Activity Sequence Generation Using Universal Mobility Patterns

2021 ◽  
pp. 036119812110624
Author(s):  
Wim Ectors ◽  
Bruno Kochan ◽  
Davy Janssens ◽  
Tom Bellemans ◽  
Geert Wets
Keyword(s):  
Power Law ◽  
Sequential Sampling ◽  
Model Development ◽  
Generation Model ◽  
Mobility Patterns ◽  
Power Law Distribution ◽  
Sequence Generation ◽  
Activity Sequence ◽  
Basic Set ◽  
The World

Previous work has established that rank ordered single-day activity sequences from various study areas exhibit a universal power law distribution called Zipf’s law. By analyzing datasets from across the world, evidence was provided that it is in fact a universal distribution. This study focuses on a potential mechanism that leads to the power law distribution that was previously discovered. It makes use of 15 household travel survey (HTS) datasets from study areas all over the world to demonstrate that reasonably accurate sets of activity sequences (or “schedules”) can be generated with extremely little information required; the model requires no input data and contains few tunable parameters. The activity sequence generation mechanism is based on sequential sampling from two universal distributions: (i) the distributions of the number of activities (trips) and (ii) the activity types (trip purposes). This paper also attempts to demonstrate the universal nature of these distributions by fitting several equations to the 15 HTS datasets. The lightweight activity sequence generation model can be implemented in any (lightweight) transportation model to create a basic set of activity sequences, saving effort and cost in data collection and in model development and calibration.

scholarly journals The structure of co-publications multilayer network

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ghislain Romaric Meleu ◽  
Paulin Yonta Melatagia
Keyword(s):  
Power Law ◽  
Degree Distribution ◽  
Preferential Attachment ◽  
Small World ◽  
Generation Model ◽  
Small World Network ◽  
Power Law Distribution ◽  
Multilayer Networks ◽  
Multilayer Network ◽  
Scientific Papers

AbstractUsing the headers of scientific papers, we have built multilayer networks of entities involved in research namely: authors, laboratories, and institutions. We have analyzed some properties of such networks built from data extracted from the HAL archives and found that the network at each layer is a small-world network with power law distribution. In order to simulate such co-publication network, we propose a multilayer network generation model based on the formation of cliques at each layer and the affiliation of each new node to the higher layers. The clique is built from new and existing nodes selected using preferential attachment. We also show that, the degree distribution of generated layers follows a power law. From the simulations of our model, we show that the generated multilayer networks reproduce the studied properties of co-publication networks.

Priority Weighted Fitness Model in Networks

2012 ◽  
Vol 229-231 ◽  
pp. 1854-1857
Author(s):  
Xin Yi Chen
Keyword(s):  
World Wide Web ◽  
Power Law ◽  
Common Property ◽  
World Wide ◽  
Genetic Networks ◽  
Power Law Distribution ◽  
Large Networks ◽  
Scale Free ◽  
The World ◽  
Complex Topology

Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a power-law distribution. This feature was found to be a consequence of three generic mechanisms: (i) networks expand continuously by the addition of new vertices, (ii) new vertex with priority selected different edges of weighted selected that connected to different vertices in the system, and (iii) by the fitness probability that a new vertices attach preferentially to sites that are already well connected. A model based on these ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena. Experiment results show that the model is more close to the actual Internet network.

scholarly journals Exploring the Citywide Human Mobility Patterns of Taxi Trips through a Topic-Modeling Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hui Xiong ◽  
Kaiqiang Xie ◽  
Lu Ma ◽  
Feng Yuan ◽  
Rui Shen
Keyword(s):  
Power Law ◽  
Traffic Management ◽  
Large Scale ◽  
Latent Dirichlet Allocation ◽  
Topic Model ◽  
Human Mobility ◽  
Mobility Patterns ◽  
Power Law Distribution ◽  
Modeling Analysis ◽  
Wide Range

