References of "Derrmann, Thierry 50001680"
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See detailDemo: MAMBA: A Platform for Personalised Multimodal Trip Planning
Faye, Sébastien UL; Cantelmo, Guido UL; Tahirou, Ibrahim UL et al

Software (2017)

In recent years, multimodal transportation has become a challenging approach to route planning. Most existing planning systems usually rely on data sourced from different organisations, enabling the user ... [more ▼]

In recent years, multimodal transportation has become a challenging approach to route planning. Most existing planning systems usually rely on data sourced from different organisations, enabling the user to select a limited number of routing strategies. As part of the MAMBA project, developed in Luxembourg until 2017, we have been interested in the potential benefits of multimodal mobility systems. A key factor has been integrated into our studies: the need for a personalised experience at user level, whether when selecting the means of transport or describing user habits (e.g. route style, environment). In this context, we have developed a platform for planning personalised multimodal trips, broken down into the three main modules presented in this demonstration. More importantly, this platform has been developed to facilitate the daily mobility of people in Luxembourg, and considers datasets and characteristics that are specific to this region, which has an exceptionally high volume of daily commuting between Luxembourg and neighbouring countries. [less ▲]

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See detailEstimating Urban Road Traffic States Using Mobile Network Signaling Data
Derrmann, Thierry UL; Frank, Raphaël UL; Viti, Francesco UL et al

in Abstract book of the 20th International Conference on Intelligent Transportation Systems (2017, October)

It is intuitive that there is a causal relationship between human mobility and signaling events in mobile phone networks. Among these events, not only the initiation of calls and data sessions can be used ... [more ▼]

It is intuitive that there is a causal relationship between human mobility and signaling events in mobile phone networks. Among these events, not only the initiation of calls and data sessions can be used in analyses, but also handovers between different locations that reflect mobility. In this work, we investigate if handovers can be used as a proxy metric for flows in the underlying road network, especially in urban environments. More precisely, we show that characteristic profiles of handovers within and between clusters of mobile network cells exist. We base these profiles on models from road traffic flow theory, and show that they can be used for traffic state estimation using floating-car data as ground truth. The presented model can be beneficial in areas with good mobile network coverage but low road traffic counting infrastructure, e.g. in developing countries, but also serve as an additional predictor for existing traffic state monitoring systems. [less ▲]

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See detailEffectiveness of the Two-Step Dynamic Demand Estimation model on large networks
Cantelmo, Guido UL; Viti, Francesco UL; Derrmann, Thierry UL

in Proceedings of 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS) (2017, June 28)

In this paper, the authors present a Two-Step approach that sequentially adjusts generation and distribution values of the (dynamic) OD matrix. While the proposed methodology already provided excellent ... [more ▼]

In this paper, the authors present a Two-Step approach that sequentially adjusts generation and distribution values of the (dynamic) OD matrix. While the proposed methodology already provided excellent results for updating demand flows on a motorway, the aim of this paper is to validate this conclusion on a real network: Luxembourg City. This network represents the typical middle-sized European city in terms of network dimension. Moreover, Luxembourg City has the typical structure of a metropolitan area, composed of a city centre, ring, and suburb areas. An innovative element of this paper is to use mobile network data to create a time-dependent profile of the generated demand inside and outside the ring. To support the claim that the model is ready for practical implementation, it is interfaced with PTV Visum, one of the most widely adopted software tools for traffic analysis. Results of these experiments provide a solid empirical ground in order to further develop this model and to understand if its assumptions hold for urban scenarios. [less ▲]

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See detailHow Mobile Phone Handovers reflect Urban Mobility: A Simulation Study
Derrmann, Thierry UL; Frank, Raphaël UL; Engel, Thomas UL et al

in Proceedings of the 5th IEEE Conference on Models and Technologies for Intelligent Transportation Systems. (2017, June 26)

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See detailSmartphone-based Adaptive Driving Maneuver Detection: A large-scale Evaluation Study
Castignani, German UL; Derrmann, Thierry UL; Frank, Raphaël UL et al

in IEEE Transactions on Intelligent Transportation Systems (2017)

