Purpose-The most sustainable forms of urban mobility are walking and cycling. These modes of transportation are the most environmental friendly, the most economically viable and the most socially inclusive and engaging modes of urban transportation. To measure and compare the effectiveness of alternative pedestrianization or cycling infrastructure plans, the authors need to measure the potential flows of pedestrians and cyclists. The paper aims to discuss this issue. Design/methodology/approach-The authors have developed a computational methodology to predict walking and cycling flows and local centrality of streets, given a road centerline network and occupancy or population density data attributed to building plots. Findings-The authors show the functionality of this model in a hypothetical grid network and a simulated setting in a real town. In addition, the authors show how this model can be validated using crowd-sensed data on human mobility trails. This methodology can be used in assessing sustainable urban mobility plans. Originality/value-The main contribution of this paper is the generalization and adaptation of two network centrality models and a trip-distribution model for studying walking and cycling mobility.

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In a quest for promoting sustainable modes of mobility, we have revisited how feasible and suitable is it for people to walk or cycle to their destinations in a neighbourhood. We propose a few accessibility measures based on an 'Easiest Path' algorithm that provides also actual temporal distance between locations. This algorithm finds paths that are as short, flat and straightforward as possible. Considering several 'points of interest', the methods can answer such questions as "do I have a 5 minutes 'easy' walking/cycling access to all/any of these points?" or, "which is the preferred point of interest with 'easy' walking cycling access?" We redefine catchment zones using Fuzzy logics and allow for mapping 'closeness' considering preferences such as 'how far' people are willing to go on foot/bike for reaching a particular destination. The accessibility measures are implemented in the toolkit CONFIGURBANIST to provide real-time analysis of urban networks for design and planning.

The COVID-19 has become an important health issue in the world and has endangered human health. The purpose of this research is to use an intelligent system model of adaptive neuro-fuzzy inference system (ANFIS) using twelve variables of input for the diagnosis of COVID-19. The evaluation of the model was performed using the information of 500 patients referred to and suspected of the COVID-19. Three hundred and fifty people were used as training data and 150 people were used as test and validation data. Information on 12 important parameters of COVID-19 such as fever, cough, headache, respiratory rate, Ct-chest, medical history, skin rash, age, family history, loss of olfactory sensation and taste, digestive symptoms, and malaise was also reported in patients with severe disease. ANFIS identified COVID-19 in accuracy, sensitivity, and specificity with more than 95%, 94%, and 95%, respectively, which indicates the high efficiency of the system in the correct diagnosis of individuals. The proposed system accurately detected more than 95% COVID-19 as well as mild, moderate, and acute severity. Due to the time-constraint, limitations, and error of COVID-19 diagnostic tools, the proposed system can be used in high-precision primary detection, as well as saving time and cost.

This report summarizes the conceptual framework and the technical implementation of a BIM-based user-friendly application to gather inhabitant’s input in the context of renovation projects developed in the European funded project BIM-SPEED. It starts with the underlying objectives and the role of data acquisition in the project. Then, it explains and outlines the conceptual framework and the methodology used in relation to the identified use cases, where inhabitants’ input is required. Furthermore, it describes the implementation and the development of the proposed methods as a user-friendly app and, finally, it ends with ethical and privacy considerations, specifically the measures taken for safe handling of privacy-sensitive data.