Sub-activity site selection and activity choice modelling in planned special events

2021 ◽  
pp. 1-16
Author(s):  
Mahmut Esad Ergin
Keyword(s):  
Site Selection ◽  
Traffic Management ◽  
Activity Patterns ◽  
Choice Modelling ◽  
Data Set ◽  
Master Plans ◽  
Research Areas ◽  
Modelling Framework ◽  
Special Events ◽  
Main Activity

The purpose of the study is to evaluate the impacts of the variables on site selection decision of the spectators just before the main activity in order to engage in eating, having fun and performing other types of activities. A multinomial logit modelling framework is hired to model activity patterns within PSE circumstances. Activities were classified into three groups that are “Eating”, “Entertainment”, and “Other”. Model estimation on PSE survey data set from selected stadiums in Istanbul shows that due to the congestion, as travel time and activity duration increase the spectators inclined to be around the stadium 184 minutes in average before the starting time of the main activity. The results obtained from this study can be used as a micro input for the macro studies such as transportation master plans and urban plans and can offer complementary research areas for PSE traffic management and urban planning.

scholarly journals Occurrence of bottlenose dolphins (Tursiops truncatus) in the Río Negro Estuary, Argentina, and their mid-distance movements along the northeastern Patagonian coast

10.5597/00226 ◽  
2017 ◽  
Vol 11 (1-2) ◽  
pp. 170-177
Author(s):  
Mauricio Failla ◽  
Verónica A. Seijas ◽  
Els Vermeulen
Keyword(s):  
Tursiops Truncatus ◽  
Feeding Activity ◽  
Bottlenose Dolphins ◽  
Activity Patterns ◽  
Long Distance ◽  
Identification Rate ◽  
Photo Identification ◽  
Rio Negro ◽  
Patagonian Coast ◽  
Main Activity

A systematic study was carried out on bottlenose dolphins (Tursiops truncatus) in the Río Negro Estuary (RNE), Patagonia, Argentina, to analyze their occurrence and activity patterns in this region. The photo-identification data of this study was further compared to data from an adjacent region to gain information on the animals' movements along the northeastern Patagonian coast. Information was gathered through land-based observations between the months of March and July of 2008 up to 2011. Data on dolphin activity patterns were collected via an ad libitum focal-group sampling mode. At the same time, dorsal fin images were obtained from as many dolphins as possible for identification and subsequent re-identification of individuals. Total effort equaled 188h, resulting in 58h of observation of 124 dolphin groups [sightings per unit effort (SPUE) = 0.66 group/h]. Most of the groups observed contained between one and five individuals, and two main activity states could be determined, namely traveling (65%) and foraging (26%). The photo-identification effort, which started opportunistically in 2006, resulted in a catalogue of 17 individual dolphins, with a total mean re-identification rate of nine days (max. = 24 days). When comparing these pictures to the existing catalogue of Bahía San Antonio (BSA; approximately 200km west from the study area) dorsal fins of 15 individuals could be matched and most (n = 12) could be subsequently re-identified in both areas, indicating their long distance movements along the northeastern Patagonian coast during the austral autumn months. This season coincides with the lowest dolphin abundance and feeding activity in BSA. This study indicates that bottlenose dolphins enter the RNE to forage at least during autumn. It further suggests that the search for food resources is the main trigger for their movement patterns along the northeastern Patagonian coast during this season, at least for certain individuals. More research is needed to accurately confirm these hypotheses.

scholarly journals A short geostatistical study of the three-dimensional spatial structure of fumonisins in stored maize

2010 ◽  
Vol 3 (1) ◽  
pp. 95-103 ◽  
Author(s):  
M. Rivas Casado ◽  
D. Parsons ◽  
N. Magan ◽  
R. Weightman ◽  
P. Battilani ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Spatial Structure ◽  
Spatial Correlation ◽  
Three Dimensional ◽  
Sample Collection ◽  
Data Set ◽  
Research Areas ◽  
Geostatistical Approach ◽  
Sampling Protocols ◽  
Current Sample

The heterogeneous three-dimensional spatial distribution of mycotoxins has proven to be one of the main limitations for the design of effective sampling protocols. Current sample collection protocols for mycotoxins have been designed to estimate the mean concentration and fail to characterise the spatial distribution of the mycotoxin concentration due to the aggregation of the incremental samples. Geostatistical techniques have been successfully applied to overcome similar problems in many research areas. However, little work has been developed on the use of geostatistics for the design of sampling protocols for mycotoxins. This paper focuses on the analysis of the two and three-dimensional spatial structure of fumonisins B1 (FB1) and B2 (FB2) in maize in a bulk store using a geostatistical approach and on how results help determine the number and location of incremental samples to be collected. The spatial correlation between FB1 and FB2, as well as between the number of kernels infected and the level of contamination was investigated. For this purpose, a bed of maize was sampled at different depths to generate a unique three-dimensional data set of FB1 and FB2. The analysis found no clear evidence of spatial structure in either the two-dimensional or three-dimensional analyses. The number of Fusarium infected kernels was not a good indicator for the prediction of fumonisin concentration and there was no spatial correlation between the concentrations of the two fumonisins.

