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Author: Ali Yazdizadeh
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Recent advances in communication technologies have enabled researchers to collect travel data from location-aware smartphones. These advances hold out the promise of allowing the automatic detection of the critical aspects (mode, purpose, etc.) of people's travel. This thesis investigates the application of artificial intelligence methods to infer mode of transport, trip purpose and transit itinerary from traveler trajectories gathered by smartphones. Supervised, Random Forest models are used to detect mode, purpose and transit itinerary of trips. Deep learning models, in particular, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), are also employed to infer mode of transport and trip purpose. The research also explores the use of Generative Adversarial Networks (GANs), as a semi-supervised learning approach, to classify trip mode. Moreover, we investigate the application of multi-task learning to simultaneously infer mode and purpose. The research uses several different data sources. Trip trajectory data was collected by the MTL Trajet smartphone Travel Survey App, in 2016. Also, other complementary datasets, such as locational data from social media, land-use, General Transit Feed Specification (GTFS), and elevation data are exploited to infer trip information. Mode of transport can be inferred with Random Forest models, ensemble CNN models, and RNN approaches with an accuracy of 87%, 91%, and 86%, respectively. The Random Forest and multi-task RNN models to infer trip purpose achieve an accuracy of 71% and 78%, respectively. Also, the Random Forest transit itinerary inference model can predict used transit itineraries with an accuracy of 81%. While further improvement is required to enhance the performance of the developed artificial intelligence models on smartphone data, the results of the research indicate the capability of smartphone-based travel surveys as a complementary (and potentially replacement) surveying tool to household travel surveys.
Author: Ali Yazdizadeh
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Recent advances in communication technologies have enabled researchers to collect travel data from location-aware smartphones. These advances hold out the promise of allowing the automatic detection of the critical aspects (mode, purpose, etc.) of people's travel. This thesis investigates the application of artificial intelligence methods to infer mode of transport, trip purpose and transit itinerary from traveler trajectories gathered by smartphones. Supervised, Random Forest models are used to detect mode, purpose and transit itinerary of trips. Deep learning models, in particular, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), are also employed to infer mode of transport and trip purpose. The research also explores the use of Generative Adversarial Networks (GANs), as a semi-supervised learning approach, to classify trip mode. Moreover, we investigate the application of multi-task learning to simultaneously infer mode and purpose. The research uses several different data sources. Trip trajectory data was collected by the MTL Trajet smartphone Travel Survey App, in 2016. Also, other complementary datasets, such as locational data from social media, land-use, General Transit Feed Specification (GTFS), and elevation data are exploited to infer trip information. Mode of transport can be inferred with Random Forest models, ensemble CNN models, and RNN approaches with an accuracy of 87%, 91%, and 86%, respectively. The Random Forest and multi-task RNN models to infer trip purpose achieve an accuracy of 71% and 78%, respectively. Also, the Random Forest transit itinerary inference model can predict used transit itineraries with an accuracy of 81%. While further improvement is required to enhance the performance of the developed artificial intelligence models on smartphone data, the results of the research indicate the capability of smartphone-based travel surveys as a complementary (and potentially replacement) surveying tool to household travel surveys.
Author: Ajinkya Ranjeet Ghorpade
Publisher:
ISBN:
Category :
Languages : en
Pages : 85
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Book Description
Understanding the travel routine of the individuals is important in many domains. In transport research understanding daily travel routine is crucial for modeling the travel behavior of the individuals. Such models help predict the travel demand and develop strategies for managing that demand. Understanding travel patterns of the individuals is also important to develop effective incentive mechanisms. Location-based services like personal digital assistants and journey planners use historical travel routine to build preferences of the user and make useful recommendations. In health sciences logging the routine travel behavior is important to monitor health of the patients and make recommendations wherever necessary. Several fitness tracking applications available on smartphones utilize the travel activity diary to evaluate the fitness of the individuals and make recommendations. The proliferation of sensing-enabled smartphone devices engendered the development of tools for logging travel routine of individuals. The research in this thesis uses the sensor data collected from smartphone devices to develop a travel activity inference algorithm. Presently, the research into travel activity inference has been focused on developing supervised learning algorithms. These algorithms require a large amount of labeled data for training algorithms that generalize well. Generalization in personalized travel activity inference is a challenging problem due to the concept drift. The problem of concept drift is magnified as the more personalized information is introduced in the input variables. Once the users start using the applications they are constantly generating new data. Expecting the users to label all the data generated by them is impractical. Instead, it would be useful to identify only those examples which would help most improve the algorithm and have the user label such instance. This reduces the burden on the user and does not discourage them from participating in the data collection process. In other words, we need a model that is identifies concept drift in data and adapts accordingly. There has been advances in the deep learning research in last few years. The deep learning algorithms provide a framework for learning feature representation from raw data. The convolutional neural networks have been particularly effective in learning feature representations on many datasets. These models have achieved significant improvement on many complex problems over other machine learning approaches. For the sequential classification problems like the travel activity inference, the recurrent neural network like long short term memory networks are particularly suitable. This thesis proposes to use the deep learning algorithms for travel activity inference. To develop an end-to-end deep learning algorithm that learns feature representations from raw sensor data and incorporates different sensors with differing frequencies. The research proposes using a combination of convolutional neural network for feature representation learning in both time and frequency domain and long short term memory network for sequential classification. In practical situations, the users of the smartphones cannot be asked to carry their smartphones in a fixed position every time. The proposed algorithm for travel activity inference need to be robust to changes in orientation of the smartphones. We compared the performance of the proposed deep learning algorithm against a baseline model based on the current supervised machine learning approaches. The deep learning algorithm achieved an overall average accuracy of 95.98% compared to the baseline method which achieved an overall average accuracy of 89%. We also show that the proposed deep learning algorithm is robust to changes in the orientation of the smartphone.
