Simulating the impact of future Internet on multimodal mobility

Zargayouna, Mahdi; Zeddini, Besma; Scemama, G; Othman, Amine

doi:10.1109/aiccsa.2014.7073203

Cited by 10 publications

(12 citation statements)

References 7 publications

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“…In this respect, an interesting approach is the so called cognitive approach of FI, as introduced in [22]- [25], and personalized driving parameters are introduced in [26] with respect to telecommunication networks. This concept is exploited in [27] in combination with web services [28], to improve the operation of power systems taking advantage of telecommunications while integrating electric vehicles. Preliminary research contributions to ITS from FI perspective are given in [29] and [30], where a FI based multimodal travel platform is simulated.…”

Section: B Future Internet and Itsmentioning

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: B Future Internet and Itsmentioning

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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“…We run our experiments on the city of Toulouse, France (cf. Figure 3), for which we have detailed data, including urban travel patterns [20]. We have considered the public transport network of Toulouse, with 80 lines, 359 itineraries and 3,887 arcs.…”

Section: A Setupmentioning

confidence: 99%

Impact of Travelers Information Level on Disturbed Transit Networks: A Multiagent Simulation

Zargayouna

Othman²,

Scemama³

et al. 2015

2015 IEEE 18th International Conference on Intelligent Transportation Systems

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With the generalization of real-time traveler information, the behavior of modern transport networks becomes harder to analyze and to predict. It is now critical to develop simulation tools for mobility policies makers, taking into account this new information environment. Information is now individualized, and the interaction of a population of individually guided travelers have to be taken into account in the simulations. Multiagent simulation is proving to be a powerful model for analyzing transport activities. Indeed, autonomous agents provide an appropriate basis for modeling heterogeneous systems and environment-based systems. In this paper, we present a multiagent simulation model to measure the impact of information provision on the quality of passengers travels. This impact is measured by simulating different scenarios in function of the percentage of connected travelers, represented as agents. These simulated scenarios are analyzed following their impact on the average travel times of the travelers. Results show that the number of connected travelers has a positive impact on overall travel times up until a certain threshold before becoming negative.

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“…Transims [3] simulates multimodal movements and evaluates impacts of policy changes in traffic or demographic characteristics while Miro [4] reproduces the urban dynamics of a French city and proposes a prototype of multiagent simulation that is able to test planning scenarios and to specify individuals' behaviors. AgentPolis [5] and SM4T [6] are also multiagent platforms for multimodal transportation. Finally, SUMO [7] is a widely used microscopic simulator mainly focused on traffic.…”

Section: Related Workmentioning

confidence: 99%

Towards a Distributed Multiagent Travel Simulation

Mastio

Zargayouna

Rana

2015

Agent and Multi-Agent Systems: Technologies and Applications

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With the generalization of real-time traveler information, the behavior of modern transport networks becomes harder to analyze and to predict. It is now critical to develop simulation tools for mobility policies makers, taking into account this new information environment. Information is now individualized, and the interaction of a huge population of individually guided travelers have to be taken into account in the simulations. However, existing mobility multiagent and micro-simulations can only consider a sample of the real volumes of travelers, especially for big regions. With distributed simulations, it would be easier to analyze and predict the status of nowadays and future networks, with informed and connected travelers. In this paper, we propose a comparison between two methods for distributing multiagent travelers mobility simulations, allowing for the consideration of realistic travelers flows and wide geographical regions

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Simulating the impact of future Internet on multimodal mobility

Cited by 10 publications

References 7 publications

A Future Internet oriented user centric extended intelligent transportation system

A Future Internet oriented user centric extended intelligent transportation system

Impact of Travelers Information Level on Disturbed Transit Networks: A Multiagent Simulation

Towards a Distributed Multiagent Travel Simulation

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