Mobility Data Driven Urban Traffic Monitoring

This book introduces the concepts of mobility data and data-driven urban traffic monitoring. A typical framework of mobility data-based urban traffic monitoring is also presented, and it describes the processes of mobility data collection, data processing, traffic modelling, and some practical issues of applying the models for urban traffic monitoring. This book presents three novel mobility data-driven urban traffic monitoring approaches. First, to attack the challenge of mobility data sparsity, the authors propose a compressive sensing-based urban traffic monitoring approach. This solution mines the traffic correlation at the road network scale and exploits the compressive sensing theory to recover traffic conditions of the whole road network from sparse traffic samplings. Second, the authors have compared the traffic estimation performances between linear and nonlinear traffic correlation models and proposed a dynamical non-linear traffic correlation modelling-based urban traffic monitoring approach. To address the challenge of involved huge computation overheads, the approach adapts the traffic modelling and estimations tasks to Apache Spark, a popular parallel computing framework. Third, in addition to mobility data collected by the public transit systems, the authors present a crowdsensing-based urban traffic monitoring approach. The proposal exploits the lightweight mobility data collected from participatory bus riders to recover traffic statuses through careful data processing and analysis. Last but not the least, the book points out some future research directions, which can further improve the accuracy and efficiency of mobility data-driven urban traffic monitoring at large scale. This book targets researchers, computer scientists, and engineers, who are interested in the research areas of intelligent transportation systems (ITS), urban computing, big data analytic, and Internet of Things (IoT). Advanced level students studying these topics benefit from this book as well.

Handbook of Mobility Data Mining: Volume Three: Mobility Data-Driven Applications introduces the fundamental technologies of mobile big data mining (MDM), advanced AI methods, and upper-level applications, helping readers comprehensively understand MDM with a bottom-up approach. The book explains how to preprocess mobile big data, visualize urban mobility, simulate and predict human travel behavior, and assess urban mobility characteristics and their matching performance as conditions and constraints in transport, emergency management, and sustainability development systems. The book contains crucial information for researchers, engineers, operators, administrators, and policymakers seeking greater understanding of current technologies' infra-knowledge structure and limitations. The book introduces how to design MDM platforms that adapt to the evolving mobility environment—and new types of transportation and users—based on an integrated solution that utilizes sensing and communication capabilities to tackle significant challenges faced by the MDM field. This third volume looks at various cases studies to illustrate and explore the methods introduced in the first two volumes, covering topics such as Intelligent Transportation Management, Smart Emergency Management—detailing cases such as the Fukushima earthquake, Hurricane Katrina, and COVID-19—and Urban Sustainability Development, covering bicycle and railway travel behavior, mobility inequality, and road and light pollution inequality. Introduces MDM applications from six major areas: intelligent transportation management, shared transportation systems, disaster management, pandemic response, low-carbon transportation, and social equality Uses case studies to examine possible solutions that facilitate ethical, secure, and controlled emergency management based on mobile big data Helps develop policy innovations beneficial to citizens, businesses, and society Stems from the editor’s strong network of global transport authorities and transport companies, providing a solid knowledge structure and data foundation as well as geographical and stakeholder coverage

Data-Driven Solutions to Transportation Problems explores the fundamental principle of analyzing different types of transportation-related data using methodologies such as the data fusion model, the big data mining approach, computer vision-enabled traffic sensing data analysis, and machine learning. The book examines the state-of-the-art in data-enabled methodologies, technologies and applications in transportation. Readers will learn how to solve problems relating to energy efficiency under connected vehicle environments, urban travel behavior, trajectory data-based travel pattern identification, public transportation analysis, traffic signal control efficiency, optimizing traffic networks network, and much more. Synthesizes the newest developments in data-driven transportation science Includes case studies and examples in each chapter that illustrate the application of methodologies and technologies employed Useful for both theoretical and technically-oriented researchers

Big Data and Mobility as a Service explores MaaS platforms that can be adaptable to the ever-evolving mobility environment. It looks at multi-mode urban crowd data to assess urban mobility characteristics, their shared transportation potential, and their performance conditions and constraints. The book analyzes the roles of multimodality, travel behavior, urban mobility dynamics and participation. Combined with insights on using big data to analyze market and policy decisions, this book is an essential tool for urban transportation management researchers and practitioners. Summarizes current fundamental MaaS technologies Shows how to utilize anonymous big data for transportation analysis and problem-solving Illustrates, with data-enabled shared transportation service examples from different countries, the similarities and differences within a global urban mobility framework