The proliferation of connected mobile devices together with advances in their sensing capacity has enabled a new distributed telematics platform. In particular, smartphones can be used as driving sensors ... [more ▼]

The proliferation of connected mobile devices together with advances in their sensing capacity has enabled a new distributed telematics platform. In particular, smartphones can be used as driving sensors to identify individual driver behavior and risky maneuvers. However, in order to estimate driver behavior with smartphones, the system must deal with different vehicle characteristics. This is the main limitation of existing sensing platforms, which are principally based on fixed thresholds for different sensing parameters. In this paper, we propose an adaptive driving maneuver detection mechanism that iteratively builds a statistical model of the driver, vehicle, and smartphone combination using a multivariate normal model. By means of experimentation over a test track and public roads, we first explore the capacity of different sensor input combinations to detect risky driving maneuvers, and we propose a training mechanism that adapts the profiling model to the vehicle, driver, and road topology. A large-scale evaluation study is conducted, showing that the model for maneuver detection and scoring is able to adapt to different drivers, vehicles, and road conditions. [less ▲]

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See detailPoster: LuST-LTE: A Simulation Package for Pervasive Vehicular Connectivity
Derrmann, Thierry UL; Faye, Sébastien UL; Frank, Raphaël UL et al

Poster (2016, December 08)

Recent technological advances in communication technology have provided new ways to understand human mobility. Connected vehicles with their rising market penetration are particularly representative of ... [more ▼]

Recent technological advances in communication technology have provided new ways to understand human mobility. Connected vehicles with their rising market penetration are particularly representative of this trend. They become increasingly interesting, not only as sensors, but also as participants in Intelligent Transportation System (ITS) applications. More specifically, their pervasive connectivity to cellular networks enables them as passive and active sensing units. In this paper, we introduce LuST-LTE, a package of open-source simulation tools that allows the simulation of vehicular traffic along with pervasive LTE connectivity. [less ▲]

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See detailTowards Characterizing Bluetooth Discovery in a Vehicular Context
Bronzi, Walter UL; Derrmann, Thierry UL; Castignani, German UL et al

in Vehicular Networking Conference (VNC), 2016 IEEE (2016, December)

Bluetooth has, in recent years, gained more and more momentum. New commodity objects and wearables im- plementing Bluetooth Smart technology (Low Energy) are re- leased everyday. In particular, the ever ... [more ▼]

Bluetooth has, in recent years, gained more and more momentum. New commodity objects and wearables im- plementing Bluetooth Smart technology (Low Energy) are re- leased everyday. In particular, the ever increasing number of discoverable devices both inside and outside a populated area gives us an encouraging insight on future research directions for this technology. In this paper, based on a sensing system developed as an Android application, we evaluate Bluetooth Classic and Low Energy discovery characteristics from a vehic- ular perspective. By recording information about devices nearby (e.g. the number of discovered devices, their signal strength, manufacturer information) and the GPS location we can derive interesting information about a driver’s situation, as well as his/her environment. Presented results indicate that the amount of discovered devices and signal strengths are dependent on velocity and road category. Finally, future work and discussions address potential use-case applications based only on Bluetooth discovery, such as low energy and privacy friendly road and traffic context awareness. The sensing system used in this article is free online under the MIT License. [less ▲]

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See detailTowards Privacy-Neutral Travel Time Estimation from Mobile Phone Signalling Data
Derrmann, Thierry UL; Frank, Raphaël UL; Faye, Sébastien UL et al

in Proceedings of the 2016 IEEE International Smart Cities Conference (ISC2) (2016, September)

Today’s mobile penetration rates enable cellular signaling data to be useful in diverse fields such as transportation planning, the social sciences and epidemiology. Of particular interest for these ... [more ▼]

Today’s mobile penetration rates enable cellular signaling data to be useful in diverse fields such as transportation planning, the social sciences and epidemiology. Of particular interest for these applications are mobile subscriber dwell times. They express how long users stay in the service range of a base station. In this paper, we want to evaluate whether dwell time distributions can serve as predictors for road travel times. To this end, we transform floating car data into synthetic dwell times that we use as weights in a graph-based model. The model predictions are evaluated using the floating car ground truth data. Additionally, we show a potential link between handover density and travel times. We conclude that dwell times are a promising predictor for travel times, and can serve as a valuable input for intelligent transportation systems. [less ▲]

