Predicting human mobility has great significance in Location based Social Network applications,while it is challenging due to the impact of historical mobility patterns and current trajectories.Among these challenges,...Predicting human mobility has great significance in Location based Social Network applications,while it is challenging due to the impact of historical mobility patterns and current trajectories.Among these challenges,historical patterns tend to be crucial in the prediction task.However,it is difficult to capture complex patterns from long historical trajectories.Motivated by recent success of Convolutional Neural Network(CNN)-based methods,we propose a Union ConvGRU(UCG)Net,which can capture long short-term patterns of historical trajectories and sequential impact of current trajectories.Specifically,we first incorporate historical trajectories into hidden states by a shared-weight layer,and then utilize a 1D CNN to capture short-term pattern of hidden states.Next,an average pooling method is involved to generate separated hidden states of historical trajectories,on which we use a Fully Connected(FC)layer to capture longterm pattern subsequently.Finally,we use a Recurrent Neural Net-work(RNN)to predict future trajectories by integrating current trajectories and long short-term patterns.Experiments demonstrate that UCG Net performs best in comparison with neural network-based methods.展开更多
The world is experiencing the unprecedented time of a pandemic caused by the coronavirus disease(i.e.,COVID-19).As a countermeasure,contact tracing and social distancing are essential to prevent the transmission of th...The world is experiencing the unprecedented time of a pandemic caused by the coronavirus disease(i.e.,COVID-19).As a countermeasure,contact tracing and social distancing are essential to prevent the transmission of the virus,which can be achieved using indoor location analytics.Based on the indoor location analytics,the human mobility on a site can be monitored and planned to minimize human’s contact and enforce social distancing to contain the transmission of COVID-19.Given the indoor location data,the clustering can be applied to cluster spatial data,spatio-temporal data and movement behavior features for proximity detection or contact tracing applications.More specifically,we propose the Coherent Moving Cluster(CMC)algorithm for contact tracing,the density-based clustering(DBScan)algorithm for identification of hotspots and the trajectory clustering(TRACLUS)algorithm for clustering indoor trajectories.The feature extraction mechanism is then developed to extract useful and valuable features that can assist the proposed system to construct the network of users based on the similarity of the movement behaviors of the users.The network of users is used to model an optimization problem to manage the human mobility on a site.The objective function is formulated to minimize the probability of contact between the users and the optimization problem is solved using the proposed effective scheduling solution based on OR-Tools.The simulation results show that the proposed indoor location analytics system outperforms the existing clustering methods by about 30%in terms of accuracy of clustering trajectories.By adopting this system for human mobility management,the count of close contacts among the users within a confined area can be reduced by 80%in the scenario where all users are allowed to access the site.展开更多
Nowadays,the anticipation of parking-space demand is an instrumental service in order to reduce traffic congestion levels in urban spaces.The purpose of our work is to study,design and develop a parking-availability p...Nowadays,the anticipation of parking-space demand is an instrumental service in order to reduce traffic congestion levels in urban spaces.The purpose of our work is to study,design and develop a parking-availability predictor that extracts the knowledge from human mobility data,based on the anonymized human displacements of an urban area,and also from weather conditions.Most of the existing solutions for this prediction take as contextual data the current road-traffic state defined at very high temporal or spatial resolution.However,access to this type of fine-grained location data is usually quite limited due to several economic or privacy-related restrictions.To overcome this limitation,our proposal uses urban areas that are defined at very low spatial and temporal resolution.We conducted several experiments using three Artificial Neural Networks:Multilayer Perceptron,Gated Recurrent Units and bidirectional Long Short Term Memory networks and we tested their suitability using different combinations of inputs.Several metrics are provided for the sake of comparison within our study and between other studies.The solution has been evaluated in a real-world testbed in the city of Murcia(Spain)integrating an open human-mobility dataset showing high accuracy.A MAPE between 4%and 10%was reported in horizons of 1 to 3 h.展开更多
In the United States,the buildings sector consumes about 76%of electricity use and 40% of all primary energy use and associated greenhouse gas emissions.Occupant behavior has drawn increasing research interests due to...In the United States,the buildings sector consumes about 76%of electricity use and 40% of all primary energy use and associated greenhouse gas emissions.Occupant behavior has drawn increasing research interests due to its impacts on the building energy consumption.However,occupant behavior study at urban scale remains a challenge,and very limited studies have been conducted.As an effort to couple big data analysis with human mobility modeling,this study has explored urban scale human mobility utilizing three months Global Positioning System(GPS)data of 93,o00 users at Phoenix Metropolitan Area.This research extracted stay points from raw data,and identified users'home,work,and other locations by Density-Based Spatial Clustering algorithm.Then,daily mobility patterns were constructed using different types of locations.We propose a novel approach to predict urban scale daily human mobility patterns with 12-hour prediction horizon,using Long Short-Term Memory(LSTM)neural network model.Results shows the developed models achieved around 85%average accuracy and about 86%mean precision.The developed models can be further applied to analyze urban scale occupant behavior,building energy demand and flexibility,and contributed to urban planning.展开更多
COVID-19 has posed formidable challenges as a significant global health crisis.Its complexity stems from factors like viral contagiousness,population density,social behaviors,governmental regulations,and environmental...COVID-19 has posed formidable challenges as a significant global health crisis.Its complexity stems from factors like viral contagiousness,population density,social behaviors,governmental regulations,and environmental conditions,with interpersonal interactions and large-scale activities being particularly pivotal.To unravel these complexities,we used a modified SEIR epidemiological model to simulate various outbreak scenarios during the holiday season,incorporating both inter-regional and intra-regional human mobility effects into the parameterization scheme.In addition,evaluation metrics were used to evaluate the accuracy of the model simulation by comparing the congruence between simulated results and recorded confirmed cases.The findings suggested that intra-city mobility led to an average surge of 57.35%in confirmed cases of China,while inter-city mobility contributed to an average increase of 15.18%.In the simulation for Tianjin,China,a one-week delay in human mobility attenuated the peak number of cases by 34.47%and postponed the peak time by 6 days.The simulation for the United States revealed that human mobility played a more pronounced part in the outbreak,with a notable disparity in peak cases when mobility was considered.This study highlights that while inter-regional mobility acted as a trigger for the epidemic spread,the diffusion effect of intra-regional mobility was primarily responsible for the outbreak.We have a better understanding on how human mobility and infectious disease epidemics interact,and provide empirical evidence that could contribute to disease prevention and control measures.展开更多
