City2City: Translating Place Representations across Cities

Large mobility datasets collected from various sources have allowed us to observe, analyze, predict and solve a wide range of important urban challenges. In particular, studies have generated place representations (or embeddings) from mobility patterns in a similar manner to word embeddings to better understand the functionality of different places within a city. However, studies have been limited to generating such representations of cities in an individual manner and has lacked an inter-city perspective, which has made it difficult to transfer the insights gained from the place representations across different cities. In this study, we attempt to bridge this research gap by treating \textit{cities} and \textit{languages} analogously. We apply methods developed for unsupervised machine language translation tasks to translate place representations across different cities. Real world mobility data collected from mobile phone users in 2 cities in Japan are used to test our place representation translation methods. Translated place representations are validated using landuse data, and results show that our methods were able to accurately translate place representations from one city to another.Comment: A short 4-page version of this work was accepted in ACM SIGSPATIAL Conference 2019. This is the full version with details. In Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. AC

Data Informed Health Simulation Modeling

Combining reliable data with dynamic models can enhance the understanding of health-related phenomena. Smartphone sensor data characterizing discrete states is often suitable for analysis with machine learning classifiers. For dynamic models with continuous states, high-velocity data also serves an important role in model parameterization and calibration. Particle filtering (PF), combined with dynamic models, can support accurate recurrent estimation of continuous system state. This thesis explored these and related ideas with several case studies. The first employed multivariate Hidden Markov models (HMMs) to identify smoking intervals, using time-series of smartphone-based sensor data. Findings demonstrated that multivariate HMMs can achieve notable accuracy in classifying smoking state, with performance being strongly elevated by appropriate data conditioning. Reflecting the advantages of dynamic simulation models, this thesis has contributed two applications of articulated dynamic models: An agent-based model (ABM) of smoking and E-Cigarette use and a hybrid multi-scale model of diabetes in pregnancy (DIP). The ABM of smoking and E-Cigarette use, informed by cross-sectional data, supports investigations of smoking behavior change in light of the influence of social networks and E-Cigarette use. The DIP model was evidenced by both longitudinal and cross-sectional data, and is notable for its use of interwoven ABM, system dynamics (SD), and discrete event simulation elements to explore the interaction of risk factors, coupled dynamics of glycemia regulation, and intervention tradeoffs to address the growing incidence of DIP in the Australia Capital Territory. The final study applied PF with an SD model of mosquito development to estimate the underlying Culex mosquito population using various direct observations, including time series of weather-related factors and mosquito trap counts. The results demonstrate the effectiveness of PF in regrounding the states and evolving model parameters based on incoming observations. Using PF in the context of automated model calibration allows optimization of the values of parameters to markedly reduce model discrepancy. Collectively, the thesis demonstrates how characteristics and availability of data can influence model structure and scope, how dynamic model structure directly affects the ways that data can be used, and how advanced analysis methods for calibration and filtering can enhance model accuracy and versatility

Building a large-scale micro-simulation transport scenario using big data

A large-scale agent-based microsimulation scenario including the transport modes car, bus, bicycle, scooter, and pedestrian, is built and validated for the city of Bologna (Italy) during the morning peak hour. Large-scale microsimulations enable the evaluation of city-wide effects of novel and complex transport technologies and services, such as intelligent traffic lights or shared autonomous vehicles. Large-scale microsimulations can be seen as an interdisciplinary project where transport planners and technology developers can work together on the same scenario; big data from OpenStreetMap, traffic surveys, GPS traces, traffic counts and transit details are merged into a unique transport scenario. The employed activity-based demand model is able to simulate and evaluate door-to-door trip times while testing different mobility strategies. Indeed, a utility-based mode choice model is calibrated that matches the official modal split. The scenario is implemented and analyzed with the software SUMOPy/SUMO which is an open source software, available on GitHub. The simulated traffic flows are compared with flows from traffic counters using different indicators. The determination coefficient has been 0.7 for larger roads (width greater than seven meters). The present work shows that it is possible to build realistic microsimulation scenarios for larger urban areas. A higher precision of the results could be achieved by using more coherent data and by merging different data sources

Recent Progress in Activity-Based Travel Demand Modeling: Rising Data and Applicability

