Decision Support Instrument Research Articles

Multi-pressure based environmental vulnerability assessment in a coastal area of Bangladesh: A case study on Cox’s Bazar

Bangladesh ranks among the top 10 countries globally in terms of climate change impacts and faces numerous anthropogenic and natural pressures. Cox’s Bazar, its primary tourist district, is experiencing severe degradation of its physical and ecological environments due to anthropogenic disturbances and climate change. To improve its environmental quality and preserve its ecological resources effectively, it is essential to develop a spatial decision support instrument addressing multi-pressures and cumulative environmental vulnerability (EV). This study presents an expert opinion-independent, scalable, and customizable spatial methodological framework, integrating multi-sourced geospatial data with GIS-based Fuzzy Logic to assess spatial distributions of five pressure groups and their resulting EV in Cox’s Bazar. 18 criteria were chosen based on a structured literature review to evaluate the five pressure groups. Results revealed that 17 % to 27 % of the study area is exposed to high to very high hydro-meteorological, topographic, land resource, anthropogenic, and natural hazard pressures. The EV results indicated that one-third of the study area, majorly covering Kutubdia, Pekua, Cox’s Bazar Sadar, Teknaf, and Ukhia upazilas, is highly environmentally vulnerable. For enhanced environmental protection, this study improved the existing method of environmental protection zoning by introducing a novel zoning approach that integrates in-situ biodiversity data with EV data. This new zoning method delineated 24 % (555 sq. km.) as strict, 45 % (1047 sq. km.) as medium, and 31 % (725 sq. km.) as soft protection zones in the study area. The sensitivity analysis identified land resource pressure as the most influential component of EV. With a correlation coefficient of 0.91, the accuracy assessment confirms a high level of reliability in the EV results. This study provides valuable insights into environmental pressures and vulnerability in Cox’s Bazar, which are crucial for informing policies at various levels, including international and national frameworks, emphasizing terrestrial ecosystem protection, coastal vulnerability mitigation, climate change impact reduction, biodiversity preservation, and sustainable land resource management.

A framework for validating watershed ecosystem service models in the United States using long-term water quality data: Applications with the InVEST Nutrient Delivery (NDR) model in Puerto Rico

Modeling of watershed Ecosystem Services (ES) processes has increased greatly in recent years, potentially improving environmental management and decision-making by describing the value of nature. ES models may be sensitive to different conditions and, therefore, should ideally be validated against observed data for their use as a decision-support instrument. However, outcomes from such ES modeling are barely validated, making it difficult to assess uncertainties associated with the modeling and justify their actual usefulness to develop generalizable management recommendations. This study proposes a framework for the systematic validation of one of such tools, the InVEST Nutrient Delivery Model (NDR) for nutrient retention estimates. The framework is divided into three stages: 1) running the NDR model inputs, processes, and outputs; 2) building a long-term reference dataset from open access water quality observations; and 3) using the reference data for model calibration and validation. We applied this framework to twenty watersheds in the Commonwealth of Puerto Rico, where data availability resembles thar of watersheds across the United States. Long-term water quality data from monitoring stations facilitated model calibration and validation. Our framework provided a reproducible method to linking the vast monitoring network in the U.S. and its territories for evaluating the InVEST's NDR model performance. Beyond the framework development, this study found that the InVEST NDR model explained 62.7 % and 79.3 % of the variance in the total nitrogen and total phosphorus between 2000 and 2022, respectively, supporting the suitability of the model for watershed scale ecosystem services assessment. The findings can also serve as a reference to support the use of InVEST for other locations in the tropics without publically available monitoring data.