Understanding human mobility patterns is of great importance for a wide range of applications from social networks to transportation planning. Toward this end, the spatial-temporal information of a large-scale dataset of taxi trips was collected via GPS, from March 10 to 23, 2014, in Beijing. The data contain trips generated by a great portion of taxi vehicles citywide. We revealed that the geographic displacement of those trips follows the power law distribution and the corresponding travel time follows a mixture of the exponential and power law distribution. To identify human mobility patterns, a topic model with the latent Dirichlet allocation (LDA) algorithm was proposed to infer the sixty-five key topics. By measuring the variation of trip displacement over time, we find that the travel distance in the morning rush hour is much shorter than that in the other time. As for daily patterns, it shows that taxi mobility presents weekly regularity both on weekdays and on weekends. Among different days in the same week, mobility patterns on Tuesday and Wednesday are quite similar. By quantifying the trip distance along time, we find that Topic 44 exhibits dominant patterns, which means distance less than 10 km is predominant no matter what time in a day. The findings could be references for travelers to arrange trips and policymakers to formulate sound traffic management policies.

scholarly journals Using Power-Law Degree Distribution to Accelerate PageRank

2012 ◽  
Vol 1 (2) ◽  
pp. 63-70
Author(s):  
Zhaoyan Jin ◽  
Quanyuan Wu
Keyword(s):  
World Wide Web ◽  
Power Law ◽  
Real World ◽  
Degree Distribution ◽  
World Wide ◽  
Power Law Distribution ◽  
The World ◽  
Real World Applications ◽  
Real World Datasets ◽  
Law Degree

The PageRank vector of a network is very important, for it can reflect the importance of a Web page in the World Wide Web, or of a people in a social network. However, with the growth of the World Wide Web and social networks, it needs more and more time to compute the PageRank vector of a network. In many real-world applications, the degree and PageRank distributions of these complex networks conform to the Power-Law distribution. This paper utilizes the degree distribution of a network to initialize its PageRank vector, and presents a Power-Law degree distribution accelerating algorithm of PageRank computation. Experiments on four real-world datasets show that the proposed algorithm converges more quickly than the original PageRank algorithm.DOI: 10.18495/comengapp.12.063070

Mining Information Spreading Based on Users' Retweet Behavior in Twitter

2013 ◽  
Vol 380-384 ◽  
pp. 2866-2870 ◽  
Author(s):  
Rong Ze Xia ◽  
Yan Jia ◽  
Wang Qun Lin ◽  
Hu Li
Keyword(s):  
Social Networks ◽  
Power Law ◽  
Power Law Distribution ◽  
Spreading Process ◽  
Spreading Model ◽  
The World ◽  
Information Spread ◽  
Information Spreading

Twitter is one of the largest social networks in the world. People could share contents on it. When we interact with each other, the information spreads. And its users retweet behavior that makes information spread so fast. So there comes an important question: Whats about users retweet behavior? Could we simulate information spreading in twitter by retweeting behavior? We crawl twitter and mine information spreading based on users retweet behavior in it. Through our dateset, we verify the power-law distribution of the retweet-width and retweet-depth. At the same time, we study the correlation between retweet-width and retweet-depth. Finally, we propose an information spreading model to simulate the information spreading process in twitter and get a good result.

Correlating international inflowing and outflowing remittances and migrants

2020 ◽  
Vol 31 (03) ◽  
pp. 2050047
Author(s):  
J. Hernández-Casildo ◽  
M. del Castillo-Mussot ◽  
E. Hernández-Ramirez ◽  
L. Guzmán-Vargas
Keyword(s):  
World Bank ◽  
Power Law ◽  
Statistical Correlation ◽  
Power Law Distribution ◽  
Economic Phenomenon ◽  
International Remittances ◽  
The World ◽  
And Migration

Remittances, as money or goods that people send to families and friends, are very important social and economic phenomenon at local, national, regional and international levels. In the year 2017, total international remittances were at levels around USD 613 billion. From World Bank bilateral remittances and migration matrixes, we calculate for each country and territory its aggregated or total amount of remittances inflow (TRI) coming from the rest of the world, its total remittances outflow (TRO) extracted from that country and sent to all other countries, its total emigrant stock (TEMI) living overseas, and its total number of foreign-world immigrants (TIMM) living in that country. For each of these quantities, its highest-ranked countries follow an approximate Pareto power law distribution. Remittances and migrants flow in opposite directions, the statistical correlation [Formula: see text] between TRI and TEMI is 0.79, and between TRO and TIMM is 0.97. Both inflowing remittances per emigrant TRI/TEMI and outflowing remittances per immigrant TRO/TIMM fluctuate approximately around 3100 USD per year.

