A multi-criteria decision making approach for personalization itineraries in intelligent transport systems

Moussa, Soumaya; Soui, Makram; Abed, Mourad

doi:10.1109/icadlt.2013.6568441

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…A personalized information retrieval system for filtering travel results, such as to satisfy user specific needs, is conceived in [32]. In [33], a multi-criteria decision making approach is proposed and validated in order to help, in a personalized way, the end user in the journey selection among several different travel solutions. In [34], a framework integrating Case Based Reasoning and Multi Criteria Decision Making is proposed in order to improve the user satisfactions for itinerary search in urban area.…”

Section: Personalization In Mobility Servicesmentioning

confidence: 99%

A Future Internet oriented user centric extended intelligent transportation system

Canale

Giorgio

Lisi

et al. 2016

2016 24th Mediterranean Conference on Control and Automation (MED)

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Intelligent Transportation Systems (ITS) are changing the way people plan a journey and travel around the world. Advanced mobility information systems, as well as intelligent multimodal mobility services, may take considerable advantage of consolidated technologies from emerging ICT frameworks. In this paper we propose an Extended Intelligent Transportation System (ExITS) consisting of a basic ITS equipped with a User Centric Control System (UCCS). The proposed ExITS relies on service personalization methodologies and is conceived as a Future Internet (FI) oriented, closed-loop, user centric architecture integrating and controlling ITS services. The proposed UCCS considers the trip planning service and takes into account both explicit and implicit user preferences in selecting travel solutions satisfying a given user request. The aim of the UCCS is to drive the trip planning service in proposing to the user travel typologies tailored to personal preferences. Implicit preferences are automatically inducted by similarity based unsupervised machine learning techniques and verified by a closed-loop control mechanism triggered by explicit user feedback

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Section: Personalization In Mobility Servicesmentioning

confidence: 99%