The defining features of a robust information security climate

Data breaches have become a common occurrence with serious consequences, making organizational security management critically important. Nonetheless, the research community has not yet clearly defined the characteristics of a strong organizational security climate. This study conducts a comprehensive literature analysis to identify a collection of research-based and managerially relevant constructs that represent the essential components of a strong security climate. We operationalize the identified measures of the constructs and empirically validate them for reliability, construct validity, and nomological validity in terms of their relationships with employees' security awareness, neutralization, and intention to comply with organizational information security policies (ISPs). The results suggest that these integral elements, when embraced by organizations, discourage employees’ use of neutralization to justify violation of ISPs and improve employees’ security awareness and their ISP compliance intention. Organizations can use the identified subcomponents and their corresponding measures as a diagnostic decision support instrument to make a reliable and valid assessment of the strengths and weaknesses of their security climate, to continuously monitor their security climate, and to develop interventions that contribute to a strong security climate.

Development and validation of a quantitative Proximity Extension Assay instrument with 21 proteins associated with cardiovascular risk (CVD-21).

Treatment of cardiovascular diseases (CVD) is a substantial burden to healthcare systems worldwide. New tools are needed to improve precision of treatment by optimizing the balance between efficacy, safety, and cost. We developed a high-throughput multi-marker decision support instrument which simultaneously quantifies proteins associated with CVD. Candidate proteins independently associated with different clinical outcomes were selected from clinical studies by the screening of 368 circulating biomarkers. We then custom-designed a quantitative PEA-panel with 21 proteins (CVD-21) by including recombinant antigens as calibrator samples for normalization and absolute quantification of the proteins. The utility of the CVD-21 tool was evaluated in plasma samples from a case-control cohort of 4224 patients with chronic coronary syndrome (CCS) using multivariable Cox regression analyses and machine learning techniques. The assays in the CVD-21 tool gave good precision and high sensitivity with lower level of determination (LOD) between 0.03-0.7 pg/ml for five of the biomarkers. The dynamic range for the assays was sufficient to accurately quantify the biomarkers in the validation study except for troponin I, which in the modeling was replaced by high-sensitive cardiac troponin T (hs-TnT). We created seven different multimarker models, including a reference model with NT-proBNP, hs-TnT, GDF-15, IL-6, and cystatin C and one model with only clinical variables, for the comparison of the discriminative value of the CVD-21 tool. All models with biomarkers including hs-TnT provided similar discrimination for all outcomes, e.g. c-index between 0.68-0.86 and outperformed models using only clinical variables. Most important prognostic biomarkers were MMP-12, U-PAR, REN, VEGF-D, FGF-23, TFF3, ADM, and SCF. The CVD-21 tool is the very first instrument which with PEA simultaneously quantifies 21 proteins with associations to different CVD. Novel pathophysiologic and prognostic information beyond that of established biomarkers were identified by a number of proteins.

Moroccan Public Buildings and the RTCM: Insights into Compliance, Energy Performance, and Regulation Improvement

This study investigates the compliance of Moroccan public buildings with the thermal regulation (RTCM). It analysis public building envelope typology, the conformity and the impact of RTCM conformity on energy performance across the six climatic zones defined by the RTCM. The outcomes of this research may serve as a decision-support instrument by identifying areas where the thermal regulation is already validated and the potential impact that a public building could undergo by implementing these provisions. Additionally, this study can be viewed as a crucial analysis contributing to the enhancement of the existing regulation. The study emphasizes various extraneous stipulations present within the prevailing regulation. In this work, we have attempted to categorize these stipulations into two distinct groups: recommendations for refinement to be integrated into the regulatory framework, and essential measures to ensure successful implementation of the regulation in the realm of building energy efficiency.