Decarbonizing transport is one of the core tasks for achieving Net Zero targets,but the COVID-19 pandemic disrupts human mobility and the established transport development strategies.Although existing research has exp...Decarbonizing transport is one of the core tasks for achieving Net Zero targets,but the COVID-19 pandemic disrupts human mobility and the established transport development strategies.Although existing research has explored the relationship between virus transmission,human mobility,and restrictions policies,few have studied the responses of multimodal human mobility to the pandemic and their impacts on the achievement of decarbonizing transport.This paper employs 32 consecutive biweekly observations of mobile phone application data to understand the influences of the pandemics on multimodal human mobility from February 2020 to April 2021 in London.We here illustrate that multimodal travel behavior and traffic flows significant changed after the pandemic and related lockdowns,but the decline or recovery varies across different travel modes and lockdowns.The car mode has shown the most resilience throughout the pandemic,but the travel modes in the public transit sector were hit hard.Cycle and walk modes remained high at the beginning of the pandemic,but the trend did not continue as the pandemic developed and the season changed.Our findings suggest that the COVID-19 pandemic brought more challenges to travel mode shifting and the achievement of decarbonizing transport rather than opportunities.This analysis will assist transport authorities to optimize the established transport policies and to redistribute limited resources for accelerating the achievement of decarbonizing transport.展开更多
In this paper,we present a three-step methodological framework,including location identification,bias modification,and out-of-sample validation,so as to promote human mobility analysis with social media data.More spec...In this paper,we present a three-step methodological framework,including location identification,bias modification,and out-of-sample validation,so as to promote human mobility analysis with social media data.More specifically,we propose ways of identifying personal activity-specific places and commuting patterns in Beijing,China,based on Weibo(China’s Twitter)check-in records,as well as modifying sample bias of check-in data with population synthesis technique.An independent citywide travel logistic survey is used as the benchmark for validating the results.Obvious differences are discerned from Weibo users’and survey respondents’activity-mobility patterns,while there is a large variation of population representativeness between data from the two sources.After bias modification,the similarity coefficient between commuting distance distributions of Weibo data and survey observations increases substantially from 23% to 63%.Synthetic data proves to be a satisfactory costeffective alternative source of mobility information.The proposed framework can inform many applications related to human mobility,ranging from transportation,through urban planning to transport emission modeling.展开更多
The COVID-19 pandemic poses unprecedented challenges around the world.Many studies have applied mobility data to explore spatiotemporal trends over time,investigate associations with other variables,and predict or sim...The COVID-19 pandemic poses unprecedented challenges around the world.Many studies have applied mobility data to explore spatiotemporal trends over time,investigate associations with other variables,and predict or simulate the spread of COVID-19.Our objective was to provide a comprehensive overview of human mobility open data to guide researchers and policymakers in conducting data-driven evaluations and decision-making for the COVID-19 pandemic and other infectious disease outbreaks.We summarized the mobility data usage in COVID-19 studies by reviewing recent publications on COVID-19 and human mobility from a data-oriented perspective.We identified three major sources of mobility data:public transit systems,mobile operators,and mobile phone applications.Four approaches have been commonly used to estimate human mobility:public transit-based flow,social activity patterns,index-based mobility data,and social media-derived mobility data.We compared mobility datasets’characteristics by assessing data privacy,quality,space–time coverage,high-performance data storage and processing,and accessibility.We also present challenges and future directions of using mobility data.This review makes a pivotal contribution to understanding the use of and access to human mobility data in the COVID-19 pandemic and future disease outbreaks.展开更多
The effective modeling of urban growth is crucial for urban planning and analyzing the causes of land-use dynamics.As urbanization has slowed down in most megacities,improved urban growth modeling with minor changes h...The effective modeling of urban growth is crucial for urban planning and analyzing the causes of land-use dynamics.As urbanization has slowed down in most megacities,improved urban growth modeling with minor changes has become a crucial open issue for these cities.Most existing models are based on stationary factors and spatial proximity,which are unlikely to depict spatial connectivity between regions.This research attempts to leverage the power of real-world human mobility and consider intra-city spatial interaction as an imperative driver in the context of urban growth simulation.Specifically,the gravity model,which considers both the scale and distance effects of geographical locations within cities,is employed to characterize the connection between land areas using individual trajectory data from a macro perspective.It then becomes possible to integrate human mobility factors into a neural-network-based cellular automata(ANN-CA)for urban growth modeling in Beijing from 2013 to 2016.The results indicate that the proposed model outperforms traditional models in terms of the overall accuracy with a 0.60%improvement in Cohen’s Kappa coefficient and a 0.41%improvement in the figure of merit.In addition,the improvements are even more significant in districts with strong relationships with the central area of Beijing.For example,we find that the Kappa coefficients in three districts(Chaoyang,Daxing,and Shunyi)are considerably higher by more than 2.00%,suggesting the possible existence of a positive link between intense human interaction and urban growth.This paper provides valuable insights into how fine-grained human mobility data can be integrated into urban growth simulation,helping us to better understand the human-land relationship.展开更多