Over 30 years have passed since activity-based travel demand models (ABMs) emerged to overcome the limitations of the preceding models which have dominated the field for over 50 years. Activity-based models are valuable tools for transportation planning and analysis, detailing the tour and mode-restricted nature of the household and individual travel choices. Nevertheless, no single approach has emerged as a dominant method, and research continues to improve ABM features to make them more accurate, robust, and practical. This paper describes the state of art and practice, including the ongoing ABM research covering both demand and supply considerations. Despite the substantial developments, ABM’s abilities in reflecting behavioral realism are still limited. Possible solutions to address this issue include increasing the inaccuracy of the primary data, improved integrity of ABMs across days of the week, and tackling the uncertainty via integrating demand and supply. Opportunities exist to test, the feasibility of spatial transferability of ABMs to new geographical contexts along with expanding the applicability of ABMs in transportation policy-making

Urban Informatics

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

Urban Informatics

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

Staging urban emergence through collective creativity: Devising an outdoor mobile augmented reality tool

The unpredictability of global geopolitical conflicts, economic trends, and impacts of climate change, coupled with an increasing urban population, necessitates a more profound commitment to resilience thinking in urban planning and design. In contrast to top-down planning and designing for sustainability, allowing for emergence to take place seems to contribute to a capacity to better deal with this complex unpredictability, by allowing incremental changes through bottom-up, self-organized adaptation made by diverse actors in the proximity of various social, economical and functional entities in the urban context.The present thesis looks into the processes of creating urban emergence from both theoretical and practical perspectives. The theoretical section of the thesis first looks into the relationship between the processes and the qualities of a compact city. The Japanese city of Tokyo is used as an example of a resilient compact city that continuously emerges through incremental micro-adaptations by individual actors guided by urban rules that ‘let it happen’ without much central control or top-down design of the individual outcomes. The thesis then connects such rule-based emergent processes and the qualities of a compact city to complex adaptive system’s (CAS) theory, emphasizing the value of incremental and individual multiple-stakeholder input. The latter part of the thesis focuses on how to create a platform that can combine the bottom-up, emergent, rule-based planning approaches, and collective creativity based on individual participation and input from the public. This section is dedicated to developing a tool for a collaborative urban design using outdoor mobile augmented reality (MAR) by research-through-design method.The thesis thus has three parts addressing the topics: 1. urban planning processes and resulting urban qualities concerning compact city – i.e., density and diversity; 2. the processes of urban emergence, which generates complexity that renders urban resilience from the urban planning theory perspective; 3. developing a tool for non-expert citizens and other stakeholders to design and visualize an urban neighborhood by simulating the rule-based urban emergence using outdoor MAR. The results include a proposal for a complementary hybrid planning approaches that might approximate the CAS in urban systems with qualities that contribute to urban resiliency. Thereafter, the results describe specifications and design criteria for a tool as a public collaborative design platform using outdoor MAR to promote public participation: Urban CoBuilder. The processes of developing and prototyping such a tool to test various urban concepts concerning identified adaptive urban planning approaches are also presented with an assessment of the MAR tool based on focus group user tests. Future studies need to better include the potential of crowdsourcing public creativity through mass participation using the collaborative design tool and actual integration of these participatory design results in urban policies

Sensor-Driven, Spatially Explicit Agent-Based Models

Conventionally, agent-based models (ABMs) are specified from well-established theory about the systems under investigation. For such models, data is only introduced to ensure the validity of the specified models. In cases where the underlying mechanisms of the system of interest are unknown, rich datasets about the system can reveal patterns and processes of the systems. Sensors have become ubiquitous allowing researchers to capture precise characteristics of entities in both time and space. The combination of data from in situ sensors to geospatial outputs provides a rich resource for characterising geospatial environments and entities on earth. More importantly, the sensor data can capture behaviours and interactions of entities allowing us to visualise emerging patterns from the interactions. However, there is a paucity of standardised methods for the integration of dynamic sensor data streams into ABMs. Further, only few models have attempted to incorporate spatial and temporal data dynamically from sensors for model specification, calibration and validation. This chapter documents the state of the art of methods for bridging the gap between sensor data observations and specification of accurate spatially explicit agent-based models. In addition, this work proposes a conceptual framework for dynamic validation of sensor-driven spatial ABMs to address the risk of model overfitting

Urban Informatics

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