The current role of management accounting: paradigm shift and future challenges

PurposeThis paper aims to map the research on management accounting (MA), clarifying its current role and identifying gaps and opportunities for future research.Design/methodology/approachIn this paper, 784 papers were reviewed for the 1958–2019 period, published in 220 scientific journals indexed on Clarivate Analytics’ Web of Science (Science Citation Index Expanded [SCI-EXPANDED] and Social Sciences Citation Index [SSCI]). In the process, content analysis, regression analysis and bibliometric analysis were used.FindingsThe most relevant journals, authors and topics in MA, along with trends and patterns in the literature, were identified. Seven clusters that represent the overall thematic research structure of the MA field were also identified. This study shows that MA is becoming a multidimensional management decision-support instrument covering all organizational dimensions. As such, the research on MA is following the recent concerns with the sustainable development and digitalization of business processes.Research limitations/implicationsBased on the findings of this research study, theoretical and practical implications for MA researchers were provided. These findings could also be useful to industry practitioners to improve their knowledge of emerging trends in MA practices, strategies and concepts.Originality/valueBased on bibliometric and content analysis, a framework that shows an organizational, market and social context for the evolution of MA over the past 60 years was provided. It highlights the dynamics of MA alignment with organizational and external environment changes. Future research opportunities and implications for researchers and practitioners were also identified.

Deep learning-assisted differentiation of pathologically proven atypical and typical hepatocellular carcinoma (HCC) versus non-HCC on contrast-enhanced MRI of the liver.

To train a deep learning model to differentiate between pathologically proven hepatocellular carcinoma (HCC) and non-HCC lesions including lesions with atypical imaging featureson MRI. This IRB-approved retrospective study included 118 patients with 150 lesions (93 (62%) HCC and 57 (38%) non-HCC) pathologically confirmed through biopsies (n = 72), resections (n = 29), liver transplants (n = 46), and autopsies (n = 3). Forty-seven percent of HCC lesions showed atypical imaging features (not meeting Liver Imaging Reporting and Data System [LI-RADS] criteria for definitive HCC/LR5). A 3D convolutional neural network (CNN) was trained on 140 lesions and tested for its ability to classify the 10 remaining lesions (5 HCC/5 non-HCC). Performance of the model was averaged over 150 runs with random sub-sampling to provide class-balanced test sets. A lesion grading system was developed to demonstrate the similarity between atypical HCC and non-HCC lesions prone to misclassification by the CNN. The CNN demonstrated an overall accuracy of 87.3%. Sensitivities/specificities for HCC and non-HCC lesions were 92.7%/82.0% and 82.0%/92.7%, respectively. The area under the receiver operating curve was 0.912. CNN's performance was correlated with the lesion grading system, becoming less accurate the more atypical imaging features the lesions showed. This study provides proof-of-concept for CNN-based classification of both typical- and atypical-appearing HCC lesions on multi-phasic MRI, utilizing pathologically confirmed lesions as "ground truth." • A CNN trained on atypical appearing pathologically proven HCC lesions not meeting LI-RADS criteria for definitive HCC (LR5) can correctly differentiate HCC lesions from other liver malignancies, potentially expanding the role of image-based diagnosis in primary liver cancer with atypical features. • The trained CNN demonstrated an overall accuracy of 87.3% and a computational time of < 3ms which paves the way for clinical application as a decision support instrument.

Evaluating Environmental Impact Assessment report quality in South African national parks

This research evaluates Environmental Impact Assessment (EIA) report quality for a selected sample of development projects in South African national parks. It applies an adapted version of an international EIA report quality review package to 24 developments within 10 national parks, across three EIA regimes. The results suggest good EIA report quality across all four quality review areas, with improvement over time, but also highlight particular weaknesses in terms of dealing with waste and, to a lesser extent, with significance and mitigation. To build on this research, the development of a sector-specific EIA report quality review package is recommended, with more emphasis on the strategic context, waste and water-related aspects. The conclusion is that EIA is well positioned to remain an important decision support instrument for developments within national parks. Conservation implications: The results show that EIA reports for developments in South African national parks are generally of sufficient quality for decision-making that benefits conservation. However, weaker performance regarding waste, significance determination, water-related impacts and a lack of consideration of strategic context requires a conservation-specific EIA report quality review package to improve report quality in the areas of weakness and thereby increase the value of EIA as an instrument for environmental governance and sustainable development in conservation areas.

How the public imagines the energy future: Exploring and clustering non-experts' techno-economic expectations towards the future energy system.