Massive spatio-temporal big data about human mobility have become increasingly available.Revealing underlying dynamic patterns from these data is essential for understanding people’s behavior and urban deployment.Spa...Massive spatio-temporal big data about human mobility have become increasingly available.Revealing underlying dynamic patterns from these data is essential for understanding people’s behavior and urban deployment.Spatio-temporal autocorrelation analysis is an exploratory approach to recognizing data distribution in space and time.The most widely used spatial autocorrelation measurements,such as Moran’s I and local indicators of spatial association(LISA),only apply to static data,so are powerless to spatio-temporal big data about human mobility.Thus,we proposed a new method by extending Moran’s I to measure the spatial autocorrelation of time series data.Then the method was applied to taxi ride data in Beijing,China to reveal the spatial pattern of collective human mobility.The result shows that there is strong positive spatio-temporal autocorrelation within the 5th Ring Road,weak negative spatio-temporal autocorrelation nearby the Sixth Ring Road,and almost no spatiotemporal autocorrelation between the roads.Local spatial patterns of taxi travel were also recognized.This method is useful for discovering underlying patterns from spatio-temporal big data to understand human mobility.展开更多
This study develops a holistic view of the novel coronavirus(COVID-19)spread worldwide through a spatial–temporal model with network dynamics.By using a unique human mobility dataset containing 547166 flights with a ...This study develops a holistic view of the novel coronavirus(COVID-19)spread worldwide through a spatial–temporal model with network dynamics.By using a unique human mobility dataset containing 547166 flights with a total capacity of 101455913 passengers from January 22 to April 24,2020,we analyze the epidemic correlations across 22 countries in six continents and particularly the changes in such correlations before and after implementing the international travel restriction policies targeting different countries.Results show that policymakers should move away from the previous practices that focus only on restricting hotspot areas with high infection rates.Instead,they should develop a new holistic view of global human mobility to impose the international movement restriction.The study further highlights potential correlations between international human mobility and focal countries’epidemic situations in the global network of COVID-19 pandemic.展开更多
This article looks at how population movements are addressed by the Sendai Framework for Disaster Risk Reduction 2015–2030(SFDRR), and highlights some of the potential implications of the SFDRR on disaster risk reduc...This article looks at how population movements are addressed by the Sendai Framework for Disaster Risk Reduction 2015–2030(SFDRR), and highlights some of the potential implications of the SFDRR on disaster risk reduction(DRR) and mobility management work. The article looks at the operational implications of the SFDRR text and covers issues of including migrants in DRR work;informing urban development about current and future mobility trends; managing relocations, evacuations, and displacement to prevent future risks and reduce existing ones; and preparing for and managing disaster-induced population movements to reduce the direct and indirect impacts of natural hazards. Overall, the references to human mobility within the SFDRR show an evolution in the way the issue is considered within global policy dialogues. Both the potential of population movements to produce risk and their role in strengthening the resilience of people and communities are now clearly recognized. This is an evolution of previously prevailing views of mobility as the consequence of disasters or as a driver of risk. While some implications of the DRR-mobility nexus might still be missing from DRR policy, population movements are now recognized as a key global risk dynamic.展开更多
Mobile Crowd Sensing(MCS)is an emerging paradigm that leverages sensor-equipped smart devices to collect data.The introduction of MCS also poses some challenges such as providing highquality data for upper layer MCS a...Mobile Crowd Sensing(MCS)is an emerging paradigm that leverages sensor-equipped smart devices to collect data.The introduction of MCS also poses some challenges such as providing highquality data for upper layer MCS applications,which requires adequate participants.However,recruiting enough participants to provide the sensing data for free is hard for the MCS platform under a limited budget,which may lead to a low coverage ratio of sensing area.This paper proposes a novel method to choose participants uniformly distributed in a specific sensing area based on the mobility patterns of mobile users.The method consists of two steps:(1)A second-order Markov chain is used to predict the next positions of users,and select users whose next places are in the target sensing area to form a candidate pool.(2)The Average Entropy(DAE)is proposed to measure the distribution of participants.The participant maximizing the DAE value of a specific sensing area with different granular sub-areas is chosen to maximize the coverage ratio of the sensing area.Experimental results show that the proposed method can maximize the coverage ratio of a sensing area under different partition granularities.展开更多
The process of urbanization is formed by regular movements of human beings. It yields different functional zones in a city, such as residential zone and commercial zone. Consequently, there exists a close connection b...The process of urbanization is formed by regular movements of human beings. It yields different functional zones in a city, such as residential zone and commercial zone. Consequently, there exists a close connection between the human mobility pattern and the city＇s zones. However, it is not easy to collect large-scale society-wide data that can precisely capture the underlying relations between the individual＇s movement and the regional functions. Hence, our knowledge for understanding the basic patterns of human mobility is still limited. In order to discover the functions of different regions in a city, we propose an affinity based method in this paper. The affinity is a recently introduced metric for measuring the correlation of two connecting node in a complex network. The proposed model groups different functional zones by measuring user＇s arrival/departure distribution via relative entropy. In addition to this, we also identify the intensity of each functional zone by taking kernel density estimation （KDE） method. In the end, some experiments are conducted to evaluate our method with a large-scale real-life dataset, which consists of 3 million cellphone users＇ records from a period of one month. Our findings on the interaction between the mobility pattern and the regional functions can capture the city dynamics efficiently and provide a valuable reference for urban planners.展开更多
The prevalence of mobile devices has spurred human mobility to be applied in mobile networking and communications by using network science, in which the temporal evolution of a network topology is of great importance ...The prevalence of mobile devices has spurred human mobility to be applied in mobile networking and communications by using network science, in which the temporal evolution of a network topology is of great importance for protocol design and performance analysis. This paper focuses on link generation in a temporal evolution network. Based on observations revealing the strong correlation between the connection patterns of different time periods, a link generation potential based on historical connections is proposed in this paper, aiming to provide a method for making topological predictions with less randomness. Using MIT Reality dataset, an evaluation of the accuracy of the proposed method was conducted. The experimental results demonstrate the proposal's adequacy in terms of its accuracy.展开更多
