Quantitative network analysis for passenger pattern recognition: An analysis of railway stations

2015 ◽  
Author(s):  
Martin Zsifkovits ◽  
Marian Sorin Nistor ◽  
Silja Meyer-Nieberg
Keyword(s):  
Pattern Recognition ◽  
Network Analysis ◽  
Railway Stations

Metabolomics combined with pattern recognition and bioinformatics analysis methods for the development of pharmacodynamic biomarkers on liver fibrosis

2017 ◽  
Vol 13 (8) ◽  
pp. 1575-1583 ◽  
Author(s):  
Junwei Fang ◽  
Liping Wang ◽  
Yang Wang ◽  
Mingfeng Qiu ◽  
Yongyu Zhang
Keyword(s):  
Pattern Recognition ◽  
Network Analysis ◽  
Liver Fibrosis ◽  
Bioinformatics Analysis ◽  
Analysis Methods ◽  
Pharmacodynamic Biomarkers

Metabolomics combined with pattern recognition and network analysis maybe an attractive strategy for the pharmacodynamics biomarkers development on liver fibrosis.

scholarly journals Using multiple hybrid spatial design network analysis to predict longitudinal effect of a major city centre redevelopment on pedestrian flows

2019 ◽  
Author(s):  
Crispin H. V. Cooper ◽  
Ian Harvey ◽  
Scott Orford ◽  
Alain J. F. Chiaradia
Keyword(s):  
Network Analysis ◽  
Shortest Path ◽  
City Centre ◽  
Demand Model ◽  
Railway Stations ◽  
Spatial Design ◽  
Demand Models ◽  
Directional Change ◽  
Spatial Network Analysis ◽  
Changes Over Time

AbstractPredicting how changes to the urban environment layout will affect the spatial distribution of pedestrian flows is important for environmental, social and economic sustainability. We present longitudinal evaluation of a model of the effect of urban environmental layout change in a city centre (Cardiff 2007–2010), on pedestrian flows. Our model can be classed as regression based direct demand using Multiple Hybrid Spatial Design Network Analysis (MH-sDNA) assignment, which bridges the gap between direct demand models, facility-based activity estimation and spatial network analysis (which can also be conceived as a pedestrian route assignment based direct demand model). Multiple theoretical flows are computed based on retail floor area: everywhere to shops, shop to shop, railway stations to shops and parking to shops. Route assignment, in contrast to the usual approach of shortest path only, is based on a hybrid of shortest path and least directional change (most direct) with a degree of randomization. The calibration process determines a suitable balance of theoretical flows to best match observed pedestrian flows, using generalized cross-validation to prevent overfit. Validation shows that the model successfully predicts the effect of layout change on flows of up to approx. 8000 pedestrians per hour based on counts spanning a 1 km2 city centre, calibrated on 2007 data and validated to 2010 and 2011. This is the first time, to our knowledge, that a pedestrian flow model with assignment has been evaluated for its ability to forecast the effect of urban layout changes over time.

scholarly journals Terrorist Group Behavior Prediction by Wavelet Transform-Based Pattern Recognition

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Ze Li ◽  
Duoyong Sun ◽  
Bo Li ◽  
Zhanfeng Li ◽  
Aobo Li
Keyword(s):  
Pattern Recognition ◽  
Social Network ◽  
Wavelet Transform ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Group Behavior ◽  
Terrorist Group ◽  
Attack Behavior ◽  
Terrorist Groups ◽  
Terrorist Acts