Various countries have pledged to carry out system-wide energy transitions to address climate change. This requires taking strategic decisions with long-term consequences under conditions of considerable uncertainty. For this reason, many actors in the energy sector develop model-based scenarios to guide debates and decision-making about plausible future energy systems. Besides being a decision support instrument for policy-makers, energy scenarios are widely recognized as a way of shaping the expectations of experts and of influencing energy policy more generally. However, relatively little is known about how energy scenarios shape preferences and expectations of the public. We use an explorative research design to assess the publics' expectations of future energy systems through an online survey among Swiss residents (N = 797). We identified four significantly different clusters of people with distinct expectations about the future energy system, each seeing different implications for the acceptability of energy policies and the compatibility with projections of techno-economic energy scenarios. Cluster 1 expects a system-wide energy transition towards renewable energy sources that is similar to the policy-relevant national energy scenario. Cluster 2 also expects an energy transition, but believes it will lead to a range of technical challenges, societal conflicts and controversies with neighboring countries. Cluster 3 is the only cluster not expecting significant changes in the future energy system and thus not anticipating an energy transition. Cluster 4's expectations are between cluster 1 and 2, but it anticipates a huge increase in per capita electricity demand while prices are expected to remain low. The study at hand offers some initial insights into the interdependencies between energy transition pathways outlined in techno-economic energy scenarios and the energy system expectations of the public. These insights are essential for gaining a better understanding of whether and how energy scenarios can contribute to informed public debates about energy futures and how desirable pathways towards them might look like.

Application of Prediction Markets Phenomenon as Decision Support Instrument in Vehicle Recycling Sector

Application of Prediction Markets Phenomenon as Decision Support Instrument in Vehicle Recycling Sector

Digitally forecasting new music product success via active crowdsourcing

Deciding which artist or song to sign and promote has always been a challenge for recording companies, especially when it comes to innovative newcomer singers without any chart history. However, the specifics of a creative industry such as the hedonic nature of music, socio-network effects, and ever fastening fashion cycles in combination with digitalization have made the recording industry even more competitive and these initial decisions even more crucial. With respect to the ongoing digital transformation and shift in power from organizations to consumers, we leverage digitally mediated wisdom of the crowd to build a forecasting model for better understanding chart success. Therefore, we draw on the literature of hedonic and experiential goods to investigate the relationship between crowd evaluations based on listening experience and popular music chart success. We track 150 song positions in reported music charts and also evaluate these songs via the crowd. Our model indicates that the wisdom of the crowd can improve forecasting chart success by almost 30% relatively to factors that have been earlier identified in the literature. However, this forecasting relevance is bound to certain conditions, namely the composition of the crowd, the underlying chart and market mechanisms, and the novelty of the musical material. In sum we find that crowd-based mechanisms are especially suited to forecast the performance of novel songs from unknown artists, which makes them a powerful yet very affordable decision support instrument for very uncertain contexts with limited historical data available. These findings can support recording companies to address the challenge of signing newcomers and thereby further enable the innovation system of the industry.

A scientific tool to address severe haze in Equatorial Asia

BackgroundAcross Indonesia, fires are frequently used to clear forests and manage land for plantations and smallholder farms. The smoke from these fires contain particles that can work their way deep into the lungs and cause premature mortality from cardiovascular disease, stroke, and lung infections, along with many other health effects. These particles can travel hundreds of kilometres downwind, leading to high levels of exposure and adverse health effects in dense population centres across Equatorial Asia. However, accurate prediction of the public health effects from these fires is complicated by links between land use decisions in Indonesia, fire emissions, and transport from source regions to population centres. We aimed to quantify how future scenarios of land management could affect local and regional exposure to smoke concentrations. MethodsWe used satellite observations to quantify the relationships between land management and fires. Using these relationships, we then estimate the cumulative fire emissions of different land management strategies, including (1) “business-as-usual” trends in deforestation and development, (2) increased conversion of forests to oil palm and timber plantations, and (3) successful implementation of the Indonesian Government's moratorium on development and burning in fuel-rich peatlands. We subsequently use the adjoint of the GEOS-Chem chemical transport model to estimate the public health implications of exposure to smoke concentrations from fires in these scenarios. FindingsOur satellite-based analysis shows that most emissions arise from non-forested peatland landscapes with mosaics of degraded lands and agriculture. When combined with three future land management strategies, we find substantial declines in regional smoke concentrations and health impacts when Indonesian peatlands are protected from fires and future development. Results from these initial scenarios show the capabilities of our modelling framework in diverse situations and introduce the capabilities of an online decision support instrument for policymakers. InterpretationWe are now synthesising these efforts to calculate the cumulative health and economic effects over coming decades associated with different land management scenarios in the region. FundingThe Rockefeller Foundation and the Gordon and Betty Moore Foundation through the Health & Ecosystems: Analysis of Linkages (HEAL) programme and the Winslow Foundation.