This paper designs a mechanical swing of placementing mobile phone, which is inspired by the mechanical watch automatic winding process. The use of the kinetic energy generated by human body motion drives the wheel sw...This paper designs a mechanical swing of placementing mobile phone, which is inspired by the mechanical watch automatic winding process. The use of the kinetic energy generated by human body motion drives the wheel swing and the generator, it can carry out mobile phone additional charge through the electronic components rectifier and DC/DC converter regulator, the use of human motion and light energy can extend a fixed charge mobile phone standby time. The human motion power uses electromagnetic coupling technique and collects energy by using foot swing, solar power generation uses DSP chip in TMS320F28927 control a plurality of charging circuit, inverter circuit and solar maximum power point tracking by sampling and multiple output PWM wave. Finally, charging process has the basic constant current process discovered by device testing, the design of human motion and light energy mobile phone charger can satisfy the need of mobile phone rechargeable lithium batteries.展开更多
The increasing availability of data in the urban context(e.g.,mobile phone,smart card and social media data)allows us to study urban dynamics at much finer temporal resolutions(e.g.,diurnal urban dynamics).Mobile phon...The increasing availability of data in the urban context(e.g.,mobile phone,smart card and social media data)allows us to study urban dynamics at much finer temporal resolutions(e.g.,diurnal urban dynamics).Mobile phone data,for instance,are found to be a useful data source for extracting diurnal human mobility patterns and for understanding urban dynamics.While previous studies often use call detail record(CDR)data,this study deploys aggregated network-driven mobile phone data that may reveal human mobility patterns more comprehensively and can mitigate some of the privacy concerns raised by mobile phone data usage.We first propose an analytical framework for characterizing and classifying urban areas based on their temporal activity patterns extracted from mobile phone data.Specifically,urban areas’diurnal spatiotemporal signatures of human mobility patterns are obtained through longitudinal mobile phone data.Urban areas are then classified based on the obtained signatures.The classification provides insights into city planning and development.Using the proposed framework,a case study was implemented in the city of Wuhu,China to understand its urban dynamics.The empirical study suggests that human activities in the city of Wuhu are highly concentrated at the Traffic Analysis Zone(TAZ)level.This large portion of local activities suggests that development and planning strategies that are different from those used by metropolitan Chinese cities should be applied in the city of Wuhu.This article concludes with discussions on several common challenges associated with using network-driven mobile phone data,which should be addressed in future studies.展开更多
Bicycle sharing system has emerged as a new mode of transportation in many big cities over the past decade.Since the large number of bicycle stations distribute widely in the city,it is difficult to identify their uni...Bicycle sharing system has emerged as a new mode of transportation in many big cities over the past decade.Since the large number of bicycle stations distribute widely in the city,it is difficult to identify their unique attributes and characteristics directly.Oriented to the real bicycle hire dataset in Hangzhou,China,the clustering analysis for the bicycle stations based on the temporal flow data was carried out firstly.Then,based on the spatial distribution and temporal attributes of calculated clusters,visual diagram and map were used to vividly analyze the bicycle hire behavior related to station category and study the travel rules of citizens.The experimental results demonstrate the relation between human mobility,the time of day,day of week and the station location.展开更多
Human mobility survey data usually suffer from a lack of resources for validation.Epidemiological survey records,which are released to the public as a containment measure by local authorities,provide place visitation ...Human mobility survey data usually suffer from a lack of resources for validation.Epidemiological survey records,which are released to the public as a containment measure by local authorities,provide place visitation details validated by the authority.This study collected and analyzed the epidemiological survey reports published by local governments in the Chinese mainland,between January 2020 and November 2021.To reveal the mobility patterns during the COVID-19 pandemic across the urban-rural gradient in China’s mainland,we derived key mobility indicators from the epidemiological survey data from rural to megacities.We then applied exploratory factor analysis to identify latent factors that affected people’s mobility.We found that the pandemic poses varying impacts across the urban-rural gradient in the Chinese mainland,and the mobility patterns of middle and small cities are more influenced.Our results also showed that the pandemic did not enlarge gender gap in people’s mobility,as gender was not a significant driving factor for explaining people’s quantity of out-of-home activities as well as extent of life space,while age group and city levels were significant.Overall,we argue that the epidemiological survey data are valuable data sources for daily mobility modeling,especially for relevant studies to understand human mobility patterns during the pandemic.展开更多
A novel coronavirus emerged in late 2019,named as the coronavirus disease 2019(COVID-19)by the World Health Organization(WHO).This study was originally conducted in January 2020 to estimate the potential risk and geog...A novel coronavirus emerged in late 2019,named as the coronavirus disease 2019(COVID-19)by the World Health Organization(WHO).This study was originally conducted in January 2020 to estimate the potential risk and geographic range of COVID-19 spread at the early stage of the transmission.A series of connectivity and risk analyses based on domestic and international travel networks were conducted using historical aggregated mobile phone data and air passenger itinerary data.We found that the cordon sanitaire of the primary city was likely to have occurred during the latter stages of peak population numbers leaving the city,with travellers departing into neighbouring cities and other megacities in China.We estimated that there were 59,912 international air passengers,of which 834(95%uncertainty interval:478–1,349)had COVID-19 infection,with a strong correlation seen between the predicted risks of importation and the number of imported cases found.Given the limited understanding of emerging infectious diseases in the very early stages of outbreaks,our approaches and findings in assessing travel patterns and risk of transmission can help guide public health preparedness and intervention design for new COVID-19 waves caused by variants of concern and future pandemics to effectively limit transmission beyond its initial extent.展开更多
基金This research was supported in part by National Key Research and Development Plan Key Special Projects under Grant No.2018YFB2100303Key Research and Development Plan Project of Shandong Province under Grant No.2016GGX101032+2 种基金Program for Innovative Postdoctoral Talents in Shandong Province under Grant No.40618030001National Natural Science Foundation of China under Grant No.61802216Postdoctoral Science Foundation of China under Grant No.2018M642613.
