Graph-Based Classification and Urban Modeling of Laser Scanning and Imagery: Toward 3D Smart Web Services

Recently, remotely sensed data obtained via laser technology has gained great importance due to its wide use in several fields, especially in 3D urban modeling. In fact, 3D city models in urban environments are efficiently employed in many fields, such as military operations, emergency management, building and height mapping, cadastral data upgrading, monitoring of changes as well as virtual reality. These applications are essentially composed of models of structures, urban elements, ground surface and vegetation. This paper presents a workflow for modeling the structure of buildings by using laser-scanned data (LiDAR) and multi-spectral images in order to develop a 3D web service for a smart city concept. Optical vertical photography is generally utilized to extract building class, while LiDAR data is used as a source of information to create the structure of the 3D building. The building reconstruction process presented in this study can be divided into four main stages: building LiDAR points extraction, piecewise horizontal roof clustering, boundaries extraction and 3D geometric modeling. Finally, an architecture for a 3D smart service based on the CityGML interchange format is proposed.

ANALYSIS OF METHODS FOR MODELING THE DEMAND FOR FREIGHT TRANSPORTATION TAKING INTO ACCOUNT THEIR CHARACTERISTICS

Existing models of demand for freight transportation are considered within the framework of various approaches. A review of approaches to urban modeling shows that the most commonly used metrics are: traffic flow, trip generation, vehicle load and commodity flow. Their advantages and disad-vantages are highlighted. To solve problems in the field of freight transport, scalable tools are need-ed that reflect as many attributes as possible at an acceptable cost in a given time period.

framework to generate virtual cities as sandboxes for land use-transport interaction models

One of the major critiques of land use-transport interaction (LUTI) models over the ages has been their over-dependence on individualized software and context. In an effort to address some of these concerns, this study proposes a framework to construct "virtual cities" that can act as sandboxes for testing different features of a LUTI model, as well as provide the capability to compare different LUTI models. We develop an approach to translate any prototypical transportation infrastructure network into a plausible land use zoning plan and synthetic population that are suitable for spatially detailed LUTI microsimulation of the virtual city. Disaggregate units of spatial geometry, like parcels and post codes, are generated using geospatial techniques applied to the transportation network. Households and jobs are randomly sampled from an actual city, and allocated in the virtual city based on matching density gradients. Students are matched with schools and workers are matched with jobs to complete the calibration of a synthetic population for the virtual city. Following the adjustment of behavioral models to complement the reduced scale of the virtual city, we demonstrate the integration between the land use and transportation simulation components in our LUTI model, SimMobility. The benefits of faster convergence times and shorter simulation times are clearly demonstrated through this exercise. We hope that this study, and the open-source releases of the SimMobility software with the virtual city database, can accelerate experimentation with LUTI models and aid the transition from individualized LUTI models to a common shared integrated urban modeling platform.

Digital Tools Aimed to Represent Urban Survey

Since the affirmation, in the Sixties, of urban survey as a discipline in the field of drawing disciplines, it aimed to represent on a plan a set of three-dimensional data. Digital revolution allows representing urban tissues by 3D models that can collect a lot of information related to the buildings, becoming real data base. Today, these models fulfill the need for update representations of urban settings, aimed both to critical studies on historical city and to manage the ongoing transformations. Moreover, these representations could become the urban scenarios for simulations and checks of master plans and architectural designs in their relationships with the built environment. Several new tools of urban procedural modeling, BIM modeling and web resources allow generating urban 3D models. The authors of this proposal will compare the knowledge and informative capabilities of different new technologies for urban modeling, through an overview of international researches, case studies, and also some experiences personally conducted.

Urban IoT: Advances, Challenges, and Opportunities for Mass Data Collection, Analysis, and Visualization

Urban Internet of Things (IoT) is in an early speculative phase. Often linked to the smart city movement, it provides a way of sensing and collecting data—environmental, societal, and transitional—both automatically, remotely, and with increasing levels of spatial and temporal detail. From city-wide data collection down to the scale of individual buildings and rooms, this chapter details the technology behind the rise of IoT in urban areas and explores the challenges (societal and technical) behind city-wide deployments. Drawing from a series of deployments at the Queen Elizabeth Olympic Park, London, it details the challenges and opportunities for mass data collection. Widening out the view, it looks at what is becoming known as “the humble lamp post” in Urban IoT fields to detail the potential of Urban IoT with the objects that already form part of the urban fabric. Finally, it examines the potential of Urban IoT for input into urban modeling and how we are on the edge of a shift in the collection, analysis, and communication of urban data.

Agent-Based Modeling and the City: A Gallery of Applications

Agent-based modeling is a powerful simulation technique that allows one to build artificial worlds and populate these worlds with individual agents. Each agent or actor has unique behaviors and rules which govern their interactions with each other and their environment. It is through these interactions that more macro-phenomena emerge: for example, how individual pedestrians lead to the emergence of crowds. Over the past two decades, with the growth of computational power and data, agent-based models have evolved into one of the main paradigms for urban modeling and for understanding the various processes which shape our cities. Agent-based models have been developed to explore a vast range of urban phenomena from that of micro-movement of pedestrians over seconds to that of urban growth over decades and many other issues in between. In this chapter, we introduce readers to agent-based modeling from simple abstract applications to those representing space utilizing geographical data not only for the creation of the artificial worlds but also for the validation and calibration of such models through a series of example applications. We will then discuss how big data, data mining, and machine learning techniques are advancing the field of agent-based modeling and demonstrate how such data and techniques can be leveraged into these models, giving us a new way to explore cities.