A Latent Feature Model Approach to Biclustering

2016 ◽  
Vol 6 (2) ◽  
pp. 11-28
Author(s):  
José Caldas ◽  
Samuel Kaski
Keyword(s):  
Expectation Maximization Algorithm ◽  
Learning Task ◽  
Biological Data ◽  
Feature Model ◽  
Data Matrix ◽  
Data Types ◽  
Data Set ◽  
Modelling Framework ◽  
Number Variation ◽  
Mirna Expression Data

Biclustering is the unsupervised learning task of mining a data matrix for useful submatrices, for instance groups of genes that are co-expressed under particular biological conditions. As these submatrices are expected to partly overlap, a significant challenge in biclustering is to develop methods that are able to detect overlapping biclusters. The authors propose a probabilistic mixture modelling framework for biclustering biological data that lends itself to various data types and allows biclusters to overlap. Their framework is akin to the latent feature and mixture-of-experts model families, with inference and parameter estimation being performed via a variational expectation-maximization algorithm. The model compares favorably with competing approaches, both in a binary DNA copy number variation data set and in a miRNA expression data set, indicating that it may potentially be used as a general-problem solving tool in biclustering.

Combining Clustering and Factor Analysis as Complementary Techniques

2020 ◽  
Vol 1 (2) ◽  
pp. 48-57
Author(s):  
Lakshmi Prayaga ◽  
Krishna Devulapalli ◽  
Chandra Prayaga
Keyword(s):  
Factor Analysis ◽  
Traffic Management ◽  
Clustering Algorithm ◽  
Perfect Match ◽  
Machine Learning Algorithms ◽  
Insurance Companies ◽  
Data Set ◽  
Traffic Violations ◽  
Traffic Violation ◽  
Complementary Techniques

The study of driver behavior and associated accidents has been of interest to researchers and insurance companies. From the perspective of insurance companies, identifying factors that contribute to traffic violations plays a significant role in providing insurance quotes as it establishes the basis for charging appropriate insurance rates to customers. This study assesses the traffic violations intensity for 64 counties in the state of Florida, USA by using the publicly available traffic violations data set. This data set consists of 3,669,796 records with 11 attributes, which include race, gender, driver's age, type of driving violation, etc. The 187 types of traffic violations are categorized into 11 broad traffic violations categories. Two machine learning algorithms, factor analysis and k-means clustering, were applied in this study. After applying factor analysis, a new comprehensive traffic violation index (TVI) was developed, which quantified the traffic violation intensity of each county. All the counties in the data set were ranked with the TVI scores, and the counties with high TVI scores were identified. K-means clustering algorithm was then applied to the same data, and four clusters of counties were derived. The counties that were grouped in each cluster were compared with the TVI scores to check if the counties in each cluster had similar TVI scores. The counties with the highest TVI scores are found to be grouped in one cluster, followed by counties with the next high TVI scores in the second cluster, and so on. Thus, it is observed that there is a perfect match in the results of both models. They serve as two techniques complementary to each other, in that the k-means clustering method groups counties with comparable traffic violation intensities and factor analysis is able to also rank individual counties according to the TVI. These techniques have identified the counties with high traffic violation intensities, which helps the policymakers to take adequate measures for traffic management.

Quantile Regression–Based Estimation of Dynamic Statistical Contingency Fuel

2020 ◽  
Author(s):  
Lei Kang ◽  
Mark Hansen
Keyword(s):  
Quantile Regression ◽  
Traffic Management ◽  
Carbon Dioxide Emission ◽  
Estimation Procedure ◽  
Air Traffic Management ◽  
Aviation Industry ◽  
Fuel Loading ◽  
Data Set ◽  
Fuel Burn ◽  
Quantile Regression Method

Reducing fuel consumption is a unifying goal across the aviation industry. One fuel-saving opportunity for airlines is reducing contingency fuel loading by dispatchers. Many airlines’ flight planning systems (FPSs) provide recommended contingency fuel for dispatchers in the form of statistical contingency fuel (SCF). However, because of limitations of the current SCF estimation procedure, the application of SCF is limited. In this study, we propose to use quantile regression–based machine learning methods to account for fuel burn uncertainties and estimate more reliable SCF values. Utilizing a large fuel burn data set from a major U.S.-based airline, we find that the proposed quantile regression method outperforms the airline’s FPS. The benefit of applying the improved SCF models is estimated to be in the range $19 million–$65 million in fuel expense savings as well as 132 million–451 million kilograms of carbon dioxide emission reductions per year, with the lower savings being realized even while maintaining the current, extremely low risk of tapping the reserve fuel. The proposed models can also be used to predict benefits from reduced fuel loading enabled by increasing system predictability, for example, with improved air traffic management.