文摘Predicting human mobility has great significance in Location based Social Network applications,while it is challenging due to the impact of historical mobility patterns and current trajectories.Among these challenges,historical patterns tend to be crucial in the prediction task.However,it is difficult to capture complex patterns from long historical trajectories.Motivated by recent success of Convolutional Neural Network(CNN)-based methods,we propose a Union ConvGRU(UCG)Net,which can capture long short-term patterns of historical trajectories and sequential impact of current trajectories.Specifically,we first incorporate historical trajectories into hidden states by a shared-weight layer,and then utilize a 1D CNN to capture short-term pattern of hidden states.Next,an average pooling method is involved to generate separated hidden states of historical trajectories,on which we use a Fully Connected(FC)layer to capture longterm pattern subsequently.Finally,we use a Recurrent Neural Net-work(RNN)to predict future trajectories by integrating current trajectories and long short-term patterns.Experiments demonstrate that UCG Net performs best in comparison with neural network-based methods.
基金This research was funded by Ministry of Education Malaysia,Grant Number FRGS/1/2019/ICT02/MMU/02/1in part by Monash Malaysia,School of Information Technology(SIT)Collaborative Research Seed Grants 2020.
文摘The world is experiencing the unprecedented time of a pandemic caused by the coronavirus disease(i.e.,COVID-19).As a countermeasure,contact tracing and social distancing are essential to prevent the transmission of the virus,which can be achieved using indoor location analytics.Based on the indoor location analytics,the human mobility on a site can be monitored and planned to minimize human’s contact and enforce social distancing to contain the transmission of COVID-19.Given the indoor location data,the clustering can be applied to cluster spatial data,spatio-temporal data and movement behavior features for proximity detection or contact tracing applications.More specifically,we propose the Coherent Moving Cluster(CMC)algorithm for contact tracing,the density-based clustering(DBScan)algorithm for identification of hotspots and the trajectory clustering(TRACLUS)algorithm for clustering indoor trajectories.The feature extraction mechanism is then developed to extract useful and valuable features that can assist the proposed system to construct the network of users based on the similarity of the movement behaviors of the users.The network of users is used to model an optimization problem to manage the human mobility on a site.The objective function is formulated to minimize the probability of contact between the users and the optimization problem is solved using the proposed effective scheduling solution based on OR-Tools.The simulation results show that the proposed indoor location analytics system outperforms the existing clustering methods by about 30%in terms of accuracy of clustering trajectories.By adopting this system for human mobility management,the count of close contacts among the users within a confined area can be reduced by 80%in the scenario where all users are allowed to access the site.
基金This work has been sponsored by UMU-CAMPUS LIVING LAB EQC2019-006176-P funded by ERDFfundsby the European Commission through the H2020PHOENIX(grant agreement 893079)andDEMETER(grant agreement 857202)EU ProjectsIt was also co-financed by the European Social Fund(ESF)and the Youth European Initiative(YEI)under the Spanish Seneca Foundation(CARM).
文摘Nowadays,the anticipation of parking-space demand is an instrumental service in order to reduce traffic congestion levels in urban spaces.The purpose of our work is to study,design and develop a parking-availability predictor that extracts the knowledge from human mobility data,based on the anonymized human displacements of an urban area,and also from weather conditions.Most of the existing solutions for this prediction take as contextual data the current road-traffic state defined at very high temporal or spatial resolution.However,access to this type of fine-grained location data is usually quite limited due to several economic or privacy-related restrictions.To overcome this limitation,our proposal uses urban areas that are defined at very low spatial and temporal resolution.We conducted several experiments using three Artificial Neural Networks:Multilayer Perceptron,Gated Recurrent Units and bidirectional Long Short Term Memory networks and we tested their suitability using different combinations of inputs.Several metrics are provided for the sake of comparison within our study and between other studies.The solution has been evaluated in a real-world testbed in the city of Murcia(Spain)integrating an open human-mobility dataset showing high accuracy.A MAPE between 4%and 10%was reported in horizons of 1 to 3 h.
基金supported by the U.S.National Science Foundation(Award No.1949372 and No.2125775)in part supported through computational resources provided by Syracuse University.
文摘In the United States,the buildings sector consumes about 76%of electricity use and 40% of all primary energy use and associated greenhouse gas emissions.Occupant behavior has drawn increasing research interests due to its impacts on the building energy consumption.However,occupant behavior study at urban scale remains a challenge,and very limited studies have been conducted.As an effort to couple big data analysis with human mobility modeling,this study has explored urban scale human mobility utilizing three months Global Positioning System(GPS)data of 93,o00 users at Phoenix Metropolitan Area.This research extracted stay points from raw data,and identified users'home,work,and other locations by Density-Based Spatial Clustering algorithm.Then,daily mobility patterns were constructed using different types of locations.We propose a novel approach to predict urban scale daily human mobility patterns with 12-hour prediction horizon,using Long Short-Term Memory(LSTM)neural network model.Results shows the developed models achieved around 85%average accuracy and about 86%mean precision.The developed models can be further applied to analyze urban scale occupant behavior,building energy demand and flexibility,and contributed to urban planning.
基金supported by the Frontier of Interdisciplinary Research on Monitoring and Prediction of Pathogenic Microorganisms in the Atmosphere (XK2022DXC005,L2224041)the Self-supporting Program of Guangzhou Laboratory (SRPG22-007)+1 种基金the Gansu Province Intellectual Property Program (Oriented Organization)Project (22ZSCQD02)the Fundamental Research Funds for the Central Universities (lzujbky-2022-kb10).
