Reliable and up-to-date geospatial data plays a fundamental role in Sustainable Development Goals(SDGs)monitoring.Aiming to providing such geospa-tial data,numerous algorithms,solutions and frame-works have been devel...Reliable and up-to-date geospatial data plays a fundamental role in Sustainable Development Goals(SDGs)monitoring.Aiming to providing such geospa-tial data,numerous algorithms,solutions and frame-works have been developed in recent years.A mong oth-ers,A rtificial Intelligence(AI)based techniques have been widely used for the tasks of processing geospatial data.Nowadays,this topic is blooming so fast and to a vast extent in the field of Geomatics that a new subdo-main seems to arise,namely GeoAI[1-2].Even for a very quick and brief glance inthe Internet,people can find a lot of applications,projects,blogs and research articles about GeoAI,w hereas new approaches to GeoAI have been proposed and tested.展开更多
Smart card-automated fare collection systems now routinely record large volumes of data comprising the origins and destinations of travelers.Processing and analyzing these data open new opportunities in urban modeling...Smart card-automated fare collection systems now routinely record large volumes of data comprising the origins and destinations of travelers.Processing and analyzing these data open new opportunities in urban modeling and travel behavior research.This study seeks to develop an accurate framework for the study of urban mobility from smart card data by developing a heuristic primary location model to identify the home and work locations.The model uses journey counts as an indicator of usage regularity,visit-frequency to identify activity locations for regular commuters,and stay-time for the classification of work and home locations and activities.London is taken as a case study,and the model results were validated against survey data from the London Travel Demand Survey and volunteer survey.Results demonstrate that the proposed model is able to detect meaningful home and work places with high precision.This study offers a new and cost-effective approach to travel behavior and demand research.展开更多
SpacetimeAI and GeoAI are currently hot topics,applying the latest algorithms in computer science,such as deep learning,to spatiotemporal data.Although deep learning algorithms have been successfully applied to raster...SpacetimeAI and GeoAI are currently hot topics,applying the latest algorithms in computer science,such as deep learning,to spatiotemporal data.Although deep learning algorithms have been successfully applied to raster data due to their natural applicability to image processing,their applications in other spatial and space-time data types are still immature.This paper sets up the proposition of using a network(&graph)-based framework as a generic spatial structure to present space-time processes that are usually represented by the points,polylines,and polygons.We illustrate network and graph-based SpaceTimeAI,from graph-based deep learning for prediction,to space-time clustering and optimisation.These applications demonstrate the advantages of network(graph)-based SpacetimeAI in the fields of transport&mobility,crime&policing,and public health.展开更多
Predicting trip purpose from comprehensive and continuous smart card data is beneficial for transport and city planners in investigating travel behaviors and urban mobility.Here,we propose a framework,ActivityNET,usin...Predicting trip purpose from comprehensive and continuous smart card data is beneficial for transport and city planners in investigating travel behaviors and urban mobility.Here,we propose a framework,ActivityNET,using Machine Learning(ML)algorithms to predict passengers’trip purpose from Smart Card(SC)data and Points-of-Interest(POIs)data.The feasibility of the framework is demonstrated in two phases.Phase I focuses on extracting activities from individuals’daily travel patterns from smart card data and combining them with POIs using the proposed“activity-POIs consolidation algorithm”.Phase II feeds the extracted features into an Artificial Neural Network(ANN)with multiple scenarios and predicts trip purpose under primary activities(home and work)and secondary activities(entertainment,eating,shopping,child drop-offs/pick-ups and part-time work)with high accuracy.As a case study,the proposed ActivityNET framework is applied in Greater London and illustrates a robust competence to predict trip purpose.The promising outcomes demonstrate that the cost-effective framework offers high predictive accuracy and valuable insights into transport planning.展开更多
The coronavirus pandemic that started in 2019 has had wide-ranging impacts on many aspects of people’s daily lives.At the peak of the outbreak,lockdown measures and social distancing changed the ways in which cities ...The coronavirus pandemic that started in 2019 has had wide-ranging impacts on many aspects of people’s daily lives.At the peak of the outbreak,lockdown measures and social distancing changed the ways in which cities function.In particular,they had profound impacts on urban transportation systems,with public transport being shut down in many cities.Bike share systems(BSS)were widely reported as having experienced an increase in demand during the early stages of the pandemic before returning to pre-pandemic levels.However,the studies published to date focus mainly on the first year of the pandemic,when various waves saw continual relaxing and reintroductions of restrictions.Therefore,they fall short of exploring the role of BSS as we move to the post-pandemic period.To address this gap,this study uses origin-destination(O-D)flow data from London’s Santander Cycle Hire Scheme from 2019-2021 to analyze the changing use of BSS throughout the first two years of the pandemic,from lockdown to recovery.A Gaussian mixture model(GMM)is used to cluster 2019 BSS trips into three distinct clusters based on their duration and