Development of Geospatial Models for Multi-Criteria Decision Making in Traffic Environmental Impacts of Heavy Vehicle Freight Transportation

Heavy vehicle freight transportation is one of the primary contributors to the socio-economic development, but it has great influence on traffic environment. To comprehensively and more accurately quantify the impacts of heavy vehicles on road infrastructure performance, a series of geospatial models are developed for both geographically global and local assessment of the impacts. The outcomes are applied in flexible multi-criteria decision making for the industrial practice of road maintenance and management

Making the third dimension (3D) explicit in hedonic price modelling : A case study of Xi’an, China

Recent rapid population growth and increasing urbanisation have led to fast vertical developments in urban areas. Therefore, in the context of the dynamic property market, factors related to the third dimension (3D) need to be considered. Current hedonic price modelling (HPM) studies have little explicit consideration for the third dimension, which may have a significant influence on modelling property values in complex urban environments. Therefore, our research aims to narrow the cognitive gap of the missing third dimension by assessing both 2D and 3D HPM and identifying important 3D factors for spatial analysis and visualisation in the selected study area, Xi’an, China. The statistical methods we used for 2D HPM are ordinary least squares (OLS) and geographically weighted regression (GWR). In 2D HPM, they both have very low R2 (0.111 in OLS and 0.217 in GWR), showing a very limited generalisation potential. However, a significant improvement is observed when adding 3D factors, namely view quality, sky view factor (SVF), sunlight and property orientation. The obtained higher R2 (0.414) shows the importance of the third dimension or—3D factors for HPM. Our findings demonstrate the necessity to include such factors into HPM and to develop 3D models with a higher level of details (LoD) to serve more purposes such as fair property taxation. © 2020 by the authors. Li-censee MDPI, Basel, Switzerland

Towards a semantic Construction Digital Twin: directions for future research

As the Architecture, Engineering and Construction sector is embracing the digital age, the processes involved in the design, construction and operation of built assets are more and more influenced by technologies dealing with value-added monitoring of data from sensor networks, management of this data in secure and resilient storage systems underpinned by semantic models, as well as the simulation and optimisation of engineering systems. Aside from enhancing the efficiency of the value chain, such information-intensive models and associated technologies play a decisive role in minimising the lifecycle impacts of our buildings. While Building Information Modelling provides procedures, technologies and data schemas enabling a standardised semantic representation of building components and systems, the concept of a Digital Twin conveys a more holistic socio-technical and process-oriented characterisation of the complex artefacts involved by leveraging the synchronicity of the cyber-physical bi-directional data flows. Moreover, BIM lacks semantic completeness in areas such as control systems, including sensor networks, social systems, and urban artefacts beyond the scope of buildings, thus requiring a holistic, scalable semantic approach that factors in dynamic data at different levels. The paper reviews the multi-faceted applications of BIM during the construction stage and highlights limits and requirements, paving the way to the concept of a Construction Digital Twin. A definition of such a concept is then given, described in terms of underpinning research themes, while elaborating on areas for future research

Revisiting Urban Dynamics through Social Urban Data:

