scholarly journals Dimensionality Reduction to Reveal Urban Truck Driver Activity Patterns

2018 ◽  
Vol 2672 (9) ◽  
pp. 81-92 ◽  
Author(s):  
Fangping Lu ◽  
Fang Zhao ◽  
Lynette Cheah
Keyword(s):  
Dimensionality Reduction ◽  
Urban Areas ◽  
Input Data ◽  
Activity Patterns ◽  
Reconstruction Error ◽  
Truck Drivers ◽  
Daily Activities ◽  
Variational Autoencoder ◽  
Components Analysis ◽  
Logistic Pca

This paper studies the activity profiles of truck drivers in urban areas. Finding repeating dynamical patterns is important in understanding freight behaviors, and aids freight-friendly planning. In the digital age, data on truck drivers is becoming more available with heterogeneous demographic and work profiles. By synthesizing such pervasive data and applying machine learning concepts, this paper proposes to identify signature travel activity patterns via dimensionality reduction. Based on driver survey data, truck drivers’ behaviors are represented as longitudinal activity sequences. Dimensionality reduction and activity reconstruction via principal components analysis (PCA), logistic PCA, and autoencoder were conducted to reveal fundamental activity features and approximate the underlying data-generating function. In the driver survey dataset, 243 truck drivers in Singapore reported their daily activities for 1,099 weekdays. This study found that PCA produced the most faithful reconstruction of drivers’ activities. When projecting the input data down from 2,592 to 82 dimensions, PCA explained 77% of variances with a reconstruction error of 0.99%. Logistic PCA is a useful extension of PCA to study the pattern of a single activity. It captures the variation of infrequent activities such as truck queuing, which PCA fails to reconstruct. Autoencoder was found to be more powerful than PCA in reconstructing activities – with 1% of original dimensions, it reconstructed the activities with an error rate of 1.24%. Moreover, when implemented as a variational autoencoder, autoencoder generated realistic-looking samples of driver activities. The top three most distinctive activity patterns of Singapore truck drivers are reported using PCA.

scholarly journals Calibration and Validation of ArcGIS Solar Radiation Tool for Photovoltaic Potential Determination in the Netherlands

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1865
Author(s):  
Bala Bhavya Kausika ◽  
Wilfried G. J. H. M. van Sark
Keyword(s):  
Solar Radiation ◽  
The Netherlands ◽  
Urban Areas ◽  
Input Data ◽  
Solar Irradiation ◽  
Energy Yield ◽  
Atmospheric Parameters ◽  
Factors Affecting ◽  
Calibration And Validation ◽  
Solar Radiation Model

Geographic information system (GIS) based tools have become popular for solar photovoltaic (PV) potential estimations, especially in urban areas. There are readily available tools for the mapping and estimation of solar irradiation that give results with the click of a button. Although these tools capture the complexities of the urban environment, they often miss the more important atmospheric parameters that determine the irradiation and potential estimations. Therefore, validation of these models is necessary for accurate potential energy yield and capacity estimations. This paper demonstrates the calibration and validation of the solar radiation model developed by Fu and Rich, employed within ArcGIS, with a focus on the input atmospheric parameters, diffusivity and transmissivity for the Netherlands. In addition, factors affecting the model’s performance with respect to the resolution of the input data were studied. Data were calibrated using ground measurements from Royal Netherlands Meteorological Institute (KNMI) stations in the Netherlands and validated with the station data from Cabauw. The results show that the default model values of diffusivity and transmissivity lead to substantial underestimation or overestimation of solar insolation. In addition, this paper also shows that calibration can be performed at different time scales depending on the purpose and spatial resolution of the input data.

Identification of Potential Freight Parking Locations in Urban Areas

2021 ◽  
pp. 036119812110130
Author(s):  
Glenn Vorhes ◽  
Ernest Perry ◽  
Soyoung Ahn
Keyword(s):  
United States ◽  
Information System ◽  
Satellite Imagery ◽  
Urban Areas ◽  
The United States ◽  
Truck Drivers ◽  
Short List ◽  
Automated Processes ◽  
Parking Facilities ◽  
Urban Sites

Truck parking is a crucial element of the United States’ transportation system as it provides truckers with safe places to rest and stage for deliveries. Demand for truck parking spaces exceeds supply and shortages are especially common in and around urban areas. Freight operations are negatively affected as truck drivers are unable to park in logistically ideal locations. Drivers may resort to unsafe practices such as parking on ramps or in abandoned lots. This report seeks to examine the potential parking availability of vacant urban parcels by establishing a methodology to identify parcels and examining whether the identified parcels are suitable for truck parking. Previous research has demonstrated that affordable, accessible parcels are available to accommodate truck parking. When used in conjunction with other policies, adaptation of urban sites could help reduce the severity of truck parking shortages. Geographic information system parcel and roadway data were obtained for one urban area in each of the 10 Mid America Association of Transportation Officials region states. Area and proximity filters were applied followed by spectral analysis of satellite imagery to identify candidate parcels for truck parking facilities within urban areas. The automated processes created a ranked short list of potential parcels from which those best suited for truck parking could be efficiently identified for inspection by satellite imagery. This process resulted in a manageable number of parcels to be evaluated further by local knowledge metrics such as availability and cost, existing infrastructure and municipal connections, and safety.