Study of aerodynamic performances of different wind tunnel configurations and air inlet velocities, using computational fluid dynamics (CFD)

Livestock and agricultural activities contribute significantly to atmospheric ammonia emission in Europe. The volatilization process depends on many factors, especially wind speed and rainfall. The most important methods to evaluate ammonia volatilization are the wind tunnel and micrometeorological methods. The tunnels are more flexible and simple to use in every situation. Few studies have been carried out to determine, which conditions are established inside the chamber and how they influence the ammonia volatilization and measurement.The aim of this research was to investigate the effects of the wind tunnel configuration and flow inlet velocity, by means of CFD simulations and wind speed measurements, in order to achieve a better aerodynamic performance. The SST k–ω model used for simulations was first validated in order to prove the consistency of the model itself. Several configurations were simulated and compared. In particular, in order to overcome the asymmetric flow conditions that occurred in all wind tunnel configurations, four flow distribution devices were proposed and simulated. The best setup was chosen with the purpose of reaching both the best uniform velocity distribution (to ensure homogeneous volatilization from the emitting surface) and easy transport for field applications. It consists of an emission chamber 40cm wide, 25cm high and 80cm long, situated between a divergent diffuser and a convergent duct, respectively 50cm and 25cm long. Moreover, structures similar to honeycombs, namely guiding channels, were introduced in the divergent diffuser, because they showed the best aerodynamic performance. These 20 channels, located in the divergent diffuser, prevent flow from separating, by means of the reduction of the expansion angle, obtaining the desired flow conditions inside the wind tunnel. Finally, it was verified that CFD confirmed its usefulness as a decision-support instrument to design and simulate possible solutions, reducing design time.

