Arrival Patterns Research Articles

Multipath arrival tracking for marine vehicles utilizing pattern recognition

In recent years, interest in the Arctic Region has been steadily growing as it has become more accessible due to continued ice recession. Extreme temperatures and significant ice cover have created a unique and challenging acoustic environment. At increased distances, individual ray path data become inconsistent due to improper ray path identification and fading. Through the use of pattern recognition, a multipath arrival tracking algorithm was developed to utilize the unique characteristics associated with each individual ray path for long range tracking purposes. This tracking algorithm analyzes the amplitude and arrival time patterns amongst all individual ray paths in order to accurately identify each ray path as scattering and fading occurs, thereby increasing range-tracking capabilities. This becomes especially useful in the Arctic Region as contacts of interest can be tracked regardless of their position above, below, or within the Beaufort Duct—a sound duct from 100 to 200 m depth. Simulations covering the numerous depth combinations of sources and receivers with respect to the Beaufort Duct highlight the effectiveness that is presented by utilizing multipath arrival data. The developed algorithm takes advantage of these unique patterns in order to provide a tracking capability for marine vehicles to employ. [Work supported by ONR.]

Acoustic mapping of ocean currents using moving vehicles

With the increased availability of highly maneuverable unmanned surface and underwater vehicles, abundant ocean environmental data can now be collected. Most environmental surveys by unmanned vehicles conduct point measurements of the ocean properties along survey lines. This study uses tomographic techniques to extend the survey area covered by the autonomous vehicles and obtain a synoptic ocean current distribution. An acoustic reciprocal transmission experiment was carried out on June 27, 2017, in ChaoJing Bay nearby Keelung City, where the water depth varies from 20 m to 65 m. A total of three tomographic sensors were deployed and they were installed on an AUV, a fishing boat, and a bottom-moored buoy. Reciprocal acoustic transmissions between the mobile platforms were used to estimate ocean currents, which required accounting for the Doppler effects on the acoustic arrival patterns and the resulting differential travel times. The estimated areal currents show consistency with the ADCP current measurement from the boat when the covered area was near the deeper water.

Characterizing residence patterns of North Atlantic right whales in the southeastern USA with a multistate open robust design model

ESR Endangered Species Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials ESR 36:279-295 (2018) - DOI: https://doi.org/10.3354/esr00902 Characterizing residence patterns of North Atlantic right whales in the southeastern USA with a multistate open robust design model A. M. Krzystan1,*, T. A. Gowan1, W. L. Kendall2, J. Martin3, J. G. Ortega-Ortiz4, K. Jackson1, A. R. Knowlton5, P. Naessig6, M. Zani5, D. W. Schulte6, C. R. Taylor6 1Florida Fish & Wildlife Research Institute, St. Petersburg, FL 33701, USA 2U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO 80523, USA 3U.S. Geological Survey, Wetland and Aquatic Research Center, Gainesville, FL 32653, USA 4University of South Florida, College of Marine Science, St. Petersburg, FL 33701, USA 5New England Aquarium, Boston, MA 02110, USA 6Sea to Shore Alliance, Sarasota, FL 34233, USA *Corresponding author: andrea.krzystan@myfwc.com ABSTRACT: Effective conservation of endangered North Atlantic right whales Eubalaena glacialis requires information about their spatio-temporal distribution. Understanding temporal distribution is particularly important, because a portion of the population migrates between high-latitude summer feeding grounds off the northeastern USA and Canadian Maritimes coasts and lower-latitude calving and wintering grounds off the southeastern US coast (SEUS). Here, we modeled SEUS residence patterns using photo-identification data from coastal South Carolina, Georgia, and Florida from 7 winter seasons (2004/2005-2010/2011). We used multistate open robust design models to evaluate effects of reproductive status, demographic group, and environmental conditions on SEUS residence. Model estimates accounted for temporal variation and imperfect detection and provided probabilities of entering the SEUS, staying in the SEUS, and being sighted in the SEUS. We also derived estimates for residence time and seasonal abundance. We observed staggered arrival and departure patterns and demographic differences in residence patterns that are characteristic of a differential migration strategy. Calving females arrived earliest and, in most seasons, had mean residence periods more than twice as long as other demographic groups. Conversely, adult males arrived the latest and had the shortest residence times. Within-season detection was positively influenced by survey effort, and overall seasonal mean (±SE) detection rate estimates ranged from 0.83 ± 0.08 for non-calving adult females to 0.98 ± 0.02 for calving females. Results provide insights into right whale behavior, biology, and temporal distribution in the SEUS and can be used to evaluate spatially and temporally dynamic management measures. KEY WORDS: Mark-recapture · Open robust design · Phenology · Residence · Eubalaena glacialis · North Atlantic right whale Full text in pdf format Supplementary material PreviousNextCite this article as: Krzystan AM, Gowan TA, Kendall WL, Martin J and others (2018) Characterizing residence patterns of North Atlantic right whales in the southeastern USA with a multistate open robust design model. Endang Species Res 36:279-295. https://doi.org/10.3354/esr00902 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in ESR Vol. 36. Online publication date: August 29, 2018 Print ISSN: 1863-5407; Online ISSN: 1613-4796 Copyright © 2018 Inter-Research.

