scholarly journals Human Origin-Destination Flow Prediction Based on Large Scale Mobile Signal Data

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qiuyang Huang ◽  
Yongjian Yang ◽  
Yuanbo Xu ◽  
En Wang ◽  
Kangning Zhu
Keyword(s):  
Machine Learning ◽  
Mobile Phone ◽  
Disease Transmission ◽  
Large Scale ◽  
Population Distribution ◽  
Base Stations ◽  
Human Origin ◽  
Safety Control ◽  
Fine Grained ◽  
Flow Prediction

The human origin-destination (OD) flow prediction is of great significance for urban safety control, stampede prevention, disease transmission control, urban planning, and many other aspects. Most of the existing methods generally divide the urban area into grids and use vehicle GPS trajectories and metrocard check-in data, combined with machine learning or deep learning models to predict human OD flow. However, these kinds of methods are challenging to capture fine-grained human mobility patterns. Moreover, these methods usually deviate from the actual human OD transfer patterns on a citywide scale due to the particularity of different datasets. To this end, in this paper, we use large-scale mobile phone signal data to achieve human OD flow prediction between the coverage of varying signal base stations. Many signal base stations are distributed in urban geographical space, collecting all the mobile phone user’s location information to obtain large-scale fine-grained unbiased human OD flow data. Due to the lack of natural topology structure between base stations, this paper adopts a TGCN model combined with a graph fusion module to pretrain the dynamic population distribution prediction task. The parameters of the graph fusion module are employed to capture the different semantic information in the proposed hybrid machine learning method and finally achieve citywide human OD flow prediction. Extensive experiments on the real-world signal datasets in Changchun, China, demonstrate the effectiveness of our model.

Estimating shopping center visitor numbers based on a new hybrid fuzzy prediction method

2021 ◽  
pp. 1-14
Author(s):  
Cagatay Ozdemir ◽  
Sezi Cevik Onar ◽  
Selami Bagriyanik ◽  
Cengiz Kahraman ◽  
Burak Zafer Akalin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Large Scale ◽  
Prediction Method ◽  
Base Stations ◽  
Shopping Mall ◽  
Multi Criteria Decision Making ◽  
Shopping Malls ◽  
Data Production ◽  
Telecommunications Services

Companies started to determine their strategies based on intelligent data analysis due to stagey enhance data production. Literature reviews show that the number of resources where demand estimation, location analysis, and decision-making technique applied together with the machine learning method is low in all sectors and almost none in the shopping mall domain. Within this study’s scope, a new hybrid fuzzy prediction method has been developed that will estimate the customer numbers for shopping malls. This new methodology is applied to predict the number of visitors of three shopping malls on the Anatolian side of Istanbul. The forecasting study for corresponding shopping malls is made by using the daily signaling data from indoor base stations of large-scale technology and telecommunications services provider and the features to be used in machine learning models is determined by fuzzy multi criteria decision making method. Output revealed by the application of the fuzzy multi criteria decision making method enables the prioritization of features.

scholarly journals Fine-Scale Dasymetric Population Mapping with Mobile Phone and Building Use Data Based on Grid Voronoi Method

2020 ◽  
Vol 9 (6) ◽  
pp. 344
Author(s):  
Zhenghong Peng ◽  
Ru Wang ◽  
Lingbo Liu ◽  
Hao Wu
Keyword(s):  
Mobile Phone ◽  
Census Data ◽  
Population Distribution ◽  
Temporal Distribution ◽  
Ordinary Least Squares ◽  
Base Stations ◽  
Mobile Phone Data ◽  
Fine Scale ◽  
Voronoi Method ◽  
Population Mapping

Fine-scale population mapping is of great significance for capturing the spatial and temporal distribution of the urban population. Compared with traditional census data, population data obtained from mobile phone data has high availability and high real-time performance. However, the spatial distribution of base stations is uneven, and the service boundaries remain uncertain, which brings significant challenges to the accuracy of dasymetric population mapping. This paper proposes a Grid Voronoi method to provide reliable spatial boundaries for base stations and to build a subsequent regression based on mobile phone and building use data. The results show that the Grid Voronoi method gives high fitness in building use regression, and further comparison between the traditional ordinary least squares (OLS) regression model and geographically weighted regression (GWR) model indicates that the building use data can well reflect the heterogeneity of urban geographic space. This method provides a relatively convenient and reliable idea for capturing high-precision population distribution, based on mobile phone and building use data.