distance.The clusters are used as a reference from which to measure spatial and temporal change in 2020 and 2021.In agreement with previous research,BSS usage was found to have declined by nearly 30%during the first lockdown.Usage then saw a sharp increase as restrictions were lifted,characterized by longer,less direct trips throughout the afternoon rather than typical peak commuting trips.Although the aggregate number of BSS trips appeared to return to normal by October 2020,this was against the backdrop of continuing restrictions on international travel and work from home orders.The period between July and December 2021 was the first period that all government restrictions were lifted.During this time,BSS trips reached higher levels than in 2019.Spatio-temporal analysis indicates a shift away from the traditional morning and evening peak to a more diffuse pattern of working hours.The results indicate that the pandemic may have had sustained impacts on travel behavior,leading to a“new normal”that reflects different ways of working.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
1.Introduction The COVID-19 pandemic has dramatically reshaped human mobility at global,national,regional,and individual levels,as evidenced by many studies(Chang et al.2021;Cheng et al.2022;Chinazzi et al.2020;Hou et...1.Introduction The COVID-19 pandemic has dramatically reshaped human mobility at global,national,regional,and individual levels,as evidenced by many studies(Chang et al.2021;Cheng et al.2022;Chinazzi et al.2020;Hou et al.2021;Santana et al.2023;Xiong et al.2020).Governments around the world have implemented containment measures such as lockdowns,travel restrictions,border closures,public transport reductions,and self-isolation for vulnerable groups.These interventions have led to effects that vary by location,timing,travel modes,and demographic characteristics.展开更多
Landslides are one of the most destructive natural hazards;they can drastically alter landscape morphology,destroy man-made struc-tures,and endanger people’s life.Landslide susceptibility maps(LSMs),which show the sp...Landslides are one of the most destructive natural hazards;they can drastically alter landscape morphology,destroy man-made struc-tures,and endanger people’s life.Landslide susceptibility maps(LSMs),which show the spatial likelihood of landslide occurrence,are crucial for environmental management,urban planning,and minimizing economic losses.To date,the majority of research into data mining LSM uses small-scale case studies focusing on a single type of landslide.This paper presents a data mining approach to producing LSM for a large,heterogeneous region that is susceptible tomultipletypesoflandslides.UsingacasestudyofPiedmont,Italy,a Random Forest algorithm is applied to produce both susceptibility maps and classification maps.These maps are combined to give a highly accurate(over 85%classification accuracy)LSM which con-tains a large amount of information and is easy to interpret.This novel method of mapping landslide susceptibility demonstrates the efficacy of Random Forest to produce highly accurate susceptibility maps for alargeheterogeneousregion withouttheneed formultiple susceptibility assessments.展开更多
The international community has made significant efforts to flatten the COVID-19 curve,including predicting transmission[1,2],executing unprecedented global lockdowns and social distancing[3,4],promoting the wearing o...The international community has made significant efforts to flatten the COVID-19 curve,including predicting transmission[1,2],executing unprecedented global lockdowns and social distancing[3,4],promoting the wearing of facemasks and social distancing measures[5],and isolating confirmed cases and contacts[6].Because of the adverse consequences of these lockdown measures[7],many cities have reopened so they can rebuild their economies.However,as mobility has gradually returned towards normal,imported cases from unknown sources have disrupted the recovery situation,and cities are continually at high risk of new waves of infection[8,9]since airborne transmission is the dominant transmission route[10].展开更多
The advent of information and communication technology and the Internet of Things have led our society toward a digital era.The proliferation of personal computers,smartphones,intelligent autonomous sensors,and pervas...The advent of information and communication technology and the Internet of Things have led our society toward a digital era.The proliferation of personal computers,smartphones,intelligent autonomous sensors,and pervasive network interactions with individuals have gradually shifted human activities from offline to online and from in person to virtual.This transformation has brought a series of challenges in a variety of fields,such as the dilemma of placelessness,some aspects of timelessness(no time relevance),and the changing relevance of distance in the field of geographic information science(GIScience).In the last two decades,“cyber thinking”in GIScience has received significant attention from different perspectives.For instance,human activities in“cyberspace”need to be reconsidered when coupled with the geographic space to observe the first law of geography.展开更多
文摘Reliable and up-to-date geospatial data plays a fundamental role in Sustainable Development Goals(SDGs)monitoring.Aiming to providing such geospa-tial data,numerous algorithms,solutions and frame-works have been developed in recent years.A mong oth-ers,A rtificial Intelligence(AI)based techniques have been widely used for the tasks of processing geospatial data.Nowadays,this topic is blooming so fast and to a vast extent in the field of Geomatics that a new subdo-main seems to arise,namely GeoAI[1-2].Even for a very quick and brief glance inthe Internet,people can find a lot of applications,projects,blogs and research articles about GeoAI,w hereas new approaches to GeoAI have been proposed and tested.