文摘COVID-19 has posed formidable challenges as a significant global health crisis.Its complexity stems from factors like viral contagiousness,population density,social behaviors,governmental regulations,and environmental conditions,with interpersonal interactions and large-scale activities being particularly pivotal.To unravel these complexities,we used a modified SEIR epidemiological model to simulate various outbreak scenarios during the holiday season,incorporating both inter-regional and intra-regional human mobility effects into the parameterization scheme.In addition,evaluation metrics were used to evaluate the accuracy of the model simulation by comparing the congruence between simulated results and recorded confirmed cases.The findings suggested that intra-city mobility led to an average surge of 57.35%in confirmed cases of China,while inter-city mobility contributed to an average increase of 15.18%.In the simulation for Tianjin,China,a one-week delay in human mobility attenuated the peak number of cases by 34.47%and postponed the peak time by 6 days.The simulation for the United States revealed that human mobility played a more pronounced part in the outbreak,with a notable disparity in peak cases when mobility was considered.This study highlights that while inter-regional mobility acted as a trigger for the epidemic spread,the diffusion effect of intra-regional mobility was primarily responsible for the outbreak.We have a better understanding on how human mobility and infectious disease epidemics interact,and provide empirical evidence that could contribute to disease prevention and control measures.
文摘Decarbonizing transport is one of the core tasks for achieving Net Zero targets,but the COVID-19 pandemic disrupts human mobility and the established transport development strategies.Although existing research has explored the relationship between virus transmission,human mobility,and restrictions policies,few have studied the responses of multimodal human mobility to the pandemic and their impacts on the achievement of decarbonizing transport.This paper employs 32 consecutive biweekly observations of mobile phone application data to understand the influences of the pandemics on multimodal human mobility from February 2020 to April 2021 in London.We here illustrate that multimodal travel behavior and traffic flows significant changed after the pandemic and related lockdowns,but the decline or recovery varies across different travel modes and lockdowns.The car mode has shown the most resilience throughout the pandemic,but the travel modes in the public transit sector were hit hard.Cycle and walk modes remained high at the beginning of the pandemic,but the trend did not continue as the pandemic developed and the season changed.Our findings suggest that the COVID-19 pandemic brought more challenges to travel mode shifting and the achievement of decarbonizing transport rather than opportunities.This analysis will assist transport authorities to optimize the established transport policies and to redistribute limited resources for accelerating the achievement of decarbonizing transport.
文摘In this paper,we present a three-step methodological framework,including location identification,bias modification,and out-of-sample validation,so as to promote human mobility analysis with social media data.More specifically,we propose ways of identifying personal activity-specific places and commuting patterns in Beijing,China,based on Weibo(China’s Twitter)check-in records,as well as modifying sample bias of check-in data with population synthesis technique.An independent citywide travel logistic survey is used as the benchmark for validating the results.Obvious differences are discerned from Weibo users’and survey respondents’activity-mobility patterns,while there is a large variation of population representativeness between data from the two sources.After bias modification,the similarity coefficient between commuting distance distributions of Weibo data and survey observations increases substantially from 23% to 63%.Synthetic data proves to be a satisfactory costeffective alternative source of mobility information.The proposed framework can inform many applications related to human mobility,ranging from transportation,through urban planning to transport emission modeling.
基金supported by the NSF[National Science Foundation]under grant 1841403,2027540,and 2028791.
文摘The COVID-19 pandemic poses unprecedented challenges around the world.Many studies have applied mobility data to explore spatiotemporal trends over time,investigate associations with other variables,and predict or simulate the spread of COVID-19.Our objective was to provide a comprehensive overview of human mobility open data to guide researchers and policymakers in conducting data-driven evaluations and decision-making for the COVID-19 pandemic and other infectious disease outbreaks.We summarized the mobility data usage in COVID-19 studies by reviewing recent publications on COVID-19 and human mobility from a data-oriented perspective.We identified three major sources of mobility data:public transit systems,mobile operators,and mobile phone applications.Four approaches have been commonly used to estimate human mobility:public transit-based flow,social activity patterns,index-based mobility data,and social media-derived mobility data.We compared mobility datasets’characteristics by assessing data privacy,quality,space–time coverage,high-performance data storage and processing,and accessibility.We also present challenges and future directions of using mobility data.This review makes a pivotal contribution to understanding the use of and access to human mobility data in the COVID-19 pandemic and future disease outbreaks.
基金Wuhan University“351”Talent Plan Teaching Position ProjectGuangdong-Hong Kong-Macao Joint Laboratory Program of the 2020 Guangdong New Innovative Strategic Research Fund from Guangdong Science and Technology Department,No.2020B1212030009。
文摘The effective modeling of urban growth is crucial for urban planning and analyzing the causes of land-use dynamics.As urbanization has slowed down in most megacities,improved urban growth modeling with minor changes has become a crucial open issue for these cities.Most existing models are based on stationary factors and spatial proximity,which are unlikely to depict spatial connectivity between regions.This research attempts to leverage the power of real-world human mobility and consider intra-city spatial interaction as an imperative driver in the context of urban growth simulation.Specifically,the gravity model,which considers both the scale and distance effects of geographical locations within cities,is employed to characterize the connection between land areas using individual trajectory data from a macro perspective.It then becomes possible to integrate human mobility factors into a neural-network-based cellular automata(ANN-CA)for urban growth modeling in Beijing from 2013 to 2016.The results indicate that the proposed model outperforms traditional models in terms of the overall accuracy with a 0.60%improvement in Cohen’s Kappa coefficient and a 0.41%improvement in the figure of merit.In addition,the improvements are even more significant in districts with strong relationships with the central area of Beijing.For example,we find that the Kappa coefficients in three districts(Chaoyang,Daxing,and Shunyi)are considerably higher by more than 2.00%,suggesting the possible existence of a positive link between intense human interaction and urban growth.This paper provides valuable insights into how fine-grained human mobility data can be integrated into urban growth simulation,helping us to better understand the human-land relationship.
基金This work is funded by the National Natural Science Foundation of China[grant numbers 41830645 and 41625003].