Material Flow Cost Accounting as a Decision Support Instrument

Material Flow Cost Accounting as a Decision Support Instrument

Urban Modality

This thesis proposes a framework for evaluating the mobility potential and performance of urban areas in the city region, as an instrument to support urban development that contributes positively to regional sustainable mobility objectives. The research takes a quantitative approach, modelling and measuring the characteristics of a city-region and of its individual urban areas, in terms of travel patterns and socioeconomic characteristics of the resident population, and in terms of built environment characteristics. It then explores how the built environment defines the affordances of urban areas for travelling by particular modes of transport, i.e. its walk-ability, cycleability, drive-ability and transit-ability, by developing a typology of what I call their ‘urban modality’. And finally the work combines this typology with the socio-economic characteristics of urban areas to determine their sustainable mobility potential and performance. It focuses on the case of the Randstad region of the Netherlands and its VINEX neighbourhoods, which are an emblematic example of new urban areas created under a policy programme with sustainable mobility objectives. A key stance in this work is the understanding that the location of an urban area in the region can be indicative of its population’s travel patterns, because the built environment (infrastructural) and socio-economic characteristics are interrelated and present strong regional spatial patterns. What types of urban areas support sustainable travel patterns, and what are their spatial characteristics? How do new neighbourhoods compare to the best performing urban areas, and to other areas of the same ‘modality’ type? These are some of the questions addressed in this study. There are two main contributions of this research: the methods for building and analysing integrated multimodal network models, and the framework for contextual performance evaluation using urban area typologies. The integrated multimodal network model combines the various mobility infrastructure networks and the buildings’ land use to create a detailed description of the region, using open spatial data and open source Geographic Information Systems (GIS) technologies. The network model’s spatial analysis covers local urban form indicators, such as street layout, network density and land use mix, as well as regional indicators of multimodal accessibility and network configuration (its structure), to give a holistic profile of urban areas across modes and scales of travel. The analysis results go through exploratory data mining and classification procedures to identify urban form typologies of urban areas. It is shown that there is a relation between this ‘urban modality’ of urban areas and the travel patterns of their residents, measured as a set of sustainable mobility indicators related to mode share and distance travelled. For this reason, ‘urban modality’ offers the possibility for ex-ante evaluation of sustainable mobility potential of planned urban areas. Furthermore, when combined with the socio-economic profile of the resident population, ‘urban modality’ defines a context for the ex-post evaluation of sustainable mobility performance of existing urban areas. The evaluation of suburban areas together with the more central historical urban areas gives invariably a high score in sustainable travel to the central areas, and rates the suburban areas negatively. On the other hand, the evaluation of sustainable mobility performance in the context of suburban areas of the same type allows the finer distinction of underperformers that have scope for improvement, and overachievers that provide examples of (relative) success. This contextual evaluation can become a decision support instrument for “hard” and “soft” planning measures involving sustainable mobility targets. Applying this method to the set of VINEX neighbourhoods of the Randstad leads to the conclusion that despite being planned following the same policy objectives, the neighbourhoods have different types of ‘urban modality’, thus present different levels of sustainable mobility potential. Neighbourhoods identified as underperformers within their context can be targeted for soft measures related to transport services, technology and individual attitudes to travel, to fulfil the potential of their ‘urban modality’ type. However, if this potential is not deemed satisfactory or if they already overachieve, only by retrofitting a set of infrastructure and land use characteristics will lead to a different ‘urban modality’ type, and a change in potential. Such a change can be lengthy, costly and sometimes impossible to implement ex-post. The thesis is based on a collection of published articles in peer-reviewed academic publications, with the first and last chapters providing an overview of the research and of its findings, and defining the main narrative thread.