Carcass concealment alters assemblages and reproduction of forensically important beetles

Most forensic studies have examined decomposition and insect colonization for estimating the minimum postmortem interval (PMImin) using carcasses that are readily accessible to insects but in homicides, cadavers are often concealed to a certain extent. Here, we tested the hypothesis that the type of concealment of a decomposing resource and the permeability of the material used affect to different extents the animals attracted to the resource. To this end, beetle assemblages were documented for 100days on 15 domestic pig carcasses, placed individually in soft shell suitcases, trashcans with a hinged lid, and sealable drums in an untended open field in New Brunswick, Canada. Five pigs were allocated to each treatment. During the study, almost 33,000 beetle occurrences from 50 recognizable taxonomic units were documented around containers that concealed carcasses. Results indicated that trashcans, drums, and suitcases had different effects on the patterns of beetle arrival and departure from the vicinity of concealed carcasses, on beetle assembly, on their breeding strategies, as well as on the interspecific relationships between beetles of forensic importance. Of the 50 recognizable taxonomic units, only six species exhibited a somewhat predictable occurrence and yielded information about the time of placement or the type of container. Results also suggested that some of the abundant Silphidae species opted to breed or feed in suboptimal conditions or at a later period to avoid competing with the dominant silphid Necrodes surinamensis (Fabr.). This suggests the occurrence of preferential colonization and/or asymmetrical competition between beetle species, which would affect the potential of these species for PMImin estimations on concealed carcasses.

A Simulation-Based Dynamic Programming Method for Interchange Scheduling of Port Collecting and Distributing Network

As one of the effective methods to reduce congestion, grade intersection has already been changed to interchange in port collecting and distributing network (PCDN) of many Chinese ports, since the first interchange was built in the PCDN of Dalian port in 1924. Due to the growing demand for port freight transportation, congestion in PCDN is becoming one of the inevitable problems that need to be solved. This paper addresses the best interchange scheduling multistage decision problem in PCDN at a network level. The main challenges are how to estimate the delay time and cope with high uncertainties in port network and PCDN. Therefore, a simulation-based dynamic programming (DP) model is proposed with the purpose of minimizing total cost in lifetime period by combining a DP model and two nested simulation models together. Two simulation models are built to figure out the delay cost in the optimization model, which cannot be calculated by mathematical analysis due to complex vehicle travel patterns and irregular traffic volume caused by random events, such as the arrival pattern of ships’, natural conditions, and storage period of cargos. Finally, a real project in northern China is presented as a case study. The proposed method can be applied in similar cases and can help solve analogous complicated multistage problems.

