Abstract

International audience; Multi-modal traffic management in Intelligent Transportation Systems (ITS) aims to provide a more efficient traffic regulation to passengers and reduce congestion and obstruction in the roads. In spite of the outstanding progress made in this research filed; traffic management still a very challenging problem regarding the multiple factors that have to be taken into account in any proposed solution. To tackle this problem, this paper introduces a collaborative model based context awareness multi-modal traffic management aiming at providing an efficient way to manage the traffic inside a transportation station. In this model (Multi-Layers Stations: the stations that have different intersections for different means of transport), the traffic management is based on a Q-learning technique that takes into account the context awareness parameters to provide more potent decisions. The learning technique offers the opportunity to the system (transportation station) to adapt dynamically its decision (choice of the best transportation mean) based on feedbacks provided by the passengers traveling from that specified station and thus optimize their journey through the transportation network. The efficacy of our proposed technique is validated through extensive simulations for different layers of transport means like metros, trains, and buses. Our proposal holds for any ITS system decisions provided the availability of real-time traces about the passengers passing by any station

Abstract

Shared spaces are gaining presence in cities, where a variety of players and mobility types (pedestrians, bicycles, motorcycles, and cars) move without specifically delimited areas. This makes the traffic they comprise challenging for automated systems. The information traditionally considered (e.g., streets, and obstacle positions and speeds) is not enough to build suitable models of the environment. The required explanatory and anticipation capabilities need additional information to improve them. Social aspects (e.g., goal of the displacement, companion, or available time) should be considered, as they have a strong influence on how people move and interact with the environment. This paper presents the Social-Aware Driver Assistance System (SADAS) approach to integrate this information into traffic systems. It relies on a domain-specific modelling language for social contexts and their changes. Specifications compliant with it describe social and system information, their links, and how to process them. Traffic social properties are the formalization within the language of relevant knowledge extracted from literature to interpret information. A multi-agent system architecture manages these specifications and additional processing resources. A SADAS can be connected to other parts of traffic systems by means of subscription-notification mechanisms. The case study to illustrate the approach applies social knowledge to predict people&rsquo s movements. It considers a distributed system for obstacle detection and tracking, and the intelligent management of traffic signals. Document type: Article