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See detailLuST-LTE: A Simulation Package for Pervasive Vehicular Connectivity
Derrmann, Thierry UL; Faye, Sébastien UL; Frank, Raphaël UL et al

Presentation (2016, June 30)

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See detailValidation study of risky event classification using driving pattern factors
Castignani, German UL; Derrmann, Thierry UL; Frank, Raphaël UL et al

Scientific Conference (2015, November 24)

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See detailA Cell Dwell Time Model for Synthetic Population Generation from Call Detail Records
Derrmann, Thierry UL; Frank, Raphaël UL; Melakessou, Foued UL et al

Scientific Conference (2015, April 08)

In this work we propose a novel Cell Dwell Time Model that can be used to generate a synthetic population. We introduce two new metrics to define the attractivity of cell sites based on global and ... [more ▼]

In this work we propose a novel Cell Dwell Time Model that can be used to generate a synthetic population. We introduce two new metrics to define the attractivity of cell sites based on global and individual parameters obtained via the analysis of the Data For Development (D4D) Call Detail Records (CDR). We rely on the shortest path road network to interconnect two distant cell sites. The resulting dwell time model can be used to compute accurate user trajectories even with partial information. This work represents a first step towards the generation of a synthetic population that can be used to perform a wide range of simulative studies to evaluate and optimize transportation networks. [less ▲]

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See detailDetection of Population Mobility Anomalies in Senegal from Base Station Profiles
Melakessou, Foued UL; Derrmann, Thierry UL; Frank, Raphaël UL et al

Scientific Conference (2015, April 08)

The analysis of Call Detail Records has captured the attention of traffic and transportation researchers to optimize people's mobility. In our work, we would like to analyze Call Detail Records in order ... [more ▼]

The analysis of Call Detail Records has captured the attention of traffic and transportation researchers to optimize people's mobility. In our work, we would like to analyze Call Detail Records in order to extract realistic human mobility models adapted to the Senegal use case. In this paper, we describe our analysis of the available D4D datasets. The first contribution is the modeling of the daily traffic demand profile of each antenna, by considering voice and short message services. The evaluation of mobility models will help to better design and develop future infrastructures in order to better support the actual demand. A classification has been performed into urban, suburban and rural modes. An algorithm has been developed to detect traffic anomalies in 2013, based on the daily profiles. The second contribution corresponds to the generation of inter-antennas and inter-arrondissements mobility graphs for each month of 2013. [less ▲]

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See detailDriver Behavior Profiling Using Smartphones: A Low-Cost Platform for Driver Monitoring
Castignani, German UL; Derrmann, Thierry UL; Frank, Raphaël UL et al

in Intelligent Transportation Systems Magazine, IEEE (2015), 7(1), 91-102

Today's smartphones and mobile devices typically embed advanced motion sensors. Due to their increasing market penetration, there is a potential for the development of distributed sensing platforms. In ... [more ▼]

Today's smartphones and mobile devices typically embed advanced motion sensors. Due to their increasing market penetration, there is a potential for the development of distributed sensing platforms. In particular, over the last few years there has been an increasing interest in monitoring vehicles and driving data, aiming to identify risky driving maneuvers and to improve driver efficiency. Such a driver profiling system can be useful in fleet management, insurance premium adjustment, fuel consumption optimization or CO2 emission reduction. In this paper, we analyze how smartphone sensors can be used to identify driving maneuvers and propose SenseFleet, a driver profile platform that is able to detect risky driving events independently from the mobile device and vehicle. A fuzzy system is used to compute a score for the different drivers using real-time context information like route topology or weather conditions. To validate our platform, we present an evaluation study considering multiple drivers along a predefined path. The results show that our platform is able to accurately detect risky driving events and provide a representative score for each individual driver. [less ▲]

Detailed reference viewed: 486 (46 UL)