Monotone properties of random geometric graphs have sharp thresholds

Random geometric graphs result from taking $n$ uniformly distributed points in the unit cube, $[0,1]^d$, and connecting two points if their Euclidean distance is at most $r$, for some prescribed $r$. We show that monotone properties for this class of graphs have sharp thresholds by reducing the problem to bounding the bottleneck matching on two sets of $n$ points distributed uniformly in $[0,1]^d$. We present upper bounds on the threshold width, and show that our bound is sharp for $d=1$ and at most a sublogarithmic factor away for $d\ge2$. Interestingly, the threshold width is much sharper for random geometric graphs than for Bernoulli random graphs. Further, a random geometric graph is shown to be a subgraph, with high probability, of another independently drawn random geometric graph with a slightly larger radius; this property is shown to have no analogue for Bernoulli random graphs.Comment: Published at http://dx.doi.org/10.1214/105051605000000575 in the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org

A methodology (CUPRITE) for urban network travel time estimation by integrating multisource data

Travel time is an important network performance measure and it quantifies congestion in a manner easily understood by all transport users. In urban networks, travel time estimation is challenging due to number of reasons such as, fluctuations in traffic flow due to traffic signals, significant flow to/from mid-link sinks/sources, etc. In this research a methodology, named CUmulative plots and PRobe Integration for Travel timE estimation (CUPRITE), has been developed, tested and validated for average travel time estimation on signalized urban network. It provides exit movement specific link travel time and can be applied for route travel time estimation. The basis of CUPRITE lies in the classical analytical procedure of utilizing cumulative plots at upstream and downstream locations for estimating travel time between the two locations. The classical procedure is vulnerable to detector counting error and non conservation of flow between the two locations that induces relative deviation amongst the cumulative plots (RD). The originality of CUPRITE resides in integration of multi-source data: detector data and signal timings from different locations on the network, and probe vehicle data. First, cumulative plots are accurately estimated by integrating detector and signal timings. Thereafter, cumulative plots are integrated with probe vehicle data and RD issue is addressed. CUPRITE is tested rigorously using traffic simulation for different scenarios with different possible combinations of sink, source and detector error. The performance of the proposed methodology has been found insensitive to percentage of sink or source or detector error. For a link between two consecutive signalized intersections and during undersaturated traffic condition, the concept of virtual probe is introduced and travel time can be accurately estimated without any real probe. For oversaturated traffic condition, CUPRITE requires only few probes per estimation interval for accurate travel time estimation. CUPRITE is also validated with real data collected from number plate survey at Lucerne, Switzerland. Two tailed t-test (at 0.05 level of significance) results confirm that travel time estimates from CUPRITE are statistically equivalent to real estimates from number plate survey. The testing and validation of CUPRITE have demonstrated that it can be applied for accurate and reliable travel time estimation. The current market penetration of probe vehicle is quite low. In urban networks, availability of a large number of probes per estimation interval is rare. With limited number of probe vehicles in urban networks, CUPRITE can significantly enhance the accuracy of travel time estimation

Analysis for the Use of Cumulative Plots for Travel Time Estimation on Signalized Network

This paper provides fundamental understanding for the use of cumulative plots for travel time estimation on signalized urban networks. Analytical modeling is performed to generate cumulative plots based on the availability of data: a) Case-D, for detector data only; b) Case-DS, for detector data and signal timings; and c) Case-DSS, for detector data, signal timings and saturation flow rate. The empirical study and sensitivity analysis based on simulation experiments have observed the consistency in performance for Case-DS and Case-DSS, whereas, for Case-D the performance is inconsistent. Case-D is sensitive to detection interval and signal timings within the interval. When detection interval is integral multiple of signal cycle then it has low accuracy and low reliability. Whereas, for detection interval around 1.5 times signal cycle both accuracy and reliability are hig

Mining temporal and spatial travel regularity for transit planning

Smart Card data from Automated Fare Collection system has been considered as a promising source of information for transit planning. However, literature has been limited to mining travel patterns from transit users and suggesting the potential of using this information. This paper proposes a method for mining spatial regular origins-destinations and temporal habitual travelling time from transit users. These travel regularity are discussed as being useful for transit planning. After reconstructing the travel itineraries, three levels of Density-Based Spatial Clustering of Application with Noise (DBSCAN) have been utilised to retrieve travel regularity of each of each frequent transit users. Analyses of passenger classifications and personal travel time variability estimation are performed as the examples of using travel regularity in transit planning. The methodology introduced in this paper is of interest for transit authorities in planning and management