Column Generation for the Integrated Berth Allocation, Quay Crane Assignment, and Yard Assignment Problem

This study investigates an integrated optimization problem on the three main types of resources used in container terminals: berths, quay cranes, and yard storage space. It presents a mixed integer linear programming model, which takes account of the decisions of berth allocation, quay crane assignment, and yard storage space unit assignment for incoming vessels. In addition, since the majority of the liner shipping services operate according to a weekly arrival pattern, the periodicity of the plan is also considered in the model and in the proposed algorithm. To solve the model on large-scale instances, a column generation (CG) procedure is developed to provide a lower bound for the integrated problem, in which an exact pseudopolynomial algorithm is designed for the pricing problems. Using this procedure, we propose a CG-based heuristic with different solution strategies and apply dual stabilization techniques to accelerate the algorithm. Based on some realistic instances, we conduct extensive numerical experiments to validate the effectiveness of the proposed model and the efficiency of the algorithm. The results show that the CG-based heuristic can yield a good solution with an approximate 1% optimality gap within a much shorter computation time than that of CPLEX. The online appendix is available at https://doi.org/10.1287/trsc.2018.0822 .

Red/LeD: An Asymptotically Optimal and Scalable Online Algorithm for Service Caching at the Edge

Edge servers, which are small servers located close to mobile users, have the potential to greatly reduce delay and backhaul traffic of mobile Internet applications by moving cloud services to the edge of the network. Due to limited capacity of edge servers and dynamic request arrival, proper service caching at the edge is essential to guarantee good performance. This paper proposes a tractable online algorithm called retrospective download with least-requested deletion that caches services dynamically without any assumptions on the arrival patterns of mobile applications. We evaluate the competitive ratio of our policy, which quantifies the worst case performance in comparison to an optimal offline policy. We prove that the competitive ratio of our policy is linear with the capacity of the edge server. We also show that no deterministic online policy can achieve a competitive ratio that is asymptotically better than ours. Moreover, we prove that our policy is scalable, in the sense that it only needs doubled capacity to achieve a constant competitive ratio. The utility of our online policy is further evaluated on real-world traces. These trace-based simulations demonstrate that our policy has better, or similar, performance compared with many intelligent offline policies.

Design of passengers’ circulation areas at the transfer station: An automated hybrid simulation-differential evolution framework

The capacity design of passengers’ circulation areas at the transfer stations is a crucial issue from the designers’ point of view. An adequate design reflects the better performance while inadequacy leads to the congestion issues at the transfer stations. To overcome the shortcomings in the design guide (TCRP-165 Report) and existing exponential-based (M/G(n)/N/N) design model, this paper reports a novel Discrete-Event Simulation (DES) model based on the Phase-Type (PH)-distributed random variates for the circulation areas (passageways and stairways), integrated with the Differential Evolution (DE) optimization approach. This hybrid simulation-optimization model is an automated framework that simultaneously works to provide optimal widths of the passengers’ circulation areas, under a desirable Level-of-Service (LOS) and blocking probability criteria. The passengers’ arrival and service time pattern of the circulation areas in the DES model is described by the PH distribution, which accurately takes into account the variation of passengers’ arrival at different instants as well as randomness and state-dependent service time of the circulation areas. The comparison of proposed model with exponential-based (M/G(n)/N/N) model and TCRP-165 Report reveals that: (1) the optimal widths of circulation areas obtained by proposed model are greater than both M/G(n)/N/N and TCRP-100 Report; (2) the optimal width increases with the increase in squared coefficient of variation for all the passengers’ arrival rates, LOS and lengths of the circulation areas; (3) the optimal width values drop from LOS B towards D; (4) our proposed simulation-DE optimization outperforms the simulation-GA optimization approach in term of the speed of convergence. This novel hybrid simulation-DE optimization framework is an effective tool for the designers of transfer stations in decision making process and analyzing the complex facilities with ease.

Emergency department arrival times in Florida heart failure patients utilizing Fisher-Rao curve registration: A descriptive population-based study

BackgroundEmergency room utilization and hospital readmission rates are disproportionately high for heart failure patients (HF). Emergency department (ED) utilization is intimately intertwined with hospital readmissions. ObjectiveDescribe the arrival time distribution of HF patients presenting to the ED. MethodThe study analyzed heart failure discharge data from the Florida State Emergency Department Database and the Florida State Inpatient Database from the Healthcare Cost and Utilization Project of the Agency for Healthcare Research and Quality. Data were treated as a Poisson process and analyzed using functional data analysis tools. ResultsHF arrivals are multi-modal with the largest peak arrival time in the middle of the day as well as a smaller peak in the early morning hours, especially in rural areas. ConclusionsThe arrival pattern has minor differences in rural and urban areas. HF clinic appointments should be established in the early morning hours when these patients utilize the ED.

