ZTE Corporation announced on April 19, 2010 that it has started selling world＇s fastest HSPA＋ 28.8M data card with COSMOTE in Greece, moving the industry forward with record speeds for mobile broadband services by u...ZTE Corporation announced on April 19, 2010 that it has started selling world＇s fastest HSPA＋ 28.8M data card with COSMOTE in Greece, moving the industry forward with record speeds for mobile broadband services by using advanced HSPA＋ MIMO technology.展开更多
ZTE Corporation, a leading global provider of telecommunications equipment and network solutions, launched the world’s fastest HSUPA data card at the 3GSM World Congress 2007 in Barcelona, Spain.
ZTE Corporation (ZTE) announced on February 16,2009 that their complete line of mobile broadband data cards would support Windows 7 and be compliant with the Windows Network Driver Interface Specification 6.20,NDIS6.20.
ZTE Corporation announced on August 25,2009 that sales of its Data Card have topped 7 million in first half of 2009,representing an increase of 366% compared to the same period last year,the fastest growth amongst all
Metro is an important form of public transport in Shanghai.Based on the metro card data,we conduct the cluster analysis of Shanghai metro stations according to the pattern of passenger flow changing with time.Then the...Metro is an important form of public transport in Shanghai.Based on the metro card data,we conduct the cluster analysis of Shanghai metro stations according to the pattern of passenger flow changing with time.Then the characteristics of travel time and surrounding land use are investigated for different types of stations to explore the relationship between urban land-use characteristics and travel activities reflected by passenger flow at metro stations.It is found that the passenger flow pattern of metro stations is closely related to the location conditions of stations and its surrounding land-use patterns.Based on various characteristics,285 metro stations are classified into four types,including residential-oriented stations,employmentoriented stations,employment-residence-oriented stations,and integrated functionaloriented stations,reflecting the interaction between spontaneous travel behavior and urban land-use characteristics and providing a reference for optimizing the urban functional structure and the spatial allocation of facilities.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
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
As an essential component of bus dwelling time, passenger boarding time has a significant impact on bus running reliability and service quality. In order to understand the passengers’ boarding process and mitigate pa...As an essential component of bus dwelling time, passenger boarding time has a significant impact on bus running reliability and service quality. In order to understand the passengers’ boarding process and mitigate passenger boarding time, a regression analysis framework is proposed to capture the difference and influential factors of boarding time for adult and elderly passengers based on smart card data from Changzhou. Boarding gap, the time difference between two consecutive smart card tapping records, is calculated to approximate passenger boarding time. Analysis of variance is applied to identify whether the difference in boarding time between adults and seniors is statistically significant. The multivariate regression modeling approach is implemented to analyze the influences of passenger types, marginal effects of each additional boarding passenger and bus floor types on the total boarding time at each stop. Results show that a constant difference exists in boarding time between adults and seniors even without considering the specific bus characteristics. The average passenger boarding time decreases when the number of passenger increases. The existence of two entrance steps delays the boarding process, especially for elderly passengers.展开更多
Metro system has experienced the global rapid rise over the past decades. However,few studies have paid attention to the evolution in system usage with the network expanding. The paper's main objectives are to ana...Metro system has experienced the global rapid rise over the past decades. However,few studies have paid attention to the evolution in system usage with the network expanding. The paper's main objectives are to analyze passenger flow characteristics and evaluate travel time reliability for the Nanjing Metro network by visualizing the smart card data of April 2014,April 2015 and April 2016. We performed visualization techniques and comparative analyses to examine the changes in system usage between before and after the system expansion. Specifically,workdays,holidays and weekends were specially segmented for analysis.Results showed that workdays had obvious morning and evening peak hours due to daily commuting,while no obvious peak hours existed in weekends and holidays and the daily traffic was evenly distributed. Besides,some metro stations had a serious directional imbalance,especially during the morning and evening peak hours of workdays. Serious unreliability occurred in morning peaks on workdays and the reliability of new lines was relatively low,meanwhile,new stations had negative effects on exiting stations in terms of reliability. Monitoring the evolution of system usage over years enables the identification of system performance and can serve as an input for improving the metro system quality.展开更多
In order to design a more efficient and more convenient temperature acquisition system, an approach combining USB data acquisition card with K type thermocouple temperature sensor is proposed under the circumstance of...In order to design a more efficient and more convenient temperature acquisition system, an approach combining USB data acquisition card with K type thermocouple temperature sensor is proposed under the circumstance of LabVIEW 2012 programming software. Firstly, the LabVIEW 2012 programming software is used to complete a temperature acquisition control program. Secondly, K type thermocouple temperature sensor is employed to transfer the temperature information. Thirdly, Then the USB data acquisition card can collect the voltage of K type thermocouple temperature sensor and convert it to a temperature scale. And, the simplification of experimental procedure can reduce the cost of development greatly. Finally, the experimental results illustrate that the range of measurement temperature is more wide and the temperature scale is more accurate.展开更多
A data acquisition system for testing gas sensor array response to multi-gas is presented.The testing system is based on the character of the gas response of metal oxide semiconductor gas sensor array.The data acquisi...A data acquisition system for testing gas sensor array response to multi-gas is presented.The testing system is based on the character of the gas response of metal oxide semiconductor gas sensor array.The data acquisition is realized automatically through the real time controlling of the data acquisition card PCI1711.This system is highly attractive for electronic nose,which is a powerful tool for the discrimination of gases.展开更多