A Primal-Dual Algorithm for Link Dependent Origin Destination Matrix Estimation

Origin-Destination Matrix (ODM) estimation is a classical problem in transport engineering aiming to recover flows from every Origin to every Destination from measured traffic counts and a priori model information. In addition to traffic counts, the present contribution takes advantage of probe trajectories, whose capture is made possible by new measurement technologies. It extends the concept of ODM to that of Link dependent ODM (LODM), keeping the information about the flow distribution on links and containing inherently the ODM assignment. Further, an original formulation of LODM estimation, from traffic counts and probe trajectories is presented as an optimisation problem, where the functional to be minimized consists of five convex functions, each modelling a constraint or property of the transport problem: consistency with traffic counts, consistency with sampled probe trajectories, consistency with traffic conservation (Kirchhoff's law), similarity of flows having close origins and destinations, positivity of traffic flows. A primal-dual algorithm is devised to minimize the designed functional, as the corresponding objective functions are not necessarily differentiable. A case study, on a simulated network and traffic, validates the feasibility of the procedure and details its benefits for the estimation of an LODM matching real-network constraints and observations

Making User-Generated Content Available When a Device is Offline

Some applications such as digital maps support offline use, including download of certain types of data, e.g., map data of a region for navigation. However, the downloaded information does not include user-generated content (UGC), reviews, external feeds, problem reports, geo-tagged news, etc. Such content can include timely and topical information which is unavailable to users if their device is offline. This disclosure describes techniques to make curated UGC and third-party feeds of specific types available when a device is offline. UGC is curated by topic and location using a multimodal large language model or other suitable technique. With user permission, a map annotated with recent, relevant UGC is downloaded or pushed to a mobile app on the user device prior to the loss of wireless connectivity. Summarized UGC content is provided to enable offline operation. Key pieces of information that can enhance safety and improve user experience are thus made available even in the absence of a network. The described techniques can also be of value to users on low-bandwidth networks or in remote areas

Study of Wind Turbine Driven Doubly Fed Induction Generator (DFIG) Using AC/DC/AC Converter

In recent years, wind energy has become one of the most important and promising sources of renewable energy, which demands additional transmission capacity and better means of maintaining system reliability. The evolution of technology related to wind systems industry leaded to the development of a generation of variable speed wind turbines that present many advantages compared to the fixed speed wind turbines. These wind energy conversion systems are connected to the grid through Voltage Source Converters (VSC) to make variable speed operation possible. The studied system here is a variable speed wind generation system based on Doubly Fed Induction Generator (DFIG). The stator of the generator is directly connected to the grid while the rotor is connected through a back-to-back converter which is dimensioned to stand only a fraction of the generator rated power. To harness the wind power efficiently the most reliable system in the present era is grid connected doubly fed induction generator. The DFIG brings the advantage of utilizing the turns ratio of the machine, so the converter does not need to be rated for the machine’s full rated power. The rotor side converter (RSC) usually provides active and reactive power control of the machine while the grid-side converter (GSC) keeps the voltage of the DC-link constant. The additional freedom of reactive power generation by the GSC is usually not used due to the fact that it is more preferable to do so using the RSC. However, within the available current capacity the GSC can be controlled to participate in reactive power generation in steady state as well as during low voltage periods. The GSC can supply the required reactive current very quickly while the RSC passes the current through the machine resulting in a delay. Both converters can be temporarily overloaded, so the DFIG is able to provide a considerable contribution to grid voltage support during short circuit periods. This report deals with the introduction of DFIG, AC/DC/AC converter control and finally the SIMULINK/MATLAB simulation for isolated Induction generator as well as for grid connected Doubly Fed Induction Generator and corresponding results and waveforms are displayed

Classification of cow diet based on milk mid infrared spectra: a data analysis competition at the "International workshop of spectroscopy and chemometrics 2022"

In April 2022, the Vistamilk SFI Research Centre organized the second edition of the "International Workshop on Spectroscopy and Chemometrics - Applications in Food and Agriculture". Within this event, a data challenge was organized among participants of the workshop. Such data competition aimed at developing a prediction model to discriminate dairy cows' diet based on milk spectral information collected in the mid-infrared region. In fact, the development of an accurate and reliable discriminant model for dairy cows' diet can provide important authentication tools for dairy processors to guarantee product origin for dairy food manufacturers from grass-fed animals. Different statistical and machine learning modelling approaches have been employed during the workshop, with different pre-processing steps involved and different degree of complexity. The present paper aims to describe the statistical methods adopted by participants to develop such classification model.Comment: 27 pages, 9 figure