Author: Rich Ling
Publisher: Oxford University Press
ISBN: 0190864400
Category : Language Arts & Disciplines
Languages : en
Pages : 731
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Book Description
Mobile communication has dramatically changed over the past decade with the diffusion of smartphones. Unlike the basic 2G mobile phones, which "merely" facilitated communication between individuals on the move, smartphones allow individuals to communicate, to entertain and inform themselves, to transact, to navigate, to take photos, and countless other things. Mobile communication has thus transformed society by allowing new forms of coordination, communication, consumption, social interaction, and access to news/entertainment. All of this is regardless of the space in which users are immersed. Set in the context of the developed and the developing world, The Oxford Handbook of Mobile Communication and Society updates current scholarship surrounding mobile media and communication. The 43 chapters in this handbook examine mobile communication and its evolving impact on individuals, institutions, groups, societies, and businesses. Contributors examine the communal benefits, social consequences, theoretical perspectives, organizational potential, and future consequences of mobile communication. Topics covered include, among many other things, trends in the Global South, location-based services, and the "appification" of mobile communication and society.
Author: Prabin K. Bora
Publisher: Springer Nature
ISBN: 9811615500
Category : Technology & Engineering
Languages : en
Pages : 209
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Book Description
This book comprises the select proceedings of the International Conference on Emerging Global Trends in Engineering and Technology (EGTET 2020), held in Guwahati, India. The chapters in this book focus on the latest cleaner, greener, and efficient technologies being developed for the implementation of smart cities across the world. The broader topical sections include Smart Buildings, Infrastructures and Disaster Management; Smart Governance; Technologies for Smart Cities, and Wireless Connectivity for Smart Cities. This book will cater to students, researchers, industry professionals, and policy making bodies interested and involved in the planning and implementation of smart city projects.
Author: Mohamed Elhoseny
Publisher: Springer Nature
ISBN: 3030630684
Category : Science
Languages : en
Pages : 249
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Book Description
This volume discusses recent advances in Artificial Intelligence (AI) applications in smart, internet-connected societies, highlighting three key focus areas. The first focus is on intelligent sensing applications. This section details the integration of Wireless Sensing Networks (WSN) and the use of intelligent platforms for WSN applications in urban infrastructures, and discusses AI techniques on hardware and software systems such as machine learning, pattern recognition, expert systems, neural networks, genetic algorithms, and intelligent control in transportation and communications systems. The second focus is on AI-based Internet of Things (IoT) systems, which addresses applications in traffic management, medical health, smart homes and energy. Readers will also learn about how AI can extract useful information from Big Data in IoT systems. The third focus is on crowdsourcing (CS) and computing for smart cities. this section discusses how CS via GPS devices, GIS tools, traffic cameras, smart cards, smart phones and road deceleration devices enables citizens to collect and share data to make cities smart, and how these data can be applied to address urban issues including pollution, traffic congestion, public safety and increased energy consumption. This book will of interest to academics, researchers and students studying AI, cloud computing, IoT and crowdsourcing in urban applications.
Author: Wookey Lee
Publisher: Springer
ISBN: 9811065209
Category : Technology & Engineering
Languages : en
Pages : 349
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Book Description
This proceedings volume presents selected papers from the 7th International Conference on Emerging Databases: Technologies, Applications, and Theory (EDB 2017), which was held in Busan, Korea from 7 to 9 August, 2017. This conference series was launched by the Korean Institute of Information Scientists and Engineers (KIISE) Database Society of Korea as an annual forum for exploring novel technologies, applications, and research advances in the field of emerging databases. This forum has evolved into the premier international venue for researchers and practitioners to discuss current research issues, challenges, new technologies, and solutions.
Author: Jean-Loup Madre
Publisher: Emerald Group Publishing
ISBN: 1848558449
Category : Transportation
Languages : en
Pages : 662
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Book Description
Identifies various challenges to the world community of transport survey specialists as well as the larger constituency of practitioners, planners, and decision-makers that it serves and provides potential solutions and recommendations for addressing them.
Author: Hassan A. Karimi
Publisher: CRC Press
ISBN: 1351855980
Category : Computers
Languages : en
Pages : 283
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Book Description
Data science has recently gained much attention for a number of reasons, and among them is Big Data. Scientists (from almost all disciplines including physics, chemistry, biology, sociology, among others) and engineers (from all fields including civil, environmental, chemical, mechanical, among others) are faced with challenges posed by data volume, variety, and velocity, or Big Data. This book is designed to highlight the unique characteristics of geospatial data, demonstrate the need to different approaches and techniques for obtaining new knowledge from raw geospatial data, and present select state-of-the-art geospatial data science techniques and how they are applied to various geoscience problems.
Author: Wenzhong Shi
Publisher: Springer Nature
ISBN: 9811589836
Category : Social Science
Languages : en
Pages : 941
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Book Description
This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity.
Author: Anna Maria Vegni
Publisher: CRC Press
ISBN: 1498749208
Category : Computers
Languages : en
Pages : 192
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Book Description
The book provides a comprehensive guide to vehicular social networks. The book focuses on a new class of mobile ad hoc networks that exploits social aspects applied to vehicular environments. Selected topics are related to social networking techniques, social-based routing techniques applied to vehicular networks, data dissemination in VSNs, architectures for VSNs, and novel trends and challenges in VSNs. It provides significant technical and practical insights in different aspects from a basic background on social networking, the inter-related technologies and applications to vehicular ad-hoc networks, the technical challenges, implementation and future trends.
