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Fast urbanization generates increasing amounts of travel flows, urging the need for efficient transport planning policies. In parallel, mobile phone data have emerged as the largest mobility data source, but are not yet integrated to transport planning models. Currently, transport authorities are lacking a global picture of daily passenger flows on multimodal transport networks. In this work, we propose the first methodology to infer dynamic Origin-Destination flows by transport modes using mobile network data e.g., Call Detail Records. For this study, we pre-process 360 million trajectories for more than 2 million devices from the Greater Paris as our case study region. The model combines mobile network geolocation with transport network geospatial data, travel survey, census and travel card data. The transport modes of mobile network trajectories are identified through a two-steps semi-supervised learning algorithm. The later involves clustering of mobile network areas and Bayesian inference to generate transport probabilities for trajectories. After attributing the mode with highest probability to each trajectory, we construct Origin-Destination matrices by transport mode. Flows are up-scaled to the total population using state-of-the-art expansion factors. The model generates time variant road and rail passenger flows for the complete region. From our results, we observe different mobility patterns for road and rail modes and between Paris and its suburbs. The resulting transport flows are extensively validated against the travel survey and the travel card data for different spatial scales.

Many studies have tried to evaluate wireless networks and especially the IEEE 802.15.4 standard. Hence, several papers have aimed to describe the functionalities of the physical (PHY) and medium access control (MAC) layers. They have highlighted some characteristics with experimental results and/or have attempted to reproduce them using theoretical models. In this paper, we use the first way to better understand IEEE 802.15.4 standard. Indeed, we provide a comprehensive model, able more faithfully to mimic the functionalities of this standard at the PHY and MAC layers. We propose a combination of two relevant models for the two layers. The PHY layer behavior is reproduced by a mathematical framework, which is based on radio and channel models, in order to quantify link reliability. On the other hand, the MAC layer is mimed by an enhanced Markov chain. The results show the pertinence of our approach compared to the model based on a Markov chain for IEEE 802.15.4 MAC layer. This contribution allows us fully and more precisely to estimate the network performance with different network sizes, as well as different metrics such as node reliability and delay. Our contribution enables us to catch possible failures at both layers.

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Understanding distribution system water quality is a complex task because it involves not only numerous parameters but also the interactions among these parameters. This article highlights the development of a visualization tool capable of showing all types of information, (modeled and measured parameters), simultaneously or independently as a function of space or time. In this study, diversified databases that included such system parameters as structure, hyudraulics, and water quality were compiled and explored spatially and temporally through the visualization software. This approach proved to be successful in identifying probable sources of water contamination at a specific sampling point. It also helped establish general relationships between distribution system parameters, i.e., pipe breaks, pipe age, and water pressure, that are often difficult to link. A clearer understanding of the reasons for water quality degradation during distribution is important to water suppliers because research has suggested that such degradation increases the rate of gastrointestinal illnesses. When water quality is questionable, often the only parameters taken into account are measurements of water characteristics. Visualization of multiple parameters at one time enables utilities to pinpoint the source of their water quality problems and distinguish which areas of the distribution system are most at risk.