scholarly journals Adaptations of the red fox (Vulpes vulpes) to urban environments in Sydney, Australia

2020 ◽  
Vol 6 (1) ◽  
Author(s):  
Margarita Gil-Fernández ◽  
Robert Harcourt ◽  
Thomas Newsome ◽  
Alison Towerton ◽  
Alexandra Carthey
Keyword(s):  
Urban Areas ◽  
Vulpes Vulpes ◽  
Red Fox ◽  
Activity Patterns ◽  
Urban Environments ◽  
Canis Lupus Familiaris ◽  
Red Foxes ◽  
High Degree ◽  
Sydney Australia ◽  
Site Types

Abstract With urban encroachment on wild landscapes accelerating globally, there is an urgent need to understand how wildlife is adapting to anthropogenic change. We compared the behaviour of the invasive red fox (Vulpes vulpes) at eight urban and eight peri-urban areas of Sydney, Australia. We observed fox behaviour around a lure and compared fox activity patterns to those of potential prey and to two domestic predators (dogs—Canis lupus familiaris and cats—Felis catus). We assessed the influence of site type, vegetation cover, and distance from habitation on fox behaviour, and compared the temporal activity patterns of urban and peri-urban red foxes. Urban red foxes were marginally more nocturnal than those in peri-urban areas (88% activity overlap). There was greater overlap of red fox activity patterns with introduced mammalian prey in urban areas compared with peri-urban areas (90% urban vs 84% peri-urban). Red fox temporal activity overlapped 78% with cats, but only 20% with dogs, across both site types. The high degree of overlap with cats and introduced mammalian prey is most likely explained by the nocturnal behaviour of these species, while pet dogs are generally kept in yards or indoors at night. The behavioural differences we documented by urban red foxes suggest they may adapt to human modifications and presence, by being more nocturnal and/or more confident in urban areas.

scholarly journals An Unsupervised Learning-Based Network Threat Situation Assessment Model for Internet of Things

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Hongyu Yang ◽  
Renyun Zeng ◽  
Fengyan Wang ◽  
Guangquan Xu ◽  
Jiyong Zhang
Keyword(s):  
Internet Of Things ◽  
Unsupervised Learning ◽  
Network Traffic ◽  
Test Group ◽  
Reconstruction Error ◽  
Assessment Model ◽  
Generative Adversarial Networks ◽  
Situation Assessment ◽  
Variational Autoencoder ◽  
Threat Situation

With the wide application of network technology, the Internet of Things (IoT) systems are facing the increasingly serious situation of network threats; the network threat situation assessment becomes an important approach to solve these problems. Aiming at the traditional methods based on data category tag that has high modeling cost and low efficiency in the network threat situation assessment, this paper proposes a network threat situation assessment model based on unsupervised learning for IoT. Firstly, we combine the encoder of variational autoencoder (VAE) and the discriminator of generative adversarial networks (GAN) to form the V-G network. Then, we obtain the reconstruction error of each layer network by training the network collection layer of the V-G network with normal network traffic. Besides, we conduct the reconstruction error learning by the 3-layer variational autoencoder of the output layer and calculate the abnormal threshold of the training. Moreover, we carry out the group threat testing with the test dataset containing abnormal network traffic and calculate the threat probability of each test group. Finally, we obtain the threat situation value (TSV) according to the threat probability and the threat impact. The simulation results show that, compared with the other methods, this proposed method can evaluate the overall situation of network security threat more intuitively and has a stronger characterization ability for network threats.

A New Scan Conversion Algorithm for Ultrasound Compound Scanning

1985 ◽  
Vol 7 (3) ◽  
pp. 215-224 ◽  
Author(s):  
Seung-Woo Lee ◽  
Song-Bai Park
Keyword(s):  
Real Time ◽  
Input Data ◽  
Linear Interpolation ◽  
Reconstruction Error ◽  
Ultrasound Images ◽  
Two Dimensional ◽  
Spatial Compounding ◽  
One Dimensional ◽  
Conversion Algorithm ◽  
Scan Conversion

An improved scan conversion algorithm for ultrasound compound scanning is proposed. In this algorithm, the input data in the spatial domain is sampled by the concentric square raster sampling (CSRS) method, and the display pixel data are filled by one-dimensional linear interpolation. The reconstruction error of the proposed algorithm is much smaller than that of other algorithms, because only one-dimensional, rather than two-dimensional, interpolation is involved. This algorithm greatly simplifies implementation of a real-time digital scan converter (DSC) for spatial compounding of ultrasound images.