The study of dynamic spatial and social phenomena in cities has evolved rapidly in the recent years, yielding new insights into urban dynamics. This evolution is strongly related to the emergence of new sources of data for cities (e.g. sensors, mobile phones, online social media etc.), which have potential to capture dimensions of social and geographic systems that are difficult to detect in traditional urban data (e.g. census data). However, as the available sources increase in number, the produced datasets increase in diversity. Besides heterogeneity, emerging social urban data are also characterized by multidimensionality. The latter means that the information they contain may simultaneously address spatial, social, temporal, and topical attributes of people and places. Therefore, integration and geospatial (statistical) analysis of multidimensional data remain a challenge. The question which, then, arises is how to integrate heterogeneous and multidimensional social urban data into the analysis of human activity dynamics in cities? To address the above challenge, this thesis proposes the design of a framework of novel methods and tools for the integration, visualization, and exploratory analysis of large-scale and heterogeneous social urban data to facilitate the understanding of urban dynamics. The research focuses particularly on the spatiotemporal dynamics of human activity in cities, as inferred from different sources of social urban data. The main objective is to provide new means to enable the incorporation of heterogeneous social urban data into city analytics, and to explore the influence of emerging data sources on the understanding of cities and their dynamics.  In mitigating the various heterogeneities, a methodology for the transformation of heterogeneous data for cities into multidimensional linked urban data is, therefore, designed. The methodology follows an ontology-based data integration approach and accommodates a variety of semantic (web) and linked data technologies. A use case of data interlinkage is used as a demonstrator of the proposed methodology. The use case employs nine real-world large-scale spatiotemporal data sets from three public transportation organizations, covering the entire public transport network of the city of Athens, Greece.  To further encourage the consumption of linked urban data by planners and policy-makers, a set of webbased tools for the visual representation of ontologies and linked data is designed and developed. The tools – comprising the OSMoSys framework – provide graphical user interfaces for the visual representation, browsing, and interactive exploration of both ontologies and linked urban data.   After introducing methods and tools for data integration, visual exploration of linked urban data, and derivation of various attributes of people and places from different social urban data, it is examined how they can all be combined into a single platform. To achieve this, a novel web-based system (coined SocialGlass) for the visualization and exploratory analysis of human activity dynamics is designed. The system combines data from various geo-enabled social media (i.e. Twitter, Instagram, Sina Weibo) and LBSNs (i.e. Foursquare), sensor networks (i.e. GPS trackers, Wi-Fi cameras), and conventional socioeconomic urban records, but also has the potential to employ custom datasets from other sources. A real-world case study is used as a demonstrator of the capacities of the proposed web-based system in the study of urban dynamics. The case study explores the potential impact of a city-scale event (i.e. the Amsterdam Light festival 2015) on the activity and movement patterns of different social categories (i.e. residents, non-residents, foreign tourists), as compared to their daily and hourly routines in the periods  before and after the event. The aim of the case study is twofold. First, to assess the potential and limitations of the proposed system and, second, to investigate how different sources of social urban data could influence the understanding of urban dynamics. The contribution of this doctoral thesis is the design and development of a framework of novel methods and tools that enables the fusion of heterogeneous multidimensional data for cities. The framework could foster planners, researchers, and policy makers to capitalize on the new possibilities given by emerging social urban data. Having a deep understanding of the spatiotemporal dynamics of cities and, especially of the activity and movement behavior of people, is expected to play a crucial role in addressing the challenges of rapid urbanization. Overall, the framework proposed by this research has potential to open avenues of quantitative explorations of urban dynamics, contributing to the development of a new science of cities