This dissertation, "Spatial and Temporal Regularized Compressive Sensing for Urban Traffic Monitoring" by Tian, Lan, 蘭天, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Urban transport system plays an important role in the economic, social, and environmental dimensions of cities. However, transport system is still facing many challenges, such as the traffic congestion issue. With recent advancements in sensor technologies, urban traffic monitoring system is capable of collecting traffic information from new data sources to mitigate these challenges. Traffic information can be used for both real-time traffic management and long term transport planning. Nonetheless, data sparseness is a common issue among these traffic sensor data, which leads to inaccurate or even mistaken results for higher-level traffic data analysis. To solve the data sparseness issue of traffic sensors, real-world floating car data from Wuhan city is collected and examined in this research. By extracting link-based average traffic speed for road links at different time intervals, an incomplete traffic condition matrix is formulated with missing entries due to the data sparseness issue. The research question can be posed as how to interpolate the missing entries from known sample in the traffic condition matrix. The literature shows that the typical traffic interpolation models are vulnerable to high data loss. On the contrary, compressive sensing based interpolation models in the literature can still perform well under high data loss. However, current compressive sensing based traffic interpolation models are too general owing to their data-driven strategies. A spatial and temporal regularized compressive sensing model is proposed to fill in the research gap identified from the literature. The model framework is established primarily based on current compressive sensing interpolation models. Using non-negative matrix factorization, the traffic condition matrix can be decomposed into the spatial factor matrix and temporal factor matrix. The model framework further employs the spatial and temporal constraints on the two factor matrices respectively, such as the spatial correlation, network topology, and short-term stability. The proposed model is equivalent to an optimization problem that minimizes errors with the constraints from low rank and spatio-temporal properties. Stochastic gradient descent algorithm is provided to solve the minimization problem of the proposed model. The proposed model is evaluated using root mean square error with a 5-fold cross validation. The proposed model is competed with temporal KNN model, space-time KNN model, Kriging model, and baseline compressive sensing model under different data loss patterns and data loss ratios (e.g. from 50% to 90%). Results show that the proposed model performs generally better than these models under these scenarios. This research establishes a paradigm for regularized compressive sensing interpolation models. The regularization terms on the spatial factor matrix and temporal factor matrix can be substituted with alternative constraints from domain knowledge. With further extensions, the proposed model has potential to be applied in several future studies such as the traffic data compression and traffic prediction. DOI: 10.5353/th_b5689252 Subjects: Urban transportation Traffic monitoring

Smart Urban Mobility: Transport Planning in the Age of Big Data and Digital Twins explores the data-driven paradigm shift in urban mobility planning and examines how well-established practices and strong data analytics efforts can be better aligned to fit transport planning practices and "smart" mobility management needs. The book provides a comprehensive survey of the major big data and technology resources derived from smart cities research which are collectively poised to transform urban mobility. Chapters highlight the important aspects of each data source affecting applicability, along with the outcomes of smart mobility measures and campaigns.Transport planners, urban policymakers, public administrators, city managers, data scientists, and consulting companies managing smart city interventions and data-driven urban transformation projects will gain a better understanding of this up-and-coming research from this book’s detailed overview and numerous practical examples and best practices for operational deployment. Addresses key principles underlying smart mobility, as well as opportunities and challenges of integrating big data-driven insights into transport planning and smart cities Presents practical advice on how to implement smart mobility advances, providing a benchmark reference by best practice examples in the field Examines synthesis of existing gaps, limitations, and big data potential beyond traditional data needs for transport planning, as well as examples of the best practices

This book starts from the relationship between urban built environment and travel behavior and focuses on analyzing the origin of traffic phenomena behind the data through multi-source traffic big data, which makes the book unique and different from the previous data-driven traffic big data analysis literature. This book focuses on understanding, estimating, predicting, and optimizing mobility patterns. Readers can find multi-source traffic big data processing methods, related statistical analysis models, and practical case applications from this book. This book bridges the gap between traffic big data, statistical analysis models, and mobility pattern analysis with a systematic investigation of traffic big data’s impact on mobility patterns and urban planning.