Urban Modality

This thesis proposes a framework for evaluating the mobility potential and performance of urban areas in the city region, as an instrument to support urban development that contributes positively to regional sustainable mobility objectives. The research takes a quantitative approach, modelling and measuring the characteristics of a city-region and of its individual urban areas, in terms of travel patterns and socioeconomic characteristics of the resident population, and in terms of built environment characteristics. It then explores how the built environment defines the affordances of urban areas for travelling by particular modes of transport, i.e. its walk-ability, cycleability, drive-ability and transit-ability, by developing a typology of what I call their ‘urban modality’. And finally the work combines this typology with the socio-economic characteristics of urban areas to determine their sustainable mobility potential and performance. It focuses on the case of the Randstad region of the Netherlands and its VINEX neighbourhoods, which are an emblematic example of new urban areas created under a policy programme with sustainable mobility objectives. A key stance in this work is the understanding that the location of an urban area in the region can be indicative of its population’s travel patterns, because the built environment (infrastructural) and socio-economic characteristics are interrelated and present strong regional spatial patterns. What types of urban areas support sustainable travel patterns, and what are their spatial characteristics? How do new neighbourhoods compare to the best performing urban areas, and to other areas of the same ‘modality’ type? These are some of the questions addressed in this study. There are two main contributions of this research: the methods for building and analysing integrated multimodal network models, and the framework for contextual performance evaluation using urban area typologies. The integrated multimodal network model combines the various mobility infrastructure networks and the buildings’ land use to create a detailed description of the region, using open spatial data and open source Geographic Information Systems (GIS) technologies. The network model’s spatial analysis covers local urban form indicators, such as street layout, network density and land use mix, as well as regional indicators of multimodal accessibility and network configuration (its structure), to give a holistic profile of urban areas across modes and scales of travel. The analysis results go through exploratory data mining and classification procedures to identify urban form typologies of urban areas. It is shown that there is a relation between this ‘urban modality’ of urban areas and the travel patterns of their residents, measured as a set of sustainable mobility indicators related to mode share and distance travelled. For this reason, ‘urban modality’ offers the possibility for ex-ante evaluation of sustainable mobility potential of planned urban areas. Furthermore, when combined with the socio-economic profile of the resident population, ‘urban modality’ defines a context for the ex-post evaluation of sustainable mobility performance of existing urban areas. The evaluation of suburban areas together with the more central historical urban areas gives invariably a high score in sustainable travel to the central areas, and rates the suburban areas negatively. On the other hand, the evaluation of sustainable mobility performance in the context of suburban areas of the same type allows the finer distinction of underperformers that have scope for improvement, and overachievers that provide examples of (relative) success. This contextual evaluation can become a decision support instrument for “hard” and “soft” planning measures involving sustainable mobility targets. Applying this method to the set of VINEX neighbourhoods of the Randstad leads to the conclusion that despite being planned following the same policy objectives, the neighbourhoods have different types of ‘urban modality’, thus present different levels of sustainable mobility potential. Neighbourhoods identified as underperformers within their context can be targeted for soft measures related to transport services, technology and individual attitudes to travel, to fulfil the potential of their ‘urban modality’ type. However, if this potential is not deemed satisfactory or if they already overachieve, only by retrofitting a set of infrastructure and land use characteristics will lead to a different ‘urban modality’ type, and a change in potential. Such a change can be lengthy, costly and sometimes impossible to implement ex-post. The thesis is based on a collection of published articles in peer-reviewed academic publications, with the first and last chapters providing an overview of the research and of its findings, and defining the main narrative thread.

Urban Modality

This thesis proposes a framework for evaluating the mobility potential and performance of urban areas in the city region, as an instrument to support urban development that contributes positively to regional sustainable mobility objectives. The research takes a quantitative approach, modelling and measuring the characteristics of a city-region and of its individual urban areas, in terms of travel patterns and socioeconomic characteristics of the resident population, and in terms of built environment characteristics. It then explores how the built environment defines the affordances of urban areas for travelling by particular modes of transport, i.e. its walk-ability, cycleability, drive-ability and transit-ability, by developing a typology of what I call their ‘urban modality’. And finally the work combines this typology with the socio-economic characteristics of urban areas to determine their sustainable mobility potential and performance. It focuses on the case of the Randstad region of the Netherlands and its VINEX neighbourhoods, which are an emblematic example of new urban areas created under a policy programme with sustainable mobility objectives. A key stance in this work is the understanding that the location of an urban area in the region can be indicative of its population’s travel patterns, because the built environment (infrastructural) and socio-economic characteristics are interrelated and present strong regional spatial patterns. What types of urban areas support sustainable travel patterns, and what are their spatial characteristics? How do new neighbourhoods compare to the best performing urban areas, and to other areas of the same ‘modality’ type? These are some of the questions addressed in this study. There are two main contributions of this research: the methods for building and analysing integrated multimodal network models, and the framework for contextual performance evaluation using urban area typologies. The integrated multimodal network model combines the various mobility infrastructure networks and the buildings’ land use to create a detailed description of the region, using open spatial data and open source Geographic Information Systems (GIS) technologies. The network model’s spatial analysis covers local urban form indicators, such as street layout, network density and land use mix, as well as regional indicators of multimodal accessibility and network configuration (its structure), to give a holistic profile of urban areas across modes and scales of travel. The analysis results go through exploratory data mining and classification procedures to identify urban form typologies of urban areas. It is shown that there is a relation between this ‘urban modality’ of urban areas and the travel patterns of their residents, measured as a set of sustainable mobility indicators related to mode share and distance travelled. For this reason, ‘urban modality’ offers the possibility for ex-ante evaluation of sustainable mobility potential of planned urban areas. Furthermore, when combined with the socio-economic profile of the resident population, ‘urban modality’ defines a context for the ex-post evaluation of sustainable mobility performance of existing urban areas. The evaluation of suburban areas together with the more central historical urban areas gives invariably a high score in sustainable travel to the central areas, and rates the suburban areas negatively. On the other hand, the evaluation of sustainable mobility performance in the context of suburban areas of the same type allows the finer distinction of underperformers that have scope for improvement, and overachievers that provide examples of (relative) success. This contextual evaluation can become a decision support instrument for “hard” and “soft” planning measures involving sustainable mobility targets. Applying this method to the set of VINEX neighbourhoods of the Randstad leads to the conclusion that despite being planned following the same policy objectives, the neighbourhoods have different types of ‘urban modality’, thus present different levels of sustainable mobility potential. Neighbourhoods identified as underperformers within their context can be targeted for soft measures related to transport services, technology and individual attitudes to travel, to fulfil the potential of their ‘urban modality’ type. However, if this potential is not deemed satisfactory or if they already overachieve, only by retrofitting a set of infrastructure and land use characteristics will lead to a different ‘urban modality’ type, and a change in potential. Such a change can be lengthy, costly and sometimes impossible to implement ex-post. The thesis is based on a collection of published articles in peer-reviewed academic publications, with the first and last chapters providing an overview of the research and of its findings, and defining the main narrative thread.