scholarly journals Large-Scale, Fine-Grained, Spatial, and Temporal Analysis, and Prediction of Mobile Phone Users’ Distributions Based upon a Convolution Long Short-Term Model

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2156 ◽  
Author(s):  
Guangyuan Zhang ◽  
Xiaoping Rui ◽  
Stefan Poslad ◽  
Xianfeng Song ◽  
Yonglei Fan ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Temporal Resolution ◽  
Large Scale ◽  
Short Term Memory ◽  
Social Geography ◽  
Temporal Analysis ◽  
Economic Research ◽  
Mobile Phone Data ◽  
Short Term ◽  
Fine Grained

Accurate and timely estimations of large-scale population distributions are a valuable input for social geography and economic research and for policy-making. The most popular large-scale method to calculate such estimations uses mobile phone data. We propose a novel method, firstly based upon using a kernel density estimation (KDE) to estimate dynamic mobile phone users’ distributions at a two-hourly scale temporal resolution. Secondly, a convolutional long short-term memory (ConvLSTM) model was used in our study to predict mobile phone users’ spatial and temporal distributions for the first time at such a fine-grained temporal resolution. The evaluation results show that the predicted people’s mobility derived from the mobile phone users’ density correlates much better with the actual density, both temporally and spatially, as compared to traditional methods such as time-series prediction, autoregressive moving average model (ARMA), and LSTM.

scholarly journals Radiofrequency Exposure Levels from Mobile Phone Base Stations in Outdoor Environments and an Underground Shopping Mall in Japan

2021 ◽  
Vol 18 (15) ◽  
pp. 8068
Author(s):  
Teruo Onishi ◽  
Miwa Ikuyo ◽  
Kazuhiro Tobita ◽  
Sen Liu ◽  
Masao Taki ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Urban Areas ◽  
Human Exposure ◽  
Large Scale ◽  
Time Course ◽  
Base Stations ◽  
Shopping Mall ◽  
Exposure Monitoring ◽  
Guideline Values ◽  
Exposure Levels

Recent progress in wireless technologies has made human exposure to electromagnetic fields (EMFs) increasingly complex. The situation can increase public concerns related to possible health effects due to EMF exposure. Monitoring EMF exposure levels and characterizing them are indispensable for risk communications of human exposure to EMFs. From this background, a project on the acquisition, accumulation, and applications of EMF exposure monitoring data in Japan was started in 2019. One of the objectives of this project is to obtain a comprehensive picture of EMF exposure in actual daily lives. In 2019 and 2020, we measured the electric field (E-field) strength from mainly mobile phone base stations in the same areas as those in measurements conducted in 2006 and 2007 by the Ministry of Internal Affairs and Communications (MIC), Japan, and compared the data to investigate the time-course of the EMF environment. The number of measured points was 100 (10 × 10 grids) in an area of 1 km × 1 km in two urban and two suburban areas, and that in an underground shopping mall was 158. This large-scale study is the first in Japan. As a result, we found that the measured E-field strengths tended to be higher in 2019 and 2020 than those in 2006 and 2007, especially in the mall. However, the median ratios to the Japanese radio wave protection guideline values for urban areas and malls are lower than −40 dB.

scholarly journals Mobile phone data highlights the role of mass gatherings in the spreading of cholera outbreaks

2016 ◽  
Vol 113 (23) ◽  
pp. 6421-6426 ◽  
Author(s):  
Flavio Finger ◽  
Tina Genolet ◽  
Lorenzo Mari ◽  
Guillaume Constantin de Magny ◽  
Noël Magloire Manga ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Disease Transmission ◽  
Large Scale ◽  
Human Mobility ◽  
Disease Outbreaks ◽  
Mobility Model ◽  
Phone Call ◽  
Mobile Phone Data ◽  
Major Influence ◽  
Mass Gatherings

The spatiotemporal evolution of human mobility and the related fluctuations of population density are known to be key drivers of the dynamics of infectious disease outbreaks. These factors are particularly relevant in the case of mass gatherings, which may act as hotspots of disease transmission and spread. Understanding these dynamics, however, is usually limited by the lack of accurate data, especially in developing countries. Mobile phone call data provide a new, first-order source of information that allows the tracking of the evolution of mobility fluxes with high resolution in space and time. Here, we analyze a dataset of mobile phone records of ∼150,000 users in Senegal to extract human mobility fluxes and directly incorporate them into a spatially explicit, dynamic epidemiological framework. Our model, which also takes into account other drivers of disease transmission such as rainfall, is applied to the 2005 cholera outbreak in Senegal, which totaled more than 30,000 reported cases. Our findings highlight the major influence that a mass gathering, which took place during the initial phase of the outbreak, had on the course of the epidemic. Such an effect could not be explained by classic, static approaches describing human mobility. Model results also show how concentrated efforts toward disease control in a transmission hotspot could have an important effect on the large-scale progression of an outbreak.