Activity patterns, movements and burrows of platypuses (Ornithorhynchus anatinus) in a sub-alpine Tasmanian lake

2000 ◽  
Vol 48 (6) ◽  
pp. 701 ◽  
Author(s):  
Helen M. Otley ◽  
Sarah A. Munks ◽  
Mark A. Hindell
Keyword(s):  
Adult Male ◽  
Site Selection ◽  
Activity Patterns ◽  
Predation Pressure ◽  
Water Bodies ◽  
Male And Female ◽  
Frequent Use ◽  
Ornithorhynchus Anatinus ◽  
North Western ◽  
Tracking Period

Adult male and female platypuses were radio-tracked in summer and winter at Lake Lea, north-western Tasmania. They appeared to exhibit greater diurnality, particularly during winter months, a greater degree of overland movement and more frequent use of non-earth refuge sites than do animals inhabiting mainland water bodies. Individuals foraged continuously for up to 16 h, with longer foraging bouts observed during the winter tracking period. Foraging range varied between 2 and 58 ha, with no significant differences observed between the sexes or seasons. All platypuses foraged predominantly in the lake; however, a number of animals were observed moving overland to and from waterbodies and burrows. Burrows were located on lake, creek and pool edges. A high percentage of burrows (25%) were located within dense sedge tussocks and scrub vegetation. Both the terrestrial activity and more opportunistic burrow-site selection may be related to reduced predation pressure in Tasmania.

scholarly journals Weather Impact on Airport Performance

Aerospace ◽  
2018 ◽  
Vol 5 (4) ◽  
pp. 109 ◽  
Author(s):  
Michael Schultz ◽  
Sandro Lorenz ◽  
Reinhard Schmitz ◽  
Luis Delgado
Keyword(s):  
Traffic Management ◽  
Probability Distributions ◽  
Weather Conditions ◽  
Air Traffic ◽  
Weather Data ◽  
Data Set ◽  
Airport Capacity ◽  
Weather Events ◽  
The Individual ◽  
The Impact

Weather events have a significant impact on airport performance and cause delayed operations if the airport capacity is constrained. We provide quantification of the individual airport performance with regards to an aggregated weather-performance metric. Specific weather phenomena are categorized by the air traffic management airport performance weather algorithm, which aims to quantify weather conditions at airports based on aviation routine meteorological reports. Our results are computed from a data set of 20.5 million European flights of 2013 and local weather data. A methodology is presented to evaluate the impact of weather events on the airport performance and to select the appropriate threshold for significant weather conditions. To provide an efficient method to capture the impact of weather, we modelled departing and arrival delays with probability distributions, which depend on airport size and meteorological impacts. These derived airport performance scores could be used in comprehensive air traffic network simulations to evaluate the network impact caused by weather induced local performance deterioration.

scholarly journals A Scalable Approach for Short-Term Predictions of Link Traffic Flow by Online Association of Clustering Profiles

2020 ◽  
Vol 21 (2) ◽  
pp. 119-124
Author(s):  
Alessandro Attanasi ◽  
Marco Pezzulla ◽  
Luca Simi ◽  
Lorenzo Meschini ◽  
Guido Gentile
Keyword(s):  
Real Time ◽  
Traffic Management ◽  
Time Window ◽  
Management Control ◽  
Spatial Correlations ◽  
Time Data ◽  
Short Term ◽  
Data Set ◽  
Affinity Propagation Algorithm ◽  
Short Term Prediction

AbstractShort-term prediction of traffic flows is an important topic for any traffic management control room. The large availability of real-time data raises not only the expectations for high accuracy of the forecast methodology, but also the requirements for fast computing performances. The proposed approach is based on a real-time association of the latest data received from a sensor to the representative daily profile of one among the clusters that are built offline based on an historical data set using Affinity Propagation algorithm. High scalability is achieved ignoring spatial correlations among different sensors, and for each of them an independent model is built-up. Therefore, each sensor has its own clusters of profiles with their representatives; during the short-term forecast operation the most similar representative is selected by looking at the last data received in a specified time window and the proposed forecast corresponds to the values of the cluster representative.

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