Revisiting Urban Dynamics through Social Urban Data

The study of dynamic spatial and social phenomena in cities has evolved rapidly in the recent years, yielding new insights into urban dynamics. This evolution is strongly related to the emergence of new sources of data for cities (e.g. sensors, mobile phones, online social media etc.), which have potential to capture dimensions of social and geographic systems that are difficult to detect in traditional urban data (e.g. census data). However, as the available sources increase in number, the produced datasets increase in diversity. Besides heterogeneity, emerging social urban data are also characterized by multidimensionality. The latter means that the information they contain may simultaneously address spatial, social, temporal, and topical attributes of people and places. Therefore, integration and geospatial (statistical) analysis of multidimensional data remain a challenge. The question which, then, arises is how to integrate heterogeneous and multidimensional social urban data into the analysis of human activity dynamics in cities?  To address the above challenge, this thesis proposes the design of a framework of novel methods and tools for the integration, visualization, and exploratory analysis of large-scale and heterogeneous social urban data to facilitate the understanding of urban dynamics. The research focuses particularly on the spatiotemporal dynamics of human activity in cities, as inferred from different sources of social urban data. The main objective is to provide new means to enable the incorporation of heterogeneous social urban data into city analytics, and to explore the influence of emerging data sources on the understanding of cities and their dynamics.  In mitigating the various heterogeneities, a methodology for the transformation of heterogeneous data for cities into multidimensional linked urban data is, therefore, designed. The methodology follows an ontology-based data integration approach and accommodates a variety of semantic (web) and linked data technologies. A use case of data interlinkage is used as a demonstrator of the proposed methodology. The use case employs nine real-world large-scale spatiotemporal data sets from three public transportation organizations, covering the entire public transport network of the city of Athens, Greece.  To further encourage the consumption of linked urban data by planners and policy-makers, a set of webbased tools for the visual representation of ontologies and linked data is designed and developed. The tools – comprising the OSMoSys framework – provide graphical user interfaces for the visual representation, browsing, and interactive exploration of both ontologies and linked urban data.  After introducing methods and tools for data integration, visual exploration of linked urban data, and derivation of various attributes of people and places from different social urban data, it is examined how they can all be combined into a single platform. To achieve this, a novel web-based system (coined SocialGlass) for the visualization and exploratory analysis of human activity dynamics is designed. The system combines data from various geo-enabled social media (i.e. Twitter, Instagram, Sina Weibo) and LBSNs (i.e. Foursquare), sensor networks (i.e. GPS trackers, Wi-Fi cameras), and conventional socioeconomic urban records, but also has the potential to employ custom datasets from other sources.  A real-world case study is used as a demonstrator of the capacities of the proposed web-based system in the study of urban dynamics. The case study explores the potential impact of a city-scale event (i.e. the Amsterdam Light festival 2015) on the activity and movement patterns of different social categories (i.e. residents, non-residents, foreign tourists), as compared to their daily and hourly routines in the periods  before and after the event. The aim of the case study is twofold. First, to assess the potential and limitations of the proposed system and, second, to investigate how different sources of social urban data could influence the understanding of urban dynamics.  The contribution of this doctoral thesis is the design and development of a framework of novel methods and tools that enables the fusion of heterogeneous multidimensional data for cities. The framework could foster planners, researchers, and policy makers to capitalize on the new possibilities given by emerging social urban data. Having a deep understanding of the spatiotemporal dynamics of cities and, especially of the activity and movement behavior of people, is expected to play a crucial role in addressing the challenges of rapid urbanization. Overall, the framework proposed by this research has potential to open avenues of quantitative explorations of urban dynamics, contributing to the development of a new science of cities

A Process-Based Approach for Integrating the Last Planner System In 4D Modeling for Equipment Workspace Planning in Elevated Urban Highway

Transportation developments are shifting from the construction of new highways to the reconstruction of existing ones, especially in urban areas. The reconstruction of elevated urban highways typically requires substantial capital investments and long durations. The prevalence of non-value adding activities otherwise referred to as non-physical wastes according to the Lean Construction (LC) paradigm is one attributable reason for this. Another feature of urban highway projects is the use of heavy construction equipment. Planning the equipment workspace becomes very important to facilitate the reduction/elimination of non-physical wastes and ensure no delays to the project completion arising from spatio-temporal conflicts. Four-dimensional (4D) modelling techniques have proven benefits to effective construction planning. Still, some limitations exist in the lack of a practical approach to support construction planning and incorporate workspace modelling in the 4D model development process. Several studies with different perspectives have been carried out to describe the gains of using 4D models in workspace management. However, none of them considered the effects of the limited usable space in the reconstruction of elevated urban highways. Moreover, the requirements for multiple levels of detail (LOD) in scheduling large and complex projects present a new challenge. To counter these challenges, a considerable amount of time is required to ensure that the LOD of the 4D model is sufficient to account for the following: (1) micro-scheduling of heavy equipment typically used in these types of operations, and (2) producing a 4D model with a sufficient LOD to accommodate daily work plans. The purpose of this study is to categorize and prioritize factors contributing to non-physical wastes using empirical data obtained from a questionnaire survey. The survey results identified "planning" as an important factor in promoting non-physical wastes in elevated urban highway projects. A hybrid Multi-Criteria Decision Making (MCDM) approach was proposed to formalize selecting project planning/scheduling methods applicable to elevated urban highway projects where micro-scheduling short duration activities involving heavy construction equipment is critical to project success. Equipment workspace planning was considered a vital aspect in the planning process as conventional planning methods fail to consider spatial planning for short duration activities, especially in highway projects. To facilitate the equipment workspace planning, a research initiative that involved developing a detailed 4D model by integrating the Last Planner System (LPS), a LC planning and scheduling technique in a 4D model with multiple LOD's was proposed. The development of this 4D model can help facilitate the reduction of non-physical wastes during the construction phase of elevated urban highways, improve the reliability of the planning process, and reduce the time waste associated with planning and scheduling urban highway projects subject to space constraints. The research method is described, and a case study is developed to demonstrate the proposed method's feasibility