Design of Observation Units for Acute Decompensated Heart Failure Patients

In light of reported benefits, including reduced unnecessary hospitalization as well as post-discharge events, an increasing number of hospitals are considering the option of introducing observation units for acute decompensated heart failure patients. While implementing this strategy, the hospital administrators face the questions of (i) whether it is cost effective to create an observation unit; (ii) if so, what the capacity of the observation unit should be; (iii) which optimal admission and discharge policies would improve the efficiency of these units under a given capacity. The objective of paper is to propose a two-phase methodological framework to design an observation unit for acute decomposition heart failure patients in order to provide insights for these questions. In the first phase, by studying the various patient volumes and arrival patterns based on a data set from a large secondary hospital, we investigate the optimal capacity regarding the number of beds to be installed in an observation unit through analyzing multiple analytical models. In the second phase, we study the possible admission and discharge policies for this observation unit with a fixed capacity. We assess each policy under various quality of care and cost criteria including probability of post-discharge events, likelihood of hospitalization, the total cost and cost-gain ratio. We provide managerial insights for hospital administrators through extensive numerical experiments.

PENDEKATAN TEORI ANTRIAN SINGLE CHANNEL SINGLE PHASE PADA PELAYANAN ADMINISTRASI

The queuing theory is a branch of the applied probability theory that had been used for 108 years ago to study telephone traffic congestion (1910-2018). Until now the queue theory is widely used for alternative queue troubleshooting. The purpose of this research is to know queue system at campus administration service AMIK BSI Bandung have exponential pattern of service distribution, and measure of effectiveness of queuing system using WinQSB. Queue on campus administration services using single channel single phase model. The results of the analysis obtained the pattern of arrival of the distributed Poisson, the effectiveness of administrative services 8.33% for the average arrival of 1, 16.67% for the average arrival of 2, and 25% for the average arrival of 3, while the average service assumption is 12 There is no need for additional service personnel with idle time greater than 83%.

<italic>TrafficShaper:</italic> Shaping Inter-Datacenter Traffic to Reduce the Transmission Cost

The emerging deployment of geographically distributed data centers (DCs) incurs a significant amount of data transfers over the Internet. Such transfers are typically charged by Internet service providers with the widely adopted $q$ th percentile charging model . In such a charging model, the time slots with top ( $100-q$ ) percent of data transmission do not affect the total transmission cost and can be viewed as “free.” This brings the opportunity to optimize the scheduling of inter-DC transfers to minimize the entire transmission cost. However, a very little work has been done to exploit those “free” time slots for scheduling inter-DC transfers. The crux is that existing work either lacks a mechanism to accumulate traffic to “free” time slots, or inevitably relies on prior knowledge of future traffic arrival patterns. In this paper, we present TrafficShaper , a new scheduler that shapes the inter-DC traffic to exploit the “free” time slots involved in the $q$ th percentile charging model , so as to reduce or even minimize the transmission cost. When shaping traffic, TrafficShaper advocates a simple principle: more traffic peaks should be scheduled in “free” time slots, while less traffic differentiation should be maintained among the remaining time slots. To this end, TrafficShaper designs a pricing-aware control framework, which makes online decisions for inter-DC transfers without requiring a prior knowledge of traffic arrivals. To verify the performance of TrafficShaper , we conduct rigorous theoretical analysis based on Lyapunov optimization techniques, large-scale trace-driven simulations, and small-scale testbed implementation. Results from rigorous mathematical analyses demonstrate that TrafficShaper can make the transmission cost arbitrarily close to the optimum value. Extensive trace-driven simulation results show that TrafficShaper can reduce the transmission cost by up to 40.23%, compared with the state-of-the-art solutions. The testbed experiments further verify that TrafficShaper can realistically reduce the transmission cost by up to 19.38%.

