References of "Toader, Bogdan 50008763"
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See detailUsing Passive Data Collection Methods to Learn Complex Mobility Patterns: An Exploratory Analysis
Toader, Bogdan UL; Cantelmo, Guido UL; Popescu, Mioara et al

Scientific Conference (in press)

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See detailA New Modelling Framework over Temporal Graphs for Collaborative Mobility Recommendation Systems
Toader, Bogdan UL; Moawad, Assaad UL; Fouquet, François UL et al

in A New Modelling Framework over Temporal Graphs for Collaborative Mobility Recommendation Systems (2018, March 15)

Over the years, collaborative mobility proved to be an important but challenging component of the smart cities paradigm. One of the biggest challenges in the smart mobility domain is the use of data ... [more ▼]

Over the years, collaborative mobility proved to be an important but challenging component of the smart cities paradigm. One of the biggest challenges in the smart mobility domain is the use of data science as an enabler for the implementation of large scale transportation sharing solutions. In particular, the next generation of Intelligent Transportation Systems (ITS) requires the combination of artificial intelligence and discrete simulations when exploring the effects of whatif decisions in complex scenarios with millions of users. In this paper, we address this challenge by presenting an innovative data modelling framework that can be used for ITS related problems. We demonstrate that the use of graphs and time series in multi-dimensional data models can satisfy the requirements of descriptive and predictive analytics in real-world case studies with massive amounts of continuously changing data. The features of the framework are explained in a case study of a complex collaborative mobility system that combines carpooling, carsharing and shared parking. The performance of the framework is tested with a large-scale dataset, performing machine learning tasks and interactive realtime data visualization. The outcome is a fast, efficient and complete architecture that can be easily deployed, tested and used for research as well in an industrial environment. [less ▲]

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See detailUsage of Smartphone Data to Derive an Indicator for Collaborative Mobility between Individuals
Toader, Bogdan UL; Sprumont, François UL; Faye, Sébastien UL et al

in ISPRS International Journal of Geo-Information (2017), 6(3), 62

The potential of geospatial big data has been drawing attention for a few years. Despite the larger and larger market penetration of portable technologies (nomadic and wearable devices like smartphones ... [more ▼]

The potential of geospatial big data has been drawing attention for a few years. Despite the larger and larger market penetration of portable technologies (nomadic and wearable devices like smartphones and smartwatches), their opportunities for travel behavior analysis are still relatively unexplored. The main objective of our study is to extract the human mobility patterns from GPS traces in order to derive an indicator for enhancing Collaborative Mobility (CM) between individuals. The first step, extracting activity duration and location, is done using state-of-the-art automated recognition tools. Sensors data are used to reconstruct individual’s activity location and duration across time. For constructing the indicator, in a second step, we defined different variables and methods for specific case studies. Smartphone sensor data are being collected from a limited number of individuals and for one week. These data are used to evaluate the proposed indicator. Based on the value of the indicator, we analyzed the potential for identifying CM among groups of users, such as sharing traveling resources (e.g., carpooling, ridesharing, parking sharing) and time (rescheduling and reordering activities). [less ▲]

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