Power-Law Distribution of the World Wide Web

Science ◽  
2000 ◽  
Vol 287 (5461) ◽  
pp. 2115 ◽  
Author(s):  
Lada A. Adamic ◽  
Bernardo A. Huberman ◽  
A.-L. Barabási ◽  
R. Albert ◽  
H. Jeong ◽  
...  
Keyword(s):  
World Wide Web ◽  
Power Law ◽  
World Wide ◽  
Power Law Distribution ◽  
The World

Mobility Pattern of Taxi Passengers at Intra-Urban Scale: Empirical Study of Three Cities

2017 ◽  
Vol 5 (6) ◽  
pp. 537-555 ◽  
Author(s):  
Mengqiao Xu ◽  
Ling Zhang ◽  
Wen Li ◽  
Haoxiang Xia
Keyword(s):  
Power Law ◽  
Urban Areas ◽  
Scaling Laws ◽  
Human Mobility ◽  
Mobility Pattern ◽  
Mobility Patterns ◽  
Power Law Distribution ◽  
Long Distance ◽  
Distance Travel ◽  
Long Distance Travel

AbstractThe study of human mobility patterns is of both theoretical and practical values in many aspects. For long-distance travel, a few research endeavors have shown that the displacements of human travels follow a power-law distribution. However, controversies remain regarding the issue of the scaling laws of human mobility in intra-urban areas. In this work, we focus on the mobility pattern of taxi passengers by examining five datasets of three metropolitans. Through statistical analysis, we find that the lognormal distribution with a power-law tail can best approximate both the displacement and the duration time of taxi trips in all the examined cities. The universality of the scaling laws of human mobility is subsequently discussed, in view of the analysis of the data. The consistency of the statistical properties of the selected datasets that cover different cities and study periods suggests that, the identified pattern of taxi-based intra-urban travels seems to be ubiquitous over cities and time periods.

scholarly journals Power-law behavior of transcription factor dynamics at the single-molecule level implies a continuum affinity model

2021 ◽  
Author(s):  
David A Garcia ◽  
Gregory Fettweis ◽  
Diego M Presman ◽  
Ville Paakinaho ◽  
Christopher Jarzynski ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Single Molecule ◽  
Power Law ◽  
Dwell Time ◽  
Specific Binding ◽  
Power Law Distribution ◽  
Single Molecule Level ◽  
Binding Behavior ◽  
Chromatin Template ◽  
Nuclear Domains

Abstract Single-molecule tracking (SMT) allows the study of transcription factor (TF) dynamics in the nucleus, giving important information regarding the diffusion and binding behavior of these proteins in the nuclear environment. Dwell time distributions obtained by SMT for most TFs appear to follow bi-exponential behavior. This has been ascribed to two discrete populations of TFs—one non-specifically bound to chromatin and another specifically bound to target sites, as implied by decades of biochemical studies. However, emerging studies suggest alternate models for dwell-time distributions, indicating the existence of more than two populations of TFs (multi-exponential distribution), or even the absence of discrete states altogether (power-law distribution). Here, we present an analytical pipeline to evaluate which model best explains SMT data. We find that a broad spectrum of TFs (including glucocorticoid receptor, oestrogen receptor, FOXA1, CTCF) follow a power-law distribution of dwell-times, blurring the temporal line between non-specific and specific binding, suggesting that productive binding may involve longer binding events than previously believed. From these observations, we propose a continuum of affinities model to explain TF dynamics, that is consistent with complex interactions of TFs with multiple nuclear domains as well as binding and searching on the chromatin template.

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