基金This work was funded by the Economic and Social Research Council(ESRC)in the United Kingdom[grant number 1477365].
文摘Smart card-automated fare collection systems now routinely record large volumes of data comprising the origins and destinations of travelers.Processing and analyzing these data open new opportunities in urban modeling and travel behavior research.This study seeks to develop an accurate framework for the study of urban mobility from smart card data by developing a heuristic primary location model to identify the home and work locations.The model uses journey counts as an indicator of usage regularity,visit-frequency to identify activity locations for regular commuters,and stay-time for the classification of work and home locations and activities.London is taken as a case study,and the model results were validated against survey data from the London Travel Demand Survey and volunteer survey.Results demonstrate that the proposed model is able to detect meaningful home and work places with high precision.This study offers a new and cost-effective approach to travel behavior and demand research.
基金UK Research and Innovation Council (UKRI) Funding(Nos.EP/R511683/1,EP/J004197/1,ES/L011840/1)UCL Dean Prize and China Scholarship Council(No.201603170309)。
文摘SpacetimeAI and GeoAI are currently hot topics,applying the latest algorithms in computer science,such as deep learning,to spatiotemporal data.Although deep learning algorithms have been successfully applied to raster data due to their natural applicability to image processing,their applications in other spatial and space-time data types are still immature.This paper sets up the proposition of using a network(&graph)-based framework as a generic spatial structure to present space-time processes that are usually represented by the points,polylines,and polygons.We illustrate network and graph-based SpaceTimeAI,from graph-based deep learning for prediction,to space-time clustering and optimisation.These applications demonstrate the advantages of network(graph)-based SpacetimeAI in the fields of transport&mobility,crime&policing,and public health.
基金This work is part of the Consumer Data Research Centre project(ES/L011840/1)funded by the UK Economic and Social Research Council(grant number 1477365).
文摘Predicting trip purpose from comprehensive and continuous smart card data is beneficial for transport and city planners in investigating travel behaviors and urban mobility.Here,we propose a framework,ActivityNET,using Machine Learning(ML)algorithms to predict passengers’trip purpose from Smart Card(SC)data and Points-of-Interest(POIs)data.The feasibility of the framework is demonstrated in two phases.Phase I focuses on extracting activities from individuals’daily travel patterns from smart card data and combining them with POIs using the proposed“activity-POIs consolidation algorithm”.Phase II feeds the extracted features into an Artificial Neural Network(ANN)with multiple scenarios and predicts trip purpose under primary activities(home and work)and secondary activities(entertainment,eating,shopping,child drop-offs/pick-ups and part-time work)with high accuracy.As a case study,the proposed ActivityNET framework is applied in Greater London and illustrates a robust competence to predict trip purpose.The promising outcomes demonstrate that the cost-effective framework offers high predictive accuracy and valuable insights into transport planning.
文摘The coronavirus pandemic that started in 2019 has had wide-ranging impacts on many aspects of people’s daily lives.At the peak of the outbreak,lockdown measures and social distancing changed the ways in which cities function.In particular,they had profound impacts on urban transportation systems,with public transport being shut down in many cities.Bike share systems(BSS)were widely reported as having experienced an increase in demand during the early stages of the pandemic before returning to pre-pandemic levels.However,the studies published to date focus mainly on the first year of the pandemic,when various waves saw continual relaxing and reintroductions of restrictions.Therefore,they fall short of exploring the role of BSS as we move to the post-pandemic period.To address this gap,this study uses origin-destination(O-D)flow data from London’s Santander Cycle Hire Scheme from 2019-2021 to analyze the changing use of BSS throughout the first two years of the pandemic,from lockdown to recovery.A Gaussian mixture model(GMM)is used to cluster 2019 BSS trips into three distinct clusters based on their duration and distance.The clusters are used as a reference from which to measure spatial and temporal change in 2020 and 2021.In agreement with previous research,BSS usage was found to have declined by nearly 30%during the first lockdown.Usage then saw a sharp increase as restrictions were lifted,characterized by longer,less direct trips throughout the afternoon rather than typical peak commuting trips.Although the aggregate number of BSS trips appeared to return to normal by October 2020,this was against the backdrop of continuing restrictions on international travel and work from home orders.The period between July and December 2021 was the first period that all government restrictions were lifted.During this time,BSS trips reached higher levels than in 2019.Spatio-temporal analysis indicates a shift away from the traditional morning and evening peak to a more diffuse pattern of working hours.The results indicate that the pandemic may have had sustained impacts on travel behavior,leading to a“new normal”that reflects different ways of working.