文摘Massive spatio-temporal big data about human mobility have become increasingly available.Revealing underlying dynamic patterns from these data is essential for understanding people’s behavior and urban deployment.Spatio-temporal autocorrelation analysis is an exploratory approach to recognizing data distribution in space and time.The most widely used spatial autocorrelation measurements,such as Moran’s I and local indicators of spatial association(LISA),only apply to static data,so are powerless to spatio-temporal big data about human mobility.Thus,we proposed a new method by extending Moran’s I to measure the spatial autocorrelation of time series data.Then the method was applied to taxi ride data in Beijing,China to reveal the spatial pattern of collective human mobility.The result shows that there is strong positive spatio-temporal autocorrelation within the 5th Ring Road,weak negative spatio-temporal autocorrelation nearby the Sixth Ring Road,and almost no spatiotemporal autocorrelation between the roads.Local spatial patterns of taxi travel were also recognized.This method is useful for discovering underlying patterns from spatio-temporal big data to understand human mobility.
基金This work was supported by the National Natural Science Foundation of China(Nos.91846302,71720107003,and 71973107).
文摘This study develops a holistic view of the novel coronavirus(COVID-19)spread worldwide through a spatial–temporal model with network dynamics.By using a unique human mobility dataset containing 547166 flights with a total capacity of 101455913 passengers from January 22 to April 24,2020,we analyze the epidemic correlations across 22 countries in six continents and particularly the changes in such correlations before and after implementing the international travel restriction policies targeting different countries.Results show that policymakers should move away from the previous practices that focus only on restricting hotspot areas with high infection rates.Instead,they should develop a new holistic view of global human mobility to impose the international movement restriction.The study further highlights potential correlations between international human mobility and focal countries’epidemic situations in the global network of COVID-19 pandemic.
文摘This article looks at how population movements are addressed by the Sendai Framework for Disaster Risk Reduction 2015–2030(SFDRR), and highlights some of the potential implications of the SFDRR on disaster risk reduction(DRR) and mobility management work. The article looks at the operational implications of the SFDRR text and covers issues of including migrants in DRR work;informing urban development about current and future mobility trends; managing relocations, evacuations, and displacement to prevent future risks and reduce existing ones; and preparing for and managing disaster-induced population movements to reduce the direct and indirect impacts of natural hazards. Overall, the references to human mobility within the SFDRR show an evolution in the way the issue is considered within global policy dialogues. Both the potential of population movements to produce risk and their role in strengthening the resilience of people and communities are now clearly recognized. This is an evolution of previously prevailing views of mobility as the consequence of disasters or as a driver of risk. While some implications of the DRR-mobility nexus might still be missing from DRR policy, population movements are now recognized as a key global risk dynamic.
基金supported by the Open Foundation of State key Laboratory of Networking and Switching Technology(Beijing University of Posts and Telecommunications)(SKLNST-2021-1-18)the General Program of Natural Science Foundation of Chongqing(cstc2020jcyj-msxmX1021)+1 种基金the Science and Technology Research Program of Chongqing Municipal Education Commission(KJZD-K202000602)Chongqing graduate research and innovation project(CYS22478).
文摘Mobile Crowd Sensing(MCS)is an emerging paradigm that leverages sensor-equipped smart devices to collect data.The introduction of MCS also poses some challenges such as providing highquality data for upper layer MCS applications,which requires adequate participants.However,recruiting enough participants to provide the sensing data for free is hard for the MCS platform under a limited budget,which may lead to a low coverage ratio of sensing area.This paper proposes a novel method to choose participants uniformly distributed in a specific sensing area based on the mobility patterns of mobile users.The method consists of two steps:(1)A second-order Markov chain is used to predict the next positions of users,and select users whose next places are in the target sensing area to form a candidate pool.(2)The Average Entropy(DAE)is proposed to measure the distribution of participants.The participant maximizing the DAE value of a specific sensing area with different granular sub-areas is chosen to maximize the coverage ratio of the sensing area.Experimental results show that the proposed method can maximize the coverage ratio of a sensing area under different partition granularities.
基金supported by the National Nature Science Foundation of China(615111300816147104861273217)
文摘The process of urbanization is formed by regular movements of human beings. It yields different functional zones in a city, such as residential zone and commercial zone. Consequently, there exists a close connection between the human mobility pattern and the city＇s zones. However, it is not easy to collect large-scale society-wide data that can precisely capture the underlying relations between the individual＇s movement and the regional functions. Hence, our knowledge for understanding the basic patterns of human mobility is still limited. In order to discover the functions of different regions in a city, we propose an affinity based method in this paper. The affinity is a recently introduced metric for measuring the correlation of two connecting node in a complex network. The proposed model groups different functional zones by measuring user＇s arrival/departure distribution via relative entropy. In addition to this, we also identify the intensity of each functional zone by taking kernel density estimation （KDE） method. In the end, some experiments are conducted to evaluate our method with a large-scale real-life dataset, which consists of 3 million cellphone users＇ records from a period of one month. Our findings on the interaction between the mobility pattern and the regional functions can capture the city dynamics efficiently and provide a valuable reference for urban planners.
基金supported by the National Natural Science Foundation of China(Grant No.61300183)the National Science Fund for Distinguished Young Scholars in China(Grant No.61425012)
文摘The prevalence of mobile devices has spurred human mobility to be applied in mobile networking and communications by using network science, in which the temporal evolution of a network topology is of great importance for protocol design and performance analysis. This paper focuses on link generation in a temporal evolution network. Based on observations revealing the strong correlation between the connection patterns of different time periods, a link generation potential based on historical connections is proposed in this paper, aiming to provide a method for making topological predictions with less randomness. Using MIT Reality dataset, an evaluation of the accuracy of the proposed method was conducted. The experimental results demonstrate the proposal's adequacy in terms of its accuracy.