Assessment of portable and miniaturized sensors for the monitoring of human exposure to air pollutants

In the last years, several in-field campaigns have been conducted using portable and miniaturized monitors to evaluate the personal exposure to different pollutants. In general, this kind of monitors are characterized by worse metrological performance if compared to the traditional standard methods. Despite this disadvantage, portable and miniaturized monitors could be easily used across different applications, because their advantageous features, such as the capability to provide real-time measurement, the high spatial and temporal resolution of acquired data, the ability to adapt to different experimental designs and, especially, the ability to follow the subject in any activity. Finally, portable and miniaturized instruments can provide data acquired in the respiratory zone of the subject, following therefore the practices for a correct exposure assessment. Obviously, the best compromise between the analytical gold standard (in terms of precision, accuracy and instrumental sensitivity) and the gold standard in regard to the exposure assessment should be chosen. Therefore, in brief, principal aims of this thesis are (i) to evaluate the on-field performances of portable and miniaturized monitors for gaseous pollutants and airborne PM and (ii) to use these monitors in exposure assessment studies and (iii) to understand if data acquired via portable and miniaturized monitors could be useful in other fields of application, such as epidemiological studies or toxicological studies, in which the evaluation of the inhaled dose of pollutants could play a key role

Doctor of Philosophy

dissertationRecent advancements in mobile devices - such as Global Positioning System (GPS), cellular phones, car navigation system, and radio-frequency identification (RFID) - have greatly influenced the nature and volume of data about individual-based movement in space and time. Due to the prevalence of mobile devices, vast amounts of mobile objects data are being produced and stored in databases, overwhelming the capacity of traditional spatial analytical methods. There is a growing need for discovering unexpected patterns, trends, and relationships that are hidden in the massive mobile objects data. Geographic visualization (GVis) and knowledge discovery in databases (KDD) are two major research fields that are associated with knowledge discovery and construction. Their major research challenges are the integration of GVis and KDD, enhancing the ability to handle large volume mobile objects data, and high interactivity between the computer and users of GVis and KDD tools. This dissertation proposes a visualization toolkit to enable highly interactive visual data exploration for mobile objects datasets. Vector algebraic representation and online analytical processing (OLAP) are utilized for managing and querying the mobile object data to accomplish high interactivity of the visualization tool. In addition, reconstructing trajectories at user-defined levels of temporal granularity with time aggregation methods allows exploration of the individual objects at different levels of movement generality. At a given level of generality, individual paths can be combined into synthetic summary paths based on three similarity measures, namely, locational similarity, directional similarity, and geometric similarity functions. A visualization toolkit based on the space-time cube concept exploits these functionalities to create a user-interactive environment for exploring mobile objects data. Furthermore, the characteristics of visualized trajectories are exported to be utilized for data mining, which leads to the integration of GVis and KDD. Case studies using three movement datasets (personal travel data survey in Lexington, Kentucky, wild chicken movement data in Thailand, and self-tracking data in Utah) demonstrate the potential of the system to extract meaningful patterns from the otherwise difficult to comprehend collections of space-time trajectories