Component-based light-rail modeling in discrete event systems specification (DEVS)

Rail modeling and simulation is an effective decision support instrument for planning and designing complex rail infrastructures and operations. To successfully support these activities at a large scale, the simulation model should be sufficiently detailed and yet be computationally efficient. This poses a set of challenges pertaining to the design of the models. In this paper, we propose a component-based light-rail modeling and simulation library in the discrete event system specification (DEVS) formalism. The proposed library is described in detail and is shown to be efficient and scalable. We conclude the paper by offering a set of good design principles derived from this experience, which are also relevant to other types of large-scale infrastructure system simulation model design.

Tool for life cycle analysis of facade-systems for industrial buildings

Minimal investment cost, flexibility and expandability of the construction have been the highest priorities in the design of industrial facilities. With the sharpening of building codes and the upcoming polices on energy efficiency, life cycle optimisation is starting to gain importance among industrial investors. On the case study of an energy efficient industrial facility, a decision-support tool was developed for analysing life cycle economic and environmental impacts of facade-systems. The tool was tested by analysing three different facade-systems (steel liner tray, steel sandwich panels, cross laminated timber panels) of the proposed building model. The construction cost of the tested facade-systems are largely differing (up to 27%), however after a period of 35 years, the life cycle costs are diverging by only 6%. In terms of ecology (Global Warming Potential) the cross laminated timber facade, with the highest initial costs, features the best performance by 80% less emissions. The test underlines the large impact the design stage has on the life cycle performance, when determining facade elements and shading concepts. The tool has large implementation potential as a relatively easily applicable decision-support instrument for designers and investors, when studying and determining sustainable construction and facade systems; thus improving the traditional decision-making process, still based on the choice of the lowest costing construction.

Intelligent Scheduling for Underground Mobile Mining Equipment.

Many studies have been carried out and many commercial software applications have been developed to improve the performances of surface mining operations, especially for the loader-trucks cycle of surface mining. However, there have been quite few studies aiming to improve the mining process of underground mines. In underground mines, mobile mining equipment is mostly scheduled instinctively, without theoretical support for these decisions. Furthermore, in case of unexpected events, it is hard for miners to rapidly find solutions to reschedule and to adapt the changes. This investigation first introduces the motivation, the technical background, and then the objective of the study. A decision support instrument (i.e. schedule optimizer for mobile mining equipment) is proposed and described to address this issue. The method and related algorithms which are used in this instrument are presented and discussed. The proposed method was tested by using a real case of Kittilä mine located in Finland. The result suggests that the proposed method can considerably improve the working efficiency and reduce the working time of the underground mine.