Integration of tourism markets in Australia

By applying the methodological framework of transition modeling and econometric convergence tests introduced by Phillips and Sul, we reveal the existence of convergence clubs and transition convergence paths of international visitor arrivals for Australia. Specifically, by using monthly data of international arrivals over the period of January 1991 to September 2017, we provide evidence that tourism markets can integrate. The analysis suggests the identification of five distinct convergence clubs. This in turn signifies an integration phenomenon of Australia’s tourism market, which is revealed through the different convergence patterns of international visitor arrivals. Finally, it is evident that the revealed integration behavior of Australia’s international tourism market will enable policy makers to target better tourism needs through customized policies.

A study on the impact of prioritising emergency department arrivals on the patient waiting time.

In the past decade, the crowding of the emergency department has gained considerable attention of researchers as the number of medical service providers is typically insufficient to fulfil the demand for emergency care. In this paper, we solve the stochastic emergency department workforce planning problem and consider the planning of nurses and physicians simultaneously for a real-life case study in Belgium. We study the patient arrival pattern of the emergency department in depth and consider different patient acuity classes by disaggregating the arrival pattern. We determine the personnel staffing requirements and the design of the shifts based on the patient arrival rates per acuity class such that the resource staffing cost and the weighted patient waiting time are minimised. In order to solve this multi-objective optimisation problem, we construct a Pareto set of optimal solutions via the 𝜖-constraints method. For a particular staffing composition, the proposed model minimises the patient waiting time subject to upper bounds on the staffing size using the Sample Average Approximation Method. In our computational experiments, we discern the impact of prioritising the emergency department arrivals. Triaging results in lower patient waiting times for higher priority acuity classes and to a higher waiting time for the lowest priority class, which does not require immediate care. Moreover, we perform a sensitivity analysis to verify the impact of the arrival and service pattern characteristics, the prioritisation weights between different acuity classes and the incorporated shift flexibility in the model.

How likely am I to find parking? – A practical model-based framework for predicting parking availability

Parking availability information (or occupancy of parking facility) is highly valued by travelers, and is one of the most important inputs to many parking models. This paper proposes a model-based practical framework to predict future occupancy from historical occupancy data alone. The framework consists of two modules: estimation of model parameters, and occupancy prediction. At the core of the predictive framework, a queuing model is employed to describe the stochastic occupancy change of a parking facility. While the underlying queuing model can be any reasonable model, we demonstrate the framework with the well-established continuous-time Markov M\\M\\C\\C queue in this paper. The possibility of adopting a different queuing model that can potentially incorporate the parking-searching process is also discussed. The parameter estimation module and the occupancy prediction module are both built on the underlying queuing model. To apply the estimation and prediction methods in real world, a few practical considerations are accounted for in the framework with methods to handle variations of arrival and departure patterns from day to day and within a day, including special events. The proposed framework and models are validated using both simulated and real data. Our San Francisco case studies demonstrate that the parameters estimated offline can lead to accurate predictions of parking facility occupancy both with and without real-time update. We also performed extensive numerical experiments to compare the proposed framework and methods with several pure machine-learning methods in recent literature. It is found that our approach delivers equal or better performance, but requires a computation time that is orders of magnitude less to tune and train the model. Additionally, our approach can predict for any time in the future with one training process, while pure machine-learning methods have to train a specific model for a different prediction interval to achieve the same level of accuracy.

Improving all-reduce collective operations for imbalanced process arrival patterns

Two new algorithms for the all-reduce operation optimized for imbalanced process arrival patterns (PAPs) are presented: (1) sorted linear tree, (2) pre-reduced ring as well as a new way of online PAP detection, including process arrival time estimations, and their distribution between cooperating processes was introduced. The idea, pseudo-code, implementation details, benchmark for performance evaluation and a real case example for machine learning are provided. The results of the experiments were described and analyzed, showing that the proposed solution has high scalability and improved performance in comparison with the usually used ring and Rabenseifner algorithms.