Most of the current existing accessibility measures quantify the potential of reaching desirable opportunities across space and time.Nevertheless,these potential measurements only illus-trate the maximum possible acce...Most of the current existing accessibility measures quantify the potential of reaching desirable opportunities across space and time.Nevertheless,these potential measurements only illus-trate the maximum possible accessibility a person can have,which may not accurately measure real-world transit accessibility in urban areas.This paper introduces a novel methodology to measure positive public transit accessibility based on multi-source big public transit data such as Smart Card Data(SCD)and Global Navigation Satellite System trajectory data,which embed rich travel information and real-world spatio-temporal constraints.First,we use multi-source transit data to reconstruct trip chains,which are used to extract popular destinations.A novel transit accessibility measure is defined to account for latent trip information such as mode/route preference,opportunity attraction,and travel impedance that are difficult to capture explicitly via traditional normative measures.Finally,we produce accessibility maps to visualize time-varying and heterogeneous accessibility patterns distributed over the study region.We performed an empirical evaluation on real-world transit data collected in Shenzhen City,China,demonstrating the applicability and effectiveness of the proposed method in mapping positive transit accessibility over large metropolitan areas.The results and findings of the empirical study demonstrate that the proposed positive accessibility measure can better capture travel behavior characteristics and constraints than traditional normative measures.The measure-ment method can be used as a practical high-resolution mapping tool for transit decision makers in evaluating public transit systems,supporting strategic transit planning,and improv-ing daily transit management.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
Credit card fraudulent data is highly imbalanced, and it has presented an overwhelmingly large portion of nonfraudulent transactions and a small portion of fraudulent transactions. The measures used to judge the verac...Credit card fraudulent data is highly imbalanced, and it has presented an overwhelmingly large portion of nonfraudulent transactions and a small portion of fraudulent transactions. The measures used to judge the veracity of the detection algorithms become critical to the deployment of a model that accurately scores fraudulent transactions taking into account case imbalance, and the cost of identifying a case as genuine when, in fact, the case is a fraudulent transaction. In this paper, a new criterion to judge classification algorithms, which considers the cost of misclassification, is proposed, and several undersampling techniques are compared by this new criterion. At the same time, a weighted support vector machine (SVM) algorithm considering the financial cost of misclassification is introduced, proving to be more practical for credit card fraud detection than traditional methodologies. This weighted SVM uses transaction balances as weights for fraudulent transactions, and a uniformed weight for nonfraudulent transactions. The results show this strategy greatly improve performance of credit card fraud detection.展开更多
文摘ZTE Corporation announced on April 19, 2010 that it has started selling world＇s fastest HSPA＋ 28.8M data card with COSMOTE in Greece, moving the industry forward with record speeds for mobile broadband services by using advanced HSPA＋ MIMO technology.
文摘ZTE Corporation, a leading global provider of telecommunications equipment and network solutions, launched the world’s fastest HSUPA data card at the 3GSM World Congress 2007 in Barcelona, Spain.
文摘ZTE Corporation (ZTE) announced on February 16,2009 that their complete line of mobile broadband data cards would support Windows 7 and be compliant with the Windows Network Driver Interface Specification 6.20,NDIS6.20.
文摘ZTE Corporation announced on August 25,2009 that sales of its Data Card have topped 7 million in first half of 2009,representing an increase of 366% compared to the same period last year,the fastest growth amongst all
文摘Metro is an important form of public transport in Shanghai.Based on the metro card data,we conduct the cluster analysis of Shanghai metro stations according to the pattern of passenger flow changing with time.Then the characteristics of travel time and surrounding land use are investigated for different types of stations to explore the relationship between urban land-use characteristics and travel activities reflected by passenger flow at metro stations.It is found that the passenger flow pattern of metro stations is closely related to the location conditions of stations and its surrounding land-use patterns.Based on various characteristics,285 metro stations are classified into four types,including residential-oriented stations,employmentoriented stations,employment-residence-oriented stations,and integrated functionaloriented stations,reflecting the interaction between spontaneous travel behavior and urban land-use characteristics and providing a reference for optimizing the urban functional structure and the spatial allocation of facilities.
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The National Natural Science Foundation of China(No.51338003,71801041)
文摘As an essential component of bus dwelling time, passenger boarding time has a significant impact on bus running reliability and service quality. In order to understand the passengers’ boarding process and mitigate passenger boarding time, a regression analysis framework is proposed to capture the difference and influential factors of boarding time for adult and elderly passengers based on smart card data from Changzhou. Boarding gap, the time difference between two consecutive smart card tapping records, is calculated to approximate passenger boarding time. Analysis of variance is applied to identify whether the difference in boarding time between adults and seniors is statistically significant. The multivariate regression modeling approach is implemented to analyze the influences of passenger types, marginal effects of each additional boarding passenger and bus floor types on the total boarding time at each stop. Results show that a constant difference exists in boarding time between adults and seniors even without considering the specific bus characteristics. The average passenger boarding time decreases when the number of passenger increases. The existence of two entrance steps delays the boarding process, especially for elderly passengers.
