We live in an era of rapid urbanization as many cities are experiencing an unprecedented rate of population growth and congestion.Public transport is playing an increasingly important role in urban mobility with a nee...We live in an era of rapid urbanization as many cities are experiencing an unprecedented rate of population growth and congestion.Public transport is playing an increasingly important role in urban mobility with a need to move people and goods efficiently around the city.With such pressures on existing public transportation systems,this paper investigates the opportunities to use social media to more effectively engage with citizens and customers using such services.This research forms a case study of the use of passively collected forms of big data in cities-focusing on Sydney,Australia.Firstly,it examines social media data(Tweets)related to public transport performance.Secondly,it joins this to longitudinal big data-delay information continuously broadcast by the network over a year,thus forming hundreds of millions of data artifacts.Topics,tones,and sentiment are modeled using machine learning and Natural Language Processing(NLP)techniques.These resulting data,and models,are compared to opinions derived from a citizen survey among users.The validity of such data and models versus the intentions of users,in the context of systems that monitor and improve transport performance,are discussed.As such,key recommendations for developing Smart Cities were formed in an applied research context based on these data and techniques.展开更多
Many cities,countries and transport operators around the world are striving to design intelligent transport systems.These systems capture the value of multisource and multiform data related to the functionality and us...Many cities,countries and transport operators around the world are striving to design intelligent transport systems.These systems capture the value of multisource and multiform data related to the functionality and use of transportation infrastructure to better support human mobility,interests,economic activity and lifestyles.They aim to provide services that can enable transportation customers and managers to be better informed and make safer and more efficient use of infrastructure.In developing principles,guidelines,methods and tools to enable synergistic work between humans and computer-generated information,the science of visual analytics continues to expand our understanding of data through effective and interactive visual interfaces.In this paper,we describe an application of visual analytics related to the study of movement and transportation systems.This application documents the use of rapid,2D and 3D web visualisation and data analytics libraries and explores their potential added value to the analysis of big public transport performance data.A novel approach to displaying such data through a generalisable framework visualisation system is demonstrated.This framework recalls over a year’sworth of public transport performance data at a highly granular level in a fast,interactive browser-based environment.Greater Sydney,Australia forms a case study to highlight potential uses of the visualisation of such large,passively-collected data sets as an applied research scenario.In this paper,we argue that such highly visual systems can add data-driven rigour to service planning and longer-term transport decision-making.Furthermore,they enable the sharing of quality of service statistics with various stakeholders and citizens and can showcase improvements in services before and after policy decisions.The paper concludes by making recommendations on the value of this approach in embedding these or similar web-based systems in transport planning practice,performance management,optimisation and understanding of customer experience.展开更多
Urban modeling facilitates the generation of virtual environments for various scenarios about cities.It requires expertise and consideration,and therefore consumes massive time and computation resources.Nevertheless,r...Urban modeling facilitates the generation of virtual environments for various scenarios about cities.It requires expertise and consideration,and therefore consumes massive time and computation resources.Nevertheless,related tasks sometimes result in dissatisfaction or even failure.These challenges have received significant attention from researchers in the area of computer graphics.Meanwhile,the burgeoning development of artificial intelligence motivates people to exploit machine learning,and hence improves the conventional solutions.In this paper,we present a review of approaches to urban modeling in computer graphics using machine learning in the literature published between 2010 and 2019.This serves as an overview of the current state of research on urban modeling from a machine learning perspective.展开更多
文摘We live in an era of rapid urbanization as many cities are experiencing an unprecedented rate of population growth and congestion.Public transport is playing an increasingly important role in urban mobility with a need to move people and goods efficiently around the city.With such pressures on existing public transportation systems,this paper investigates the opportunities to use social media to more effectively engage with citizens and customers using such services.This research forms a case study of the use of passively collected forms of big data in cities-focusing on Sydney,Australia.Firstly,it examines social media data(Tweets)related to public transport performance.Secondly,it joins this to longitudinal big data-delay information continuously broadcast by the network over a year,thus forming hundreds of millions of data artifacts.Topics,tones,and sentiment are modeled using machine learning and Natural Language Processing(NLP)techniques.These resulting data,and models,are compared to opinions derived from a citizen survey among users.The validity of such data and models versus the intentions of users,in the context of systems that monitor and improve transport performance,are discussed.As such,key recommendations for developing Smart Cities were formed in an applied research context based on these data and techniques.
文摘Many cities,countries and transport operators around the world are striving to design intelligent transport systems.These systems capture the value of multisource and multiform data related to the functionality and use of transportation infrastructure to better support human mobility,interests,economic activity and lifestyles.They aim to provide services that can enable transportation customers and managers to be better informed and make safer and more efficient use of infrastructure.In developing principles,guidelines,methods and tools to enable synergistic work between humans and computer-generated information,the science of visual analytics continues to expand our understanding of data through effective and interactive visual interfaces.In this paper,we describe an application of visual analytics related to the study of movement and transportation systems.This application documents the use of rapid,2D and 3D web visualisation and data analytics libraries and explores their potential added value to the analysis of big public transport performance data.A novel approach to displaying such data through a generalisable framework visualisation system is demonstrated.This framework recalls over a year’sworth of public transport performance data at a highly granular level in a fast,interactive browser-based environment.Greater Sydney,Australia forms a case study to highlight potential uses of the visualisation of such large,passively-collected data sets as an applied research scenario.In this paper,we argue that such highly visual systems can add data-driven rigour to service planning and longer-term transport decision-making.Furthermore,they enable the sharing of quality of service statistics with various stakeholders and citizens and can showcase improvements in services before and after policy decisions.The paper concludes by making recommendations on the value of this approach in embedding these or similar web-based systems in transport planning practice,performance management,optimisation and understanding of customer experience.
文摘Urban modeling facilitates the generation of virtual environments for various scenarios about cities.It requires expertise and consideration,and therefore consumes massive time and computation resources.Nevertheless,related tasks sometimes result in dissatisfaction or even failure.These challenges have received significant attention from researchers in the area of computer graphics.Meanwhile,the burgeoning development of artificial intelligence motivates people to exploit machine learning,and hence improves the conventional solutions.In this paper,we present a review of approaches to urban modeling in computer graphics using machine learning in the literature published between 2010 and 2019.This serves as an overview of the current state of research on urban modeling from a machine learning perspective.