文摘This paper designs a mechanical swing of placementing mobile phone, which is inspired by the mechanical watch automatic winding process. The use of the kinetic energy generated by human body motion drives the wheel swing and the generator, it can carry out mobile phone additional charge through the electronic components rectifier and DC/DC converter regulator, the use of human motion and light energy can extend a fixed charge mobile phone standby time. The human motion power uses electromagnetic coupling technique and collects energy by using foot swing, solar power generation uses DSP chip in TMS320F28927 control a plurality of charging circuit, inverter circuit and solar maximum power point tracking by sampling and multiple output PWM wave. Finally, charging process has the basic constant current process discovered by device testing, the design of human motion and light energy mobile phone charger can satisfy the need of mobile phone rechargeable lithium batteries.
基金Under the auspices of the National Natural Science Foundation of China(No.41571146)China Postdoctoral Science Foundation(No.2019M651784)。
文摘The increasing availability of data in the urban context(e.g.,mobile phone,smart card and social media data)allows us to study urban dynamics at much finer temporal resolutions(e.g.,diurnal urban dynamics).Mobile phone data,for instance,are found to be a useful data source for extracting diurnal human mobility patterns and for understanding urban dynamics.While previous studies often use call detail record(CDR)data,this study deploys aggregated network-driven mobile phone data that may reveal human mobility patterns more comprehensively and can mitigate some of the privacy concerns raised by mobile phone data usage.We first propose an analytical framework for characterizing and classifying urban areas based on their temporal activity patterns extracted from mobile phone data.Specifically,urban areas’diurnal spatiotemporal signatures of human mobility patterns are obtained through longitudinal mobile phone data.Urban areas are then classified based on the obtained signatures.The classification provides insights into city planning and development.Using the proposed framework,a case study was implemented in the city of Wuhu,China to understand its urban dynamics.The empirical study suggests that human activities in the city of Wuhu are highly concentrated at the Traffic Analysis Zone(TAZ)level.This large portion of local activities suggests that development and planning strategies that are different from those used by metropolitan Chinese cities should be applied in the city of Wuhu.This article concludes with discussions on several common challenges associated with using network-driven mobile phone data,which should be addressed in future studies.
基金the Public Projects of Zhejiang Province,China(Nos.2016C33110,2015C33067)National Natural Science Foundations of China(Nos.61602141,61473108,61402141)
文摘Bicycle sharing system has emerged as a new mode of transportation in many big cities over the past decade.Since the large number of bicycle stations distribute widely in the city,it is difficult to identify their unique attributes and characteristics directly.Oriented to the real bicycle hire dataset in Hangzhou,China,the clustering analysis for the bicycle stations based on the temporal flow data was carried out firstly.Then,based on the spatial distribution and temporal attributes of calculated clusters,visual diagram and map were used to vividly analyze the bicycle hire behavior related to station category and study the travel rules of citizens.The experimental results demonstrate the relation between human mobility,the time of day,day of week and the station location.
基金supported by the Central China Normal University startup fund[grant numbers 3110122212631101222127].
文摘Human mobility survey data usually suffer from a lack of resources for validation.Epidemiological survey records,which are released to the public as a containment measure by local authorities,provide place visitation details validated by the authority.This study collected and analyzed the epidemiological survey reports published by local governments in the Chinese mainland,between January 2020 and November 2021.To reveal the mobility patterns during the COVID-19 pandemic across the urban-rural gradient in China’s mainland,we derived key mobility indicators from the epidemiological survey data from rural to megacities.We then applied exploratory factor analysis to identify latent factors that affected people’s mobility.We found that the pandemic poses varying impacts across the urban-rural gradient in the Chinese mainland,and the mobility patterns of middle and small cities are more influenced.Our results also showed that the pandemic did not enlarge gender gap in people’s mobility,as gender was not a significant driving factor for explaining people’s quantity of out-of-home activities as well as extent of life space,while age group and city levels were significant.Overall,we argue that the epidemiological survey data are valuable data sources for daily mobility modeling,especially for relevant studies to understand human mobility patterns during the pandemic.
基金supported by the grants from the Bill&Melinda Gates Foundation(Grant Nos.:INV-024911 and OPP1134076)the European Union Horizon 2020(Grant No.:MOOD 874850)+8 种基金the National Natural Science Fund of China(Grant Nos.:81773498,71771213 and 91846301)National Science and Technology Major Project of China(Grant No.:2016ZX10004222-009)Program of Shanghai Academic/Technology Research Leader(Grant No.:18XD1400300)Hunan Science and Technology Plan Project(Grant Nos.:2017RS3040 and 2018JJ1034)supported by funding from the Bill&Melinda Gates Foundation(Grant Nos.:OPP1106427,OPP1032350,OPP1134076,and OPP1094793)the Clinton Health Access Initiative,the UK Department for International Development(DFID)and the Wellcome Trust(Grant Nos.:106866/Z/15/Z and 204613/Z/16/Z)supported by funding from the National Natural Science Fund for Distinguished Young Scholars of China(Grant No.:81525023)Program of Shanghai Academic/Technology Research Leader(Grant No.:18XD1400300)the United States National Institutes of Health(Comprehensive International Program for Research on AIDS grant U19 AI51915).
文摘A novel coronavirus emerged in late 2019,named as the coronavirus disease 2019(COVID-19)by the World Health Organization(WHO).This study was originally conducted in January 2020 to estimate the potential risk and geographic range of COVID-19 spread at the early stage of the transmission.A series of connectivity and risk analyses based on domestic and international travel networks were conducted using historical aggregated mobile phone data and air passenger itinerary data.We found that the cordon sanitaire of the primary city was likely to have occurred during the latter stages of peak population numbers leaving the city,with travellers departing into neighbouring cities and other megacities in China.We estimated that there were 59,912 international air passengers,of which 834(95%uncertainty interval:478–1,349)had COVID-19 infection,with a strong correlation seen between the predicted risks of importation and the number of imported cases found.Given the limited understanding of emerging infectious diseases in the very early stages of outbreaks,our approaches and findings in assessing travel patterns and risk of transmission can help guide public health preparedness and intervention design for new COVID-19 waves caused by variants of concern and future pandemics to effectively limit transmission beyond its initial extent.