scholarly journals Rurality and dietary patterns: associations in a UK cohort study of 10-year-old children

2014 ◽  
Vol 18 (8) ◽  
pp. 1436-1443 ◽  
Author(s):  
Tim T Morris ◽  
Kate Northstone
Keyword(s):  
Cohort Study ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Dietary Patterns ◽  
Rural Areas ◽  
Dietary Pattern ◽  
Urban Areas ◽  
Urban Group ◽  
Urban Rural ◽  
Components Analysis

AbstractObjectiveDespite differences in obesity and ill health between urban and rural areas in the UK being well documented, very little is known about differences in dietary patterns across these areas. The present study aimed to examine whether urban/rural status is associated with dietary patterns in a population-based UK cohort study of children.DesignDietary patterns were obtained using principal components analysis and cluster analysis of 3 d diet records collected from children at 10 years of age. Rurality was obtained from the 2001 UK Census urban/rural indicator at the time of dietary assessment. General linear models were used to examine the relationship between rurality and dietary pattern scores from principal components analysis; multinomial logistic regression was used to assess the association between rurality and dietary clusters.SettingThe Avon Longitudinal Study of Parents and Children (ALSPAC), South West England.SubjectsChildren (n 5677) aged 10 years (2817 boys and 2860 girls).ResultsAfter adjustment, increases in rurality were associated with increased scores on the ‘health awareness’ dietary pattern (β=0·35; 95 % CI 0·14, 0·56; P<0·001 for the most rural compared with the most urban group) and lower scores on the ‘packed lunch/snack’ dietary pattern (β=−0·39; 95 % CI −0·59, −0·19; P<0·001 for the most rural compared with the most urban group). The odds ratio for participants being in the ‘healthy’ compared with the ‘processed’ dietary cluster for the most rural areas was 1·61 (95 % CI 1·05, 2·49; P=0·02) compared with those in the most urban areas.ConclusionsThere is evidence to suggest that differences exist in dietary patterns between rural and urban areas. Similar results were found using two different methods of dietary pattern analysis, showing that children residing in rural households were more likely to consume healthier diets than those in urban households.

scholarly journals Spatial Distribution of Aedes aegypti Oviposition Temporal Patterns and Their Relationship with Environment and Dengue Incidence

Insects ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 919
Author(s):  
Verónica Andreo ◽  
Ximena Porcasi ◽  
Claudio Guzman ◽  
Laura Lopez ◽  
Carlos M. Scavuzzo
Keyword(s):  
Time Series ◽  
Spatial Distribution ◽  
Aedes Aegypti ◽  
Urban Areas ◽  
Mosquito Species ◽  
Activity Patterns ◽  
Positive Association ◽  
Temporal Patterns ◽  
Transmission Risk ◽  
Machine Learning Classification

Aedes aegypti, the mosquito species transmitting dengue, zika, chikungunya and yellow fever viruses, is fully adapted to thrive in urban areas. The temporal activity of this mosquito, however, varies within urban areas which might imply different transmission risk. In this work, we hypothesize that temporal differences in mosquito activity patterns are determined by local environmental conditions. Hence, we explore the existence of groups of temporal patterns in weekly time series of Ae. aegypti ovitraps records (2017–2019) by means of time series clustering. Next, with the aim of predicting risk in places with no mosquito field data, we use machine learning classification tools to assess the association of temporal patterns with environmental variables derived from satellite imagery and predict temporal patterns over the city area to finally test the relationship with dengue incidence. We found three groups of temporal patterns that showed association with land cover diversity, variability in vegetation and humidity and, heterogeneity measured by texture indices estimated over buffer areas surrounding ovitraps. Dengue incidence on a neighborhood basis showed a weak but positive association with the percentage of pixels belonging to only one of the temporal patterns detected. The understanding of the spatial distribution of temporal patterns and their environmental determinants might then become highly relevant to guide the allocation of prevention and potential interventions. Further investigation is still needed though to incorporate other determinants not considered here.

scholarly journals More Urbanization, Fewer Bats: The Importance of Forest Conservation in Honduras

2021 ◽  
Author(s):  
Manfredo A. Turcios-Casco ◽  
Richard K. LaVal ◽  
Marcio Martínez ◽  
Hefer D. Ávila-Palma
Keyword(s):  
Urban Areas ◽  
Forest Conservation ◽  
Private Property ◽  
Central City ◽  
Activity Patterns ◽  
Old Growth ◽  
Bat Activity ◽  
The Family ◽  
Agricultural Use ◽  
Urbanized Areas

