistance to passengers with special needs are some of the features of our proposed system. Part of this framework, passengers' needs are processed in the form of context information and the travel assistant application adapts itself optimally according to synthesized context information.| Current library | Status | |
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| Statens väg- och transportforskningsinstitut | Available |
This paper describes design, modeling and implementation of a self-adaptive and context-aware unified solution for the Paris Metro Authority (RATP). By using the system, passengers can be directed, assisted and notified during the course of their journey based on their profiles. Automatic adaptation and delivery of context-based mobile services such as voice and visual information for passengers with hearing and sight problems along with real-time monitoring and provisioning of assistance to passengers with special needs are some of the features of our proposed system. Part of this framework, passengers' needs are processed in the form of context information and the travel assistant application adapts itself optimally according to synthesized context information.