The evolution process of queues at signalized intersections under batch arrivals

The randomness of queue formation and dissipation at signalized intersections is a very important feature of traffic phenomenon, which can be directly reflected by the dynamics and randomness of queue length during a signal cycle. Focusing on the dynamic nature and uncertainty of queues at signalized intersections, a random queueing model with vacations for queues on the multilane is constructed. In particular, the periodic unavailability of signalized intersection and the queue length transitions during a cycle are explicitly taken into account under batch arrival pattern. The steady-state distributions of the queue length along with the time within one cycle on multilane are derived. With it, the effects of batch arrival characteristics of vehicles on the evolution of queue formation and dissipation at signalized intersections are fully captured. The analytical model is compared with Webster’s formula and is further validated with field data, which show that the proposed queueing model is capable of providing a reliable estimation on the queue length In addition, numerical experiments are also conducted to clarify how the queueing process along with the time is affected by different batch arrival patterns, traffic demands, and signal timing, etc.

A stochastic framework for modeling random-sized batch arrivals of sediment particles into open channel flows

Arrivals of sediment particles into open channel flows are typically simulated deterministically. However, there are random quantities of sediment particles brought into the open channel by natural phenomena such as rainfalls, landslides, mudflows, and dam breaches. As such, sediment particles may arrive at channel flow at random times in probabilistic quantities. Such arrival processes of sediment particles cannot be fully described without using probability. A stochastic framework that can account for the arrivals of random-sized batches of sediment particles into receiving waters is proposed. In this work, the random-sized batch arrival process of sediment particles is introduced to evaluate comprehensively the effects of particle arrival patterns, including random occurrences and random quantities of incoming sediment particles, on particle transport rates and sediment concentrations. Random arrivals are simulated as a Poisson process and the number of sediment particles in each arriving batch is described by a stochastic process that is specified in terms of binomially distributed random variables herein. The stochastic diffusion particle tracking model is used to simulate random trajectories of moving particles. Particle deposition and resuspension processes are considered. A probabilistic description of discrete sediment transport based on ensemble statistics of sediment concentrations and transport rates is presented. Simulation results are validated with experimental observations. Finally, time-dependent risks (i.e., probability of exceeding a pre-established turbidity standard with respect to time) are provided for decision makers to have a more comprehensive assessment for water quality management.

Simplex based three-dimensional eigenray search for underwater predictions.

A solution for the calculation of three-dimensional (3D) eigenrays based on Simplex optimization, implemented in a 3D Gaussian beam model, is investigated in this paper. The validation and performance of the solution were analyzed through comparisons against an equivalent (flat) two-dimensional waveguide, and against results of a tank scale experiment presented in Sturm and Korakas [(2013). J. Acoust. Soc. Am. 133(1), 108-118], in which cross-slope propagation in a wedge waveguide with a mild slope was considered. It was found that the search strategy based on Simplex optimization was able to calculate efficiently and accurately 3D eigenrays, thus providing predictions of arrival patterns along cross-slope range, which replicated elaborate patterns of mode shadow zones, intra-mode interference, and mode arrivals. A remarkable aspect of the search strategy was its ability to provide accurate values of initial eigenray elevation and azimuth, within the accuracy defined for the eigenray to arrive at the location of a given hydrophone.

Optimal Capacity Provisioning for Online Job Allocation With Hard Allocation Ratio Requirement

The problem of allocating jobs to appropriate servers in cloud computing is studied in this paper. We consider that the jobs of various types arrive in some unpredictable pattern and the system is required to allocate a certain ratio of jobs. In order to meet the hard allocation ratio requirement in the presence of unknown arrival patterns, one can increase the capacity of servers by expanding the size of data centers. We then aim to find the minimum capacity needed to meet a given allocation ratio requirement. We study this problem for both systems with persistent jobs, such as video streaming, and systems with dynamic jobs, such as database queries. For both systems, we propose online job allocation policies with low complexity. For systems with persistent jobs, we prove that our policies can achieve a given hard allocation ratio requirement with the least capacity. For systems with dynamic jobs, the capacity needed for our policies to achieve the hard allocation ratio requirement is close to a theoretical lower bound. Two other popular policies are studied, and we demonstrate that they need at least an order higher capacity to meet the same hard allocation ratio requirement. Simulation results demonstrate that our policies remain far superior than the other two even, when the jobs arrive according to some random process.