基金Sponsored by Projects of International Cooperation and Exchange of the National Natural Science Foundation of China(Grant No.51561135003)Key Project of National Natural Science Foundation of China(Grant No.51338003)
文摘Metro system has experienced the global rapid rise over the past decades. However,few studies have paid attention to the evolution in system usage with the network expanding. The paper's main objectives are to analyze passenger flow characteristics and evaluate travel time reliability for the Nanjing Metro network by visualizing the smart card data of April 2014,April 2015 and April 2016. We performed visualization techniques and comparative analyses to examine the changes in system usage between before and after the system expansion. Specifically,workdays,holidays and weekends were specially segmented for analysis.Results showed that workdays had obvious morning and evening peak hours due to daily commuting,while no obvious peak hours existed in weekends and holidays and the daily traffic was evenly distributed. Besides,some metro stations had a serious directional imbalance,especially during the morning and evening peak hours of workdays. Serious unreliability occurred in morning peaks on workdays and the reliability of new lines was relatively low,meanwhile,new stations had negative effects on exiting stations in terms of reliability. Monitoring the evolution of system usage over years enables the identification of system performance and can serve as an input for improving the metro system quality.
文摘In order to design a more efficient and more convenient temperature acquisition system, an approach combining USB data acquisition card with K type thermocouple temperature sensor is proposed under the circumstance of LabVIEW 2012 programming software. Firstly, the LabVIEW 2012 programming software is used to complete a temperature acquisition control program. Secondly, K type thermocouple temperature sensor is employed to transfer the temperature information. Thirdly, Then the USB data acquisition card can collect the voltage of K type thermocouple temperature sensor and convert it to a temperature scale. And, the simplification of experimental procedure can reduce the cost of development greatly. Finally, the experimental results illustrate that the range of measurement temperature is more wide and the temperature scale is more accurate.
文摘A data acquisition system for testing gas sensor array response to multi-gas is presented.The testing system is based on the character of the gas response of metal oxide semiconductor gas sensor array.The data acquisition is realized automatically through the real time controlling of the data acquisition card PCI1711.This system is highly attractive for electronic nose,which is a powerful tool for the discrimination of gases.
基金This work was supported by the National Natural Science Foundation of China[grant number 41871308]the National Key R&D Program of China(International Scientific&Technological Cooperation Program)[grant number 2019YFE0106500]the Fundamental Research Funds for the Central Universities.
文摘Most of the current existing accessibility measures quantify the potential of reaching desirable opportunities across space and time.Nevertheless,these potential measurements only illus-trate the maximum possible accessibility a person can have,which may not accurately measure real-world transit accessibility in urban areas.This paper introduces a novel methodology to measure positive public transit accessibility based on multi-source big public transit data such as Smart Card Data(SCD)and Global Navigation Satellite System trajectory data,which embed rich travel information and real-world spatio-temporal constraints.First,we use multi-source transit data to reconstruct trip chains,which are used to extract popular destinations.A novel transit accessibility measure is defined to account for latent trip information such as mode/route preference,opportunity attraction,and travel impedance that are difficult to capture explicitly via traditional normative measures.Finally,we produce accessibility maps to visualize time-varying and heterogeneous accessibility patterns distributed over the study region.We performed an empirical evaluation on real-world transit data collected in Shenzhen City,China,demonstrating the applicability and effectiveness of the proposed method in mapping positive transit accessibility over large metropolitan areas.The results and findings of the empirical study demonstrate that the proposed positive accessibility measure can better capture travel behavior characteristics and constraints than traditional normative measures.The measure-ment method can be used as a practical high-resolution mapping tool for transit decision makers in evaluating public transit systems,supporting strategic transit planning,and improv-ing daily transit management.
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.
文摘Credit card fraudulent data is highly imbalanced, and it has presented an overwhelmingly large portion of nonfraudulent transactions and a small portion of fraudulent transactions. The measures used to judge the veracity of the detection algorithms become critical to the deployment of a model that accurately scores fraudulent transactions taking into account case imbalance, and the cost of identifying a case as genuine when, in fact, the case is a fraudulent transaction. In this paper, a new criterion to judge classification algorithms, which considers the cost of misclassification, is proposed, and several undersampling techniques are compared by this new criterion. At the same time, a weighted support vector machine (SVM) algorithm considering the financial cost of misclassification is introduced, proving to be more practical for credit card fraud detection than traditional methodologies. This weighted SVM uses transaction balances as weights for fraudulent transactions, and a uniformed weight for nonfraudulent transactions. The results show this strategy greatly improve performance of credit card fraud detection.
