Advances on Smart Cities and Smart Buildings

Modern cities are facing the challenge of combining competitiveness at the global city scale and sustainable urban development to become smart cities. A smart city is a high-tech, intensive and advanced city that connects people, information, and city elements using new technologies in order to create a sustainable, greener city; competitive and innovative commerce; and an increased quality of life. This Special Issue collects the recent advancements in smart cities and covers different topics and aspects

A waste collection case study

Mestrado Bolonha em Data Analytics for BusinessInnovative waste collection strategies have been established to replace conventional systems with dynamic systems that respond to the actual fill-level of waste containers. This work proposes a method for route design involved in the waste collection, focusing on minimizing the total distance travelled, for a case study of the Portuguese municipality of Caldas da Rainha. The process uses historical data of the filling percentage of each container to determine the number of weekly collections required per container to determine the number of weekly collections required per container and therefore create collection routes. More conventional approaches are based on a single average fill-up rate common to all containers, which may not represent the reality and therefore lead to inefficiencies. By analysing the available data, is concluded that all containers only require one weekly collection. A data clustering algorithm is then applied to aggregate container in groups corresponding to the same route, based on proximity. Then, routes are designed within each cluster. Finally, the selection of pairs of routes for the same day is done through a matching problem. Data limitations lie on the manual introduction of containers’ fill level and on most historical data being from the pandemic. The established methodology is applied to the glass waste collection and transportation system of Valorsul S.A. The present project aims at proposing a method for waste collection planning and explores critical considerations and future improvements based on the difficulties faced.info:eu-repo/semantics/publishedVersio

Predicting the Transportation Activities of Construction Waste Hauling Trucks: An Input-Output Hidden Markov Approach

Construction waste hauling trucks (CWHTs), as one of the most commonly seen heavy-duty vehicles in major cities around the globe, are usually subject to a series of regulations and spatial-temporal access restrictions because they not only produce significant NOx and PM emissions but also causes on-road fugitive dust. The timely and accurate prediction of CWHTs' destinations and dwell times play a key role in effective environmental management. To address this challenge, we propose a prediction method based on an interpretable activity-based model, input-output hidden Markov model (IOHMM), and validate it on 300 CWHTs in Chengdu, China. Contextual factors are considered in the model to improve its prediction power. Results show that the IOHMM outperforms several baseline models, including Markov chains, linear regression, and long short-term memory. Factors influencing the predictability of CWHTs' transportation activities are also explored using linear regression models. Results suggest the proposed model holds promise in assisting authorities by predicting the upcoming transportation activities of CWHTs and administering intervention in a timely and effective manner.Comment: 21 pages, 8 figure

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Waste management in smart cities: Optimization of waste container’s capacity using fixed-frequency collection

One of the main problems of modern societies is the control of the production flow and removal of urban solid waste, due to the continuous massification of urban areas. This dissertation focuses on the need to reduce the impact of human activity over the environment through the management of urban solid waste. It describes how technological advancements can lead to an increase in the sustainability of urban development, through more efficient planning and reduction of logistics costs and pollution emissions. The work uses data of the product 360Waste from Evox Technologies, a company of a former student of ISCTE, operating in Castelo Branco. This company is specialized in creating an integrated solution for efficient collection of urban waste. This solution is composed of volumetric reading sensors, based on LoRaWAN technology. These sensors are installed in urban solid waste containers, which are always sending data to a LoRaWAN gateway, every time an individual opens the container. Based on the data collected from the sensors, the research work challenge will be to develop a solution to optimize the management of urban solid waste, by defining a uniform collection system and using technologies known as Data Sciences and Machine Learning.Um dos principais problemas das sociedades contemporâneas é o controlo do fluxo de produção e remoção dos resíduos sólidos urbanos, devido à massificação contínua das zonas urbanas. Esta dissertação incide sobre a necessidade de reduzir o impacto da atividade humana no meio ambiente, através da gestão dos resíduos sólidos urbanos. Neste trabalho é descrito como a evolução tecnológica pode conduzir a um desenvolvimento urbano mais sustentável, através de um planeamento mais eficiente, redução dos custos logísticos e das emissões poluentes. O trabalho utiliza dados de um produto 360Waste da Evox Technologies, uma empresa de um antigo aluno do ISCTE, que opera em Castelo Branco. Esta empresa é especializada na criação de uma solução integrada para a recolha eficiente de resíduos urbanos. Esta solução é composta por sensores de leitura volumétrica, com base na tecnologia LoRaWAN. Estes sensores são instalados em contentores de resíduos sólidos urbanos, que estão constantemente a enviar dados para uma Gateway LoRaWAN, cada vez que um indivíduo abre o contentor. Com base nos dados recolhidos dos sensores, o desafio deste trabalho de investigação será desenvolver uma solução para optimizar a gestão dos resíduos sólidos urbanos, definindo um sistema de recolha uniforme e utilizando tecnologias conhecidas como Data Sciences e Machine Learning