Urbanization is a phenomenon that results in fragmentation and eventual destruction of forests. Suburbanization is a subset of that same phenomenon in which fragmentation has resulted in the retention of small patches of the original forest and surviving old growth trees. Alternatively, the area surrounding the central city had been cleared for agricultural use and the suburban residents have planted many trees in parks and private property. This fragmentation will of course affect many species of bats, including species of the family Phyllostomidae. In this work, we estimate and compare the diversity of phyllostomid bats in three landscapes in Honduras: forests, suburban, and urban areas, from 2015 to 2018. Concurrently, we compared bat activity patterns based on the hour and percentage of moonlight at the time they were captured, and we compared external measurements, forearm and ear length. Urban areas are the least diverse and exhibited the lowest abundance. The forearm and ear length were significantly different only between forests and urban areas. The degree of lunar phobia also differed among those landscapes, but the time of capture did not differ. This is the first attempt to describe the activity patterns of phyllostomids in these studied areas and the effect of urbanization on Honduran bats. As expected, we found that from forests to cities, the diversity and abundance of phyllostomids decreased. However, there are many gaps in our knowledge of how totally or partially urbanized areas are affecting phyllostomid bats in Honduras.

scholarly journals VAE-SNE: a deep generative model for simultaneous dimensionality reduction and clustering

2020 ◽  
Author(s):  
Jacob M. Graving ◽  
Iain D. Couzin
Keyword(s):  
Dimensionality Reduction ◽  
Image Data ◽  
Stereotyped Behavior ◽  
General Purpose ◽  
Coarse Grained ◽  
High Dimensional ◽  
Nonlinear Dimensionality Reduction ◽  
Multi Scale ◽  
Variational Autoencoder ◽  
Real World Datasets

AbstractScientific datasets are growing rapidly in scale and complexity. Consequently, the task of understanding these data to answer scientific questions increasingly requires the use of compression algorithms that reduce dimensionality by combining correlated features and cluster similar observations to summarize large datasets. Here we introduce a method for both dimension reduction and clustering called VAE-SNE (variational autoencoder stochastic neighbor embedding). Our model combines elements from deep learning, probabilistic inference, and manifold learning to produce interpretable compressed representations while also readily scaling to tens-of-millions of observations. Unlike existing methods, VAE-SNE simultaneously compresses high-dimensional data and automatically learns a distribution of clusters within the data — without the need to manually select the number of clusters. This naturally creates a multi-scale representation, which makes it straightforward to generate coarse-grained descriptions for large subsets of related observations and select specific regions of interest for further analysis. VAE-SNE can also quickly and easily embed new samples, detect outliers, and can be optimized with small batches of data, which makes it possible to compress datasets that are otherwise too large to fit into memory. We evaluate VAE-SNE as a general purpose method for dimensionality reduction by applying it to multiple real-world datasets and by comparing its performance with existing methods for dimensionality reduction. We find that VAE-SNE produces high-quality compressed representations with results that are on par with existing nonlinear dimensionality reduction algorithms. As a practical example, we demonstrate how the cluster distribution learned by VAE-SNE can be used for unsupervised action recognition to detect and classify repeated motifs of stereotyped behavior in high-dimensional timeseries data. Finally, we also introduce variants of VAE-SNE for embedding data in polar (spherical) coordinates and for embedding image data from raw pixels. VAE-SNE is a robust, feature-rich, and scalable method with broad applicability to a range of datasets in the life sciences and beyond.

scholarly journals Pola Aktivitas Ibu Rumah Tangga Terhadap Pemanfaatan Ruang Terbuka di Rumah Susun

2011 ◽  
Vol 2 (2) ◽  
pp. 1372
Author(s):  
Yosica Mariana
Keyword(s):  
Urban Areas ◽  
Open Space ◽  
Activity Patterns ◽  
Descriptive Analysis ◽  
Analysis Method ◽  
Literature Study ◽  
Open Spaces ◽  
Three Stages ◽  
Remote Sensing Interpretation

In flat environments, housewives are most found staying throughout the day. They use existing open spaces in housing project to interact with other residents. To find out, discover and analyze the correlation between the pattern of open space utilization and the pattern of activity of housewives at flats, this research was conducted using descriptive analysis method bases on case studies on some flats in urban areas, namely Kebon Kacang Flat (KK), Kemayoran Flat (K), Taman Surya Flat (TS) and Pasar Jumat Flat (PJ). Subjects were housewives (residents of the flats); sampling is taken by stratified random sampling. The survey was conducted by interview to obtain data on activity patterns of the mother. Subsequently, observation was conducted to get an overview of the activity patterns of mothers and use of open space including non-physical and physical data of these open spaces. The implementation was done in three stages: preparation (literature study and data collection by remote sensing), interpretation, field test and re-interpretation (width, location, quality of open spaces and activities, professions of women at these locations), and result presentation. 

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