scholarly journals COVID-19 Asymptomatic Infection Estimation

2020 ◽  
Author(s):  
Yang Yu ◽  
Yu-Ren Liu ◽  
Fan-Ming Luo ◽  
Wei-Wei Tu ◽  
De-Chuan Zhan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Disease Transmission ◽  
Prediction Models ◽  
Real Data ◽  
Machine Learning Techniques ◽  
Fine Grained ◽  
Asymptomatic Patients ◽  
Containment Measures ◽  
Asymptomatic Infections ◽  
The Government

AbstractBackgroundMounting evidence suggests that there is an undetected pool of COVID-19 asymptomatic but infectious cases. Estimating the number of asymptomatic infections has been crucial to understand the virus and contain its spread, which is, however, hard to be accurately counted.MethodsWe propose an approach of machine learning based fine-grained simulator (ML-Sim), which integrates multiple practical factors including disease progress in the incubation period, cross-region population movement, undetected asymptomatic patients, and prevention and containment strength. The interactions among these factors are modeled by virtual transmission dynamics with several undetermined parameters, which are determined from epidemic data by machine learning techniques. When MLSim learns to match the real data closely, it also models the number of asymptomatic patients. MLSim is learned from the open Chinese global epidemic data.FindingsMLSim showed better forecast accuracy than the SEIR and LSTM-based prediction models. The MLSim learned from the data of China’s mainland reveals that there could have been 150,408 (142,178-157,417) asymptomatic and had self-healed patients, which is 65% (64% – 65%) of the inferred total infections including undetected ones. The numbers of asymptomatic but infectious patients on April 15, 2020, were inferred as, Italy: 41,387 (29,037 – 57,151), Germany: 21,118 (11,484 – 41,646), USA: 354,657 (277,641 – 495,128), France: 40,379 (10,807 – 186,878), and UK: 144,424 (127,215 – 171,930). To control the virus transmission, the containment measures taken by the government were crucial. The learned MLSim also reveals that if the date of containment measures in China’s mainland was postponed for 1, 3, 5, and 7 days later than Jan. 23, there would be 109,039 (129%), 183,930 (218%), 313,342 (371%), 537,555 (637%) confirmed cases on June 12.ConclusionsMachine learning based fine-grained simulators can better model the complex real-world disease transmission process, and thus can help decision-making of balanced containment measures. The simulator also revealed the potential great number of undetected asymptomatic infections, which poses a great risk to the virus containment.FundingNational Natural Science Foundation of China.

Evolution of Metastable Structures in Bimetallic Catalysts from Microscopy and Machine-Learning Molecular Dynamics

2020 ◽  
Author(s):  
Jin Soo Lim ◽  
Jonathan Vandermause ◽  
Matthijs A. van Spronsen ◽  
Albert Musaelian ◽  
Christopher R. O’Connor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Dynamics ◽  
Large Scale ◽  
Materials Science ◽  
Complete Characterization ◽  
Layer By Layer ◽  
Surface Restructuring ◽  
Metastable Structures ◽  
Mechanistic Investigation ◽  
Underlying Mechanisms

Restructuring of interface plays a crucial role in materials science and heterogeneous catalysis. Bimetallic systems, in particular, often adopt very different composition and morphology at surfaces compared to the bulk. For the first time, we reveal a detailed atomistic picture of the long-timescale restructuring of Pd deposited on Ag, using microscopy, spectroscopy, and novel simulation methods. Encapsulation of Pd by Ag always precedes layer-by-layer dissolution of Pd, resulting in significant Ag migration out of the surface and extensive vacancy pits. These metastable structures are of vital catalytic importance, as Ag-encapsulated Pd remains much more accessible to reactants than bulk-dissolved Pd. The underlying mechanisms are uncovered by performing fast and large-scale machine-learning molecular dynamics, followed by our newly developed method for complete characterization of atomic surface restructuring events. Our approach is broadly applicable to other multimetallic systems of interest and enables the previously impractical mechanistic investigation of restructuring dynamics.

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