Data-Driven Traffic Engineering: Understanding of Traffic and Applications Based on Three-Phase Traffic Theory shifts the current focus from using modeling and simulation data for traffic measurements to the use of actual data. The book uses real-world, empirically-derived data from a large fleet of connected vehicles, local observations and aerial observation to shed light on key traffic phenomena. Readers will learn how to develop an understanding of the empirical features of vehicular traffic networks and how to consider these features in emerging, intelligent transport systems. Topics cover congestion patterns, fuel consumption, the influence of weather, and much more. This book offers a unique, data-driven analysis of vehicular traffic in traffic networks, also considering how to apply data-driven insights to the intelligent transport systems of the future. Provides an empirically-driven analysis of traffic measurements/congestion based on real-world data collected from a global fleet of vehicles Applies Kerner’s three-phase traffic theory to empirical data Offers a critical scientific understanding of the underlying concerns of traffic control in automated driving and intelligent transport systems

Mobility data are proliferating at an unprecedented rate due to the ubiquitous GPS sensing and tracking. The increased availability of mobility data gives rise to numerous applications ranging from urban traffic monitoring to participatory environmental sensing. Detecting anomalies observed in mobility data (specified here as mobility-based anomaly detection) has attracted significant attention from researchers and practitioners in various fields due to its significant real-world impact. For example, traffic anomalies are used for traffic accident monitoring, and anomalies in environmental mobile sensing data are used to signal potential natural hazards. Despite a large number of studies available on mobility-based anomaly detection, many of the studies are conducted in distinctly different fields and have not been examined under a unified framework. This dissertation provides a systematic investigation of mobility-based anomaly detection to fill this knowledge gap. I propose a taxonomy of mobility-based anomaly detection to organize the existing relevant studies into three categories based on the source and target attributes of mobility data used in the anomaly detection process: (1) utilizing mobility attributes as both source and target in anomaly detection (mobility to mobility anomaly detection), (2) utilizing mobility attributes as the source and non-mobility attributes as the target (mobility to non-mobility anomaly detection), and (3) utilizing non-mobility attributes as the source and mobility attributes as the target (non-mobility to mobility anomaly detection). Following the taxonomy, three individual studies are presented, with each providing an example for one of the three categories. The first study (an example of mobility to mobility anomaly detection) identifies anomalous patterns of shared dockless e-scooters using an unsupervised deep learning approach. The second study (an example of mobility to non-mobility anomaly detection) detects anomalies in crowdsourced radiation measurements. The third study (an example of non-mobility to mobility anomaly detection) models the atypical event travel patterns of football fans using geolocated tweets. The three studies develop new methods in addressing the challenges of mobility-based anomaly detection and provide insights into the specific application domain. The dissertation provides one of the first systematic efforts to address mobility-based anomaly detection generally and highlights challenges and opportunities for future research.

Implementing Data-Driven Strategies in Smart Cities is a guidebook and roadmap for practitioners seeking to operationalize data-driven urban interventions. The book opens by exploring the revolution that big data, data science, and the Internet of Things are making feasible for the city. It explores alternate topologies, typologies, and approaches to operationalize data science in cities, drawn from global examples including top-down, bottom-up, greenfield, brownfield, issue-based, and data-driven. It channels and expands on the classic data science model for data-driven urban interventions – data capture, data quality, cleansing and curation, data analysis, visualization and modeling, and data governance, privacy, and confidentiality. Throughout, illustrative case studies demonstrate successes realized in such diverse cities as Barcelona, Cologne, Manila, Miami, New York, Nancy, Nice, São Paulo, Seoul, Singapore, Stockholm, and Zurich. Given the heavy emphasis on global case studies, this work is particularly suitable for any urban manager, policymaker, or practitioner responsible for delivering technological services for the public sector from sectors as diverse as energy, transportation, pollution, and waste management. Explores numerous specific urban interventions drawn from global case studies, helping readers understand real urban challenges and create data-driven solutions Provides a step-by-step and applied holistic guide and methodology for immediate application in the reader’s own business agenda Presents cutting edge technology presentation with coverage of innovations such as the Internet of Things, robotics, 5G, edge/fog computing, blockchain, intelligent transport systems, and connected-automated mobility