The disruptive effects of the COVID-19 pandemic has rapidly shifted how individuals navigate in cities.Governments are concerned that travel behavior will shift toward a car-driven and homeworking future,shifting dema...The disruptive effects of the COVID-19 pandemic has rapidly shifted how individuals navigate in cities.Governments are concerned that travel behavior will shift toward a car-driven and homeworking future,shifting demand away from public transport use.These concerns place the recovery of public transport in a possible crisis.A resilience perspective may aid the discussion around recovery-particularly one that deviates from pre-pandemic behavior.This paper presents an empirical study of London’s public transport demand and introduces a perspective of spatial resilience to the existing body of research on post-pandemic public transport demand.This study defines spatial resilience as the rate of recovery in public transport demand within census boundaries over a period after lockdown restrictions were lifted.The relationship between spatial resilience and urban socioeconomic factors was investigated by a global spatial regression model and a localized perspective through Geographically Weighted Regression(GWR)model.In this case study of London,the analysis focuses on the period after the first COVID-19 lockdown restrictions were lifted(June 2020)and before the new restrictions in mid-September 2020.The analysis shows that outer London generally recovered faster than inner London.Factors of income,car ownership and density of public transport infrastructure were found to have the greatest influence on spatial patterns in resilience.Furthermore,influential relationships vary locally,inviting future research to examine the drivers of this spatial heterogeneity.Thus,this research recommends transport policymakers capture the influences of homeworking,ensure funding for a minimum level of service,and advocate for a polycentric recovery post-pandemic.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
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
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
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 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
Recent urban transformations have led to critical reflections on the blighted urban infrastruc-tures and called for re-stimulating vital urban places.Especially,the metro has been recognized as the backbone infrastruc...Recent urban transformations have led to critical reflections on the blighted urban infrastruc-tures and called for re-stimulating vital urban places.Especially,the metro has been recognized as the backbone infrastructure for urban mobility and the associated economy agglomeration.To date,limited research has been devoted to investigating the relationship between metro vitality and built environment in mega-cities empirically.This paper presents a multisource urban data-driven approach to quantify the metro vibrancy and its association with the underlying built environment.Massive smart card data is processed to extract metro ridership,which denotes the vibrancy around the metro station in physical space.Social media check-ins are crawled to measure the vitality of metros in virtual spaces.Both physical and virtual vibrancy are integrated into a holistic metro vibrancy metric using an entropy-based weighting method.Certain built environment characteristics,including land use,transportation and buildings are modeled as independent variables.The significant influences of built environ-mental factors on the metro vibrancy are unraveled using the ordinary least square regression and the spatial lag model.With experiments conducted in Shenzhen,Singapore and London,this study comes up with a conclusion that spatial distributions of metro vibrancy metrics in three cities are spatially autocorrelated.The regression analysis suggests that in all the three cities,more affluent urban areas tend to have higher metro virbrancy,while the road density,land use and buildings tend to impact metro vibrancy in only one or two cities.These results demonstrate the relationship between the metro vibrancy and built environment is affected by complex urban contexts.These findings help us to understand metro vibrancy thus make proper policy to re-stimulate the important metro infrastructure in the future.展开更多
基金funding from the European Research Council(ERC)under the European Union’s Horizon 2020 research and innovation programme(grant agreement No 949670)from ESRC under JPI Urban Europe/NSFC(grant No.ES/T000287/1).
文摘The disruptive effects of the COVID-19 pandemic has rapidly shifted how individuals navigate in cities.Governments are concerned that travel behavior will shift toward a car-driven and homeworking future,shifting demand away from public transport use.These concerns place the recovery of public transport in a possible crisis.A resilience perspective may aid the discussion around recovery-particularly one that deviates from pre-pandemic behavior.This paper presents an empirical study of London’s public transport demand and introduces a perspective of spatial resilience to the existing body of research on post-pandemic public transport demand.This study defines spatial resilience as the rate of recovery in public transport demand within census boundaries over a period after lockdown restrictions were lifted.The relationship between spatial resilience and urban socioeconomic factors was investigated by a global spatial regression model and a localized perspective through Geographically Weighted Regression(GWR)model.In this case study of London,the analysis focuses on the period after the first COVID-19 lockdown restrictions were lifted(June 2020)and before the new restrictions in mid-September 2020.The analysis shows that outer London generally recovered faster than inner London.Factors of income,car ownership and density of public transport infrastructure were found to have the greatest influence on spatial patterns in resilience.Furthermore,influential relationships vary locally,inviting future research to examine the drivers of this spatial heterogeneity.Thus,this research recommends transport policymakers capture the influences of homeworking,ensure funding for a minimum level of service,and advocate for a polycentric recovery post-pandemic.
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.
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.
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.
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].
基金supported by the National Natural Science Foundation of China[grant numbers 42071360 and 71961137003]Natural Science Foundation of Guangdong Provinces[grant number 2019A1515011049]+2 种基金the ESRC under JPI Urban Europe/NSFC[grant number ES/T000287/1]the European Research Council(ERC)under the European Union’s Horizon 2020 research and innova-tion programme[grant number 949670]the Basic Research Program of Shenzhen Science and Technology Innovation Committee[JCYJ20180305125113883].
文摘Recent urban transformations have led to critical reflections on the blighted urban infrastruc-tures and called for re-stimulating vital urban places.Especially,the metro has been recognized as the backbone infrastructure for urban mobility and the associated economy agglomeration.To date,limited research has been devoted to investigating the relationship between metro vitality and built environment in mega-cities empirically.This paper presents a multisource urban data-driven approach to quantify the metro vibrancy and its association with the underlying built environment.Massive smart card data is processed to extract metro ridership,which denotes the vibrancy around the metro station in physical space.Social media check-ins are crawled to measure the vitality of metros in virtual spaces.Both physical and virtual vibrancy are integrated into a holistic metro vibrancy metric using an entropy-based weighting method.Certain built environment characteristics,including land use,transportation and buildings are modeled as independent variables.The significant influences of built environ-mental factors on the metro vibrancy are unraveled using the ordinary least square regression and the spatial lag model.With experiments conducted in Shenzhen,Singapore and London,this study comes up with a conclusion that spatial distributions of metro vibrancy metrics in three cities are spatially autocorrelated.The regression analysis suggests that in all the three cities,more affluent urban areas tend to have higher metro virbrancy,while the road density,land use and buildings tend to impact metro vibrancy in only one or two cities.These results demonstrate the relationship between the metro vibrancy and built environment is affected by complex urban contexts.These findings help us to understand metro vibrancy thus make proper policy to re-stimulate the important metro infrastructure in the future.