4D Simulation of Capital Construction Projects: Levels of Development and Ontology for Delay Claims Applications

4D simulation is commonly used in building construction projects as part of Building Information Modeling (BIM) processes. A construction project progresses through different phases. At each of these phases, the project schedules and 3D models have various levels of development (LODs) ranging from summarized to detailed models. Therefore, 4D simulation should consider multiple LODs. However, the literature does not define 4D-LODs adequately. On the other hand, there is limited research related to the visualization of complex delay claims using 4D simulation. Moreover, although BIM, 4D simulation, Delay Effects and Causes (DEC), and claims are knowledge domains with active research in the construction industry, there is a gap in integrating these domains in a more formal and overarching ontology-based approach to link essential concepts such as liability, causality and quantum in a delay claim using 4D simulation. The long-term goal of this thesis is to propose a systematic approach for the development of 4D simulation to fulfill the needs of different applications focusing on the area of delay claims. The thesis has the following specific objectives: (1) Providing a guideline about 4D-LODs definitions that are based on needs and project progress; (2) Introducing a formal method for developing 4D simulation of capital construction projects considering different time horizons; (3) Investigating the current usage, efficiency and value of 4D simulation in construction delay claims and applications such as analyzing delay DEC and assigning responsibilities; (4) Developing a multidisciplinary ontology for linking delay claims with 4D simulation to analyze DEC and responsibilities; and (5) Developing a method for delay claim visualization and analysis using 4D simulation. The selection of the suitable 4D-LOD based on the proposed guideline enables an effective simulation considering the needs of the project and the available information. The proposed 4D-LODs are useful in identifying the different representations of workspaces created at each LOD. Furthermore, the proposed 4D simulation development method is efficient and useful for project owners and contractors to streamline the simulation process by focusing on needs. This method has been applied in several large-scale projects, and resulted in reducing project cost and duration by quickly identifying feasible scenarios, as well as avoiding claims and minimizing site conflicts. A survey has been conducted to understand the potential applications of 4D simulation in forensic investigation of delay claims in construction projects. The results of the survey show that 4D simulation is efficient for all roles involved in delay claims negotiations and litigations including judges, lawyers, experts and witnesses. However, 4D simulation would provide more benefits if it is required in the contract. 4D simulation can facilitate the identification, visualization, quantification and responsibility assignment of delay events by identifying spatio-temporal conflicts and generating a better collaboration environment for finding appropriate mitigation measures. Finally, an ontology (called Claim4D-Onto) has been developed for linking delay claims with 4D simulation to analyze effects-causes and responsibilities. Claim4D-Onto has been validated with legal experts and delay claims professionals considering the criteria of clarity and completeness. Claim4D-Onto can facilitate a systematic and clear representation of the DEC and responsibilities in 4D simulation for delay claims management and avoidance. Using the concepts of Claim4D-Onto, it has been demonstrated that visual analytics based on 4D simulation can clarify the causality and analyze delay responsibilities and entitlements as a complementary tool to the cause-effect matrix. The main contributions developed in the context of this thesis are: (1) Defining 4D-LODs with a guideline based on the available information and needs; (2) Introducing the development of 4D simulation with a formal method considering different time horizons; (3) Identifying the efficiency and value of 4D simulation in construction claims as a tool for supporting legal arguments, stakeholder’s viewpoints and interrogatory considerations; (4) Developing a visualization method to facilitate the identification and quantification of events in delay claims using 4D simulation; (5) Developing a multidisciplinary ontology (Claim4D-Onto) for linking delay claims with 4D simulation; and (6) Extending the benefits of 4D simulation in the area of delay claims with visual analytics of DEC and responsibilities