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.
基金supported by the Economic and Social Research Council[ES/L011840/1]Medical Research Council[MR/V028375/1].
文摘1.Introduction The COVID-19 pandemic has dramatically reshaped human mobility at global,national,regional,and individual levels,as evidenced by many studies(Chang et al.2021;Cheng et al.2022;Chinazzi et al.2020;Hou et al.2021;Santana et al.2023;Xiong et al.2020).Governments around the world have implemented containment measures such as lockdowns,travel restrictions,border closures,public transport reductions,and self-isolation for vulnerable groups.These interventions have led to effects that vary by location,timing,travel modes,and demographic characteristics.
基金The research has received funding from the Seventh Framework Programme,European Union Research and Development Funding Programme for research,technological development and demonstration under grant agreement No 603960-Novel Indicators for identifying critical INFRAstructure at RISK from Natural Hazards(INFRARISK www.infrarisk-fp7.eu).The third author's PhD research is jointly supported by China Scholarship Council under Grant 201603170309 and the Dean's Prize from the University College London.
文摘Landslides are one of the most destructive natural hazards;they can drastically alter landscape morphology,destroy man-made struc-tures,and endanger people’s life.Landslide susceptibility maps(LSMs),which show the spatial likelihood of landslide occurrence,are crucial for environmental management,urban planning,and minimizing economic losses.To date,the majority of research into data mining LSM uses small-scale case studies focusing on a single type of landslide.This paper presents a data mining approach to producing LSM for a large,heterogeneous region that is susceptible tomultipletypesoflandslides.UsingacasestudyofPiedmont,Italy,a Random Forest algorithm is applied to produce both susceptibility maps and classification maps.These maps are combined to give a highly accurate(over 85%classification accuracy)LSM which con-tains a large amount of information and is easy to interpret.This novel method of mapping landslide susceptibility demonstrates the efficacy of Random Forest to produce highly accurate susceptibility maps for alargeheterogeneousregion withouttheneed formultiple susceptibility assessments.
基金support from the National Research FoundationPrime Minister’s Office+7 种基金Singapore under its Campus for Research Excellence and Technological Enterprise(CREATE)programmeThe Hong Kong Polytechnic University Strategic Hiring Scheme(P0036221)support from the Key Program of National Natural Science Foundation of China(41930648)supports from the Hong Kong Research Grants Council(15602619,15603920,and C7064-18GF)supports from the Hong Kong Research Grants Council(14605920,14611621,and C4023-20GF)support from the National University of SingaporeMinistry of Education,Tier 1 under WBS R-109-000-270-133Ministry of Natural Resources of the People’s Republic of China(GS(2021)7327)。
文摘The international community has made significant efforts to flatten the COVID-19 curve,including predicting transmission[1,2],executing unprecedented global lockdowns and social distancing[3,4],promoting the wearing of facemasks and social distancing measures[5],and isolating confirmed cases and contacts[6].Because of the adverse consequences of these lockdown measures[7],many cities have reopened so they can rebuild their economies.However,as mobility has gradually returned towards normal,imported cases from unknown sources have disrupted the recovery situation,and cities are continually at high risk of new waves of infection[8,9]since airborne transmission is the dominant transmission route[10].
文摘The advent of information and communication technology and the Internet of Things have led our society toward a digital era.The proliferation of personal computers,smartphones,intelligent autonomous sensors,and pervasive network interactions with individuals have gradually shifted human activities from offline to online and from in person to virtual.This transformation has brought a series of challenges in a variety of fields,such as the dilemma of placelessness,some aspects of timelessness(no time relevance),and the changing relevance of distance in the field of geographic information science(GIScience).In the last two decades,“cyber thinking”in GIScience has received significant attention from different perspectives.For instance,human activities in“cyberspace”need to be reconsidered when coupled with the geographic space to observe the first law of geography.
