Simulation Model of Traffic in Smart Cities for Decision-Making Support: Case Study in Tudela (Navarre, Spain)

Purpose Digital India, the flagship programme of Government of India (GoI) originated from National e-Governance Project (NeGP) in the year 2014. The programme has important aspect of information security and implementation of IT policy which supports e-Governance in a focused approach of Mission Mode. In this context, there is a need to assess situation of the programme which covers a study of initiatives and actions taken by various actor involved and processes which are responsible for overall e-Governance. Therefore, the purpose of this case study is to develop a Situation-Actor-Process (SAP), Learning-Action-Performance (LAP) based inquiry model to synthesize situation of information security governance, IT policy and overall e-Governance. Design/methodology/approach In this case study both systematic inquiry and matrices based SAP-LAP models are developed. Actors are classified who are found responsible and engaged in IT policy framing, infrastructure development and also in e-Governance implementation. Based on a synthesis of SAP components, various LAP elements were then synthesized then which further led to learning from the case study. Suitable actions and performance have also been highlighted, followed by a statement of the impact of the efficacy i.e. transformation of information security, policy and e-Governance on the Digital India programme. Findings On developing the SAP-LAP framework, it was found that actors like the Ministry of Electronics and Information Technology of the Govt. of India secures a higher rank in implementing various initiatives and central sector schemes to accelerate the agenda of e-Governance. Actions of other preferred actors include more investments in IT infrastructure, policy development and a mechanism to address cyber security threats for effective implementation of e-Governance. It was found that actors should be pro-active on enhancing technical skills, capacity building and imparting education related to ICT applications and e-Governance. Decision making should be based on the sustainable management practices of e-Governance projects implementation to manage change, policy making and the governmental process of the Indian administration and also to achieve Sustainable Development Goals by the Indian economy. Research limitations/implications The SAP-LAP synthesis is used to develop the case study. However, few other qualitative and quantitative multi criteria decision making approaches could also be explored for the development of IT security based e-Governance framework in the Indian context. Practical implications The synthesis of SAP leads to LAP components which can bridge the gaps between information security, IT policy governance and e-Governance process. Based on the learning from the Situation, it is said that the case study can provide decision making support and has impact on the e-Governance process i.e. may enhance awareness about e-services available to the general public. Such work is required to assess the transparency and accountability on the Government. Social implications Learning based on the SAP-LAP framework could provide decision making support to the administrators, policy makers and IT sector stakeholders. Thus, the case study would further help in addressing the research gaps, accelerating e-Governance initiatives and in capturing cyber threats. Originality/value The SAP-LAP model is found as an intuitive approach to analyze the present status of information security governance, IT policy and e-Governance in India in a single unitary model.

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Integrated system architecture for decision-making and urban planning in smart cities

International Journal of Distributed Sensor Networks ◽

10.1177/1550147719867829 ◽

2019 ◽

Vol 15 (8) ◽

pp. 155014771986782

Author(s):

Felipe Marques Pires ◽

Leonardo de Souza Mendes ◽

Lorena León Quiñonez

Keyword(s):

Decision Making ◽

Urban Planning ◽

System Architecture ◽

Smart Cities ◽

Building Blocks ◽

Integrated System ◽

Local Processing ◽

Area Network ◽

Cloud Platform ◽

Decision Making Support

Research and development of applications for smart cities are extremely relevant considering the various problems that population growth will bring to large urban centers in the next few years. Although research on cyber-physical systems, cloud computing, embedded devices, sensor and actuator networks, and participatory sensing, among other paradigms, is driving the growth of solutions, there are a lot of challenges that need to be addressed. Based on these observations, in this work, we present an integrated system architecture for decision-making support and urban planning by introducing its building blocks (termed components): sensing/actuation, local processing, communication, cloud platform, and application components. In the sensing/actuation component, we present the major relevant resources for data collection, identification devices, and actuators that can be used in smart city solutions. Sensing/actuation component is followed by the local processing component, which is responsible for processing, decision-making support, and control in local scale. The communication component, as the connection element among all these components, is presented with an emphasis on the open-access metropolitan area network and cellular networks. The cloud platform is the essential component for urban planning and integration with electronic governance legacy systems, and finally, the application component, in which the government administrator and users have access to public management tools, citizen services, and other urban planning resources.

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Estimating complex ecological variables at high resolution in heterogeneous terrain using multivariate matching algorithms

10.32942/osf.io/b2ux7 ◽

2021 ◽

Author(s):

Rachel Renne ◽

Daniel Schlaepfer ◽

Kyle Palmquist ◽

William Lauenroth ◽

John Bradford

Keyword(s):

Decision Making ◽

Time Series ◽

High Resolution ◽

Simulation Model ◽

Site Selection ◽

Ecosystem Structure ◽

Simulation Output ◽

Representative Points ◽

Matching Criteria

1. Simulation models are valuable tools for estimating ecosystem structure and function under various climatic and environmental conditions and disturbance regimes, and are particularly relevant for investigating the potential impacts of climate change on ecosystems. However, because computational requirements can restrict the number of feasible simulations, they are often run at coarse scales or for representative points. These results can be difficult to use in decision-making, particularly in topographically complex regions.2. We present methods for interpolating multivariate and time series simulation output to high resolution maps. First, we developed a method for applying k-means clustering to optimize selection of simulation sites to maximize the area represented for a given number of simulations. Then, we used multivariate matching to interpolate simulation results to high-resolution maps for the represented area. The methods rely on a user-defined set of matching variables that are assigned weights such that matched sites will be within a prescribed range for each variable. We demonstrate the methods with case studies using an individual-based plant simulation model to illustrate site selection and an ecosystem water balance simulation model for interpolation.3. For the site-selection case study, our approach optimized the location of 200 simulation sites and accurately represented 96% of a large study area (1.12 x 106 km2) at a 30-arcsecond resolution. For the interpolation case study, we generated high-resolution (30-arcsecond) maps across 4.38 x 106 km2 of drylands in western North America from simulated sites representing a 10 x 10 km grid. Our estimates of interpolation errors using leave-one-out cross validation were low (<10% of the range of each variable).4. Our point selection and interpolation methods provide a means of generating high-resolution maps of complex simulation output (e.g., multivariate and time-series) at scales relevant for local conservation planning and can help resolve the effects of topography that are lost in simulations at coarse scales or for representative points. These methods are flexible and allow the user to identify relevant matching criteria for an area of interest to balance quality of matching with areal coverage to enhance inference and decision-making in heterogenous terrain.

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