Predicting terrorist attacks by group networks is an important but difficult issue in intelligence and security informatics. Effective prediction of the behavior not only facilitates the understanding of the dynamics of organizational behaviors but also supports homeland security’s missions in prevention, preparedness, and response to terrorist acts. There are certain dynamic characteristics of terrorist groups, such as periodic features and correlations between the behavior and the network. In this paper, we propose a comprehensive framework that combines social network analysis, wavelet transform, and the pattern recognition approach to investigate the dynamics and eventually predict the attack behavior of terrorist group. Our ideas rely on social network analysis to model the terrorist group and extract relevant features for group behaviors. Next, based on wavelet transform, the group networks (features) are predicted and mutually checked from two aspects. Finally, based on the predicted network, the behavior of the group is recognized based on the correlation between the network and behavior. The Al-Qaeda data are investigated with the proposed framework to show the strength of our approaches. The results show that the proposed framework is highly accurate and is of practical value in predicting the behavior of terrorist groups.

scholarly journals Visitor Mobility and Spatial Structure in a Local Urban Tourism Destination: GPS Tracking and Network analysis

2019 ◽  
Vol 11 (3) ◽  
pp. 919 ◽  
Author(s):  
Koun Sugimoto ◽  
Kei Ota ◽  
Shohei Suzuki
Keyword(s):  
Network Analysis ◽  
Urban Tourism ◽  
Gps Tracking ◽  
Physical Factors ◽  
Mobility Patterns ◽  
Railway Stations ◽  
Visitor Behavior ◽  
Positional System ◽  
Tourism Destinations ◽  
The Relationship

Visitor mobility is an important element for facilitating sustainable local economics and management in urban tourism destinations. Research on visitor mobility often focuses on the patterns and structures of spatial visitor behavior and the factors that influence them. This study examines the relationship between visitor mobility and urban spatial structures through an exploratory analysis of visitors’ movements and characteristics, which were collected from surveys with global positional system (GPS) tracking technologies and questionnaires. The Ueno district, one of the most popular tourism destinations in Tokyo, Japan, was selected as the study area. For local stakeholders, the low accessibility levels between this district’s park zone and downtown zone have become a major destination management issue. We compared visitor movements and flow networks in various places from different major trip origins (railway stations) by using several analysis techniques (GPS log distribution, spatial movement sequences, and network analysis), and examined physical and human factors that caused the different mobility patterns. The results demonstrated that physical factors, including major transport hubs (railway stations), topography, commercial accumulation, and POI distribution, affected intra-destination visitor behavior, and segmented visitor markets into different main zones. Such findings could inform future destination management policies and planning in local urban tourism destinations.

scholarly journals Complex Network Analysis of Pakistan Railways

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Yasir Tariq Mohmand ◽  
Aihu Wang
Keyword(s):  
Network Analysis ◽  
Structural Properties ◽  
Complex Network ◽  
Small World ◽  
Railway Network ◽  
Railway Stations ◽  
Complex Network Analysis

We study the structural properties of Pakistan railway network (PRN), where railway stations are considered as nodes while edges are represented by trains directly linking two stations. The network displays small world properties and is assortative in nature. Based on betweenness and closeness centralities of the nodes, the most important cities are identified with respect to connectivity as this could help in identifying the potential congestion points in the network.

Improvements to Ullmann's Algorithm for the Subgraph Isomorphism Problem

2015 ◽  
Vol 29 (07) ◽  
pp. 1550025 ◽  
Author(s):  
Uroš Čibej ◽  
Jurij Mihelič
Keyword(s):  
Pattern Recognition ◽  
Social Network ◽  
Network Analysis ◽  
State Of The Art ◽  
Isomorphism Problem ◽  
Exact Algorithms ◽  
Subgraph Isomorphism ◽  
Worst Case ◽  
Time Consumption ◽  
Space Requirements

The subgraph isomorphism problem is one of the most important problems for pattern recognition in graphs. Its applications are found in many different disciplines, including chemistry, medicine, and social network analysis. Because of the [Formula: see text]-completeness of the problem, the existing exact algorithms exhibit an exponential worst-case running time. In this paper, we propose several improvements to the well-known Ullmann's algorithm for the problem. The improvements lower the time consumption as well as the space requirements of the algorithm. We experimentally demonstrate the efficiency of our improvement by comparing it to another set of improvements called FocusSearch, as well as other state-of-the-art algorithms, namely VF2 and LAD.

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