Edge computing and iot analytics for agile optimization in intelligent transportation systems

[EN] With the emergence of fog and edge computing, new possibilities arise regarding the data-driven management of citizens' mobility in smart cities. Internet of Things (IoT) analytics refers to the use of these technologies, data, and analytical models to describe the current status of the city traffic, to predict its evolution over the coming hours, and to make decisions that increase the efficiency of the transportation system. It involves many challenges such as how to deal and manage real and huge amounts of data, and improving security, privacy, scalability, reliability, and quality of services in the cloud and vehicular network. In this paper, we review the state of the art of IoT in intelligent transportation systems (ITS), identify challenges posed by cloud, fog, and edge computing in ITS, and develop a methodology based on agile optimization algorithms for solving a dynamic ride-sharing problem (DRSP) in the context of edge/fog computing. These algorithms allow us to process, in real time, the data gathered from IoT systems in order to optimize automatic decisions in the city transportation system, including: optimizing the vehicle routing, recommending customized transportation modes to the citizens, generating efficient ride-sharing and car-sharing strategies, create optimal charging station for electric vehicles and different services within urban and interurban areas. A numerical example considering a DRSP is provided, in which the potential of employing edge/fog computing, open data, and agile algorithms is illustrated.This work was partially supported by the Spanish Ministry of Science (PID2019111100RB-C21/AEI/10.13039/501100011033, RED2018-102642-T), and the Erasmus+ program (2019I-ES01-KA103-062602).Peyman, M.; Copado, PJ.; Tordecilla, RD.; Do C. Martins, L.; Xhafa, F.; Juan-Pérez, ÁA. (2021). Edge computing and iot analytics for agile optimization in intelligent transportation systems. Energies. 14(19):1-26. https://doi.org/10.3390/en14196309126141

Recommended System for Optimizing Battery Energy Management with Floating Car Data

Atualmente, os veículos pesados que transportam mercadoria sensível à temperatura utilizam sistemas de refrigeração ruidosos e com elevado consumo de combustível. Para combater estas desvantagens, está a ser instalado um sistema capaz de recuperar e produzir energia elétrica durante as travagens e a partir de painéis fotovoltaicos. Esta energia é armazenada num conjunto de baterias para, posteriormente, alimentar o sistema frigorífico em modo elétrico. Adicionalmente, estão a ser recolhidos dados em tempo real sobre o comportamento do veículo e do sistema.Tendo em conta que toda a energia disponível durante a condução está condicionada por diversas variáveis de operação, é fulcral extrair conhecimento a partir da análise dos dados recolhidos, identificando padrões que possam otimizar a produção e gestão da energia preditivamente. Este processo de extração de conhecimento inclui seleção e avaliação dos dados a recolher, construção do modelo preditivo do sistema e estudo da sua aplicação. Assim sendo, num dado momento, tendo em conta não só as métricas recolhidas da viagem atual, mas também de dados históricos de um dado percurso, será possível ao sistema de gestão de energia instalado no camião decidir qual a melhor ação a tomar de forma a otimizar a energia produzida sem causar stress ao sistema.Nowadays, heavy vehicles that transport temperature-sensitive goods, generally use a fuel-needy dedicated diesel engine. Towards solving this problem, an energy management system (EMS) capable of producing energy on-board of the vehicle is being developed. This recovery is possible due to the regenerative braking (RB) functionality, which consists in converting kinetic energy to electrical energy during a slowdown. The recovered energy is then stored in a set of batteries that supplies the refrigeration system when needed, allowing it to run in electrical mode. Using data retrieved from the vehicle's operation and this management system, an opportunity towards intelligently using the regenerative braking functionality emerges. By introducing an intelligence layer on the energy management system, a decision on applying the RB functionality could be made based on the trip's energetic potential. This decision will optimize the battery usage and reduce the load and wear on the EMS components.In order to calculate the energetic potential of a certain route, an estimation of the road is needed. This document presents context information and different approaches towards this end. In the modeling approach recommended and implemented, a route is divided in several spatial segments and each segment is categorized among three pre-defined classes. A classification model is used to predict traffic historical data as input. By using this modeling approach based on travel times, information on traffic flow and intersection queues are incorporated and by calculating the most likely sequence of states, a estimation of the road ahead is made.Using the information of the modeled path, when the RB systems detects a situation where the functionality can be applied, a decision will be made by weighting the energetic potential of the path ahead and the energy need. When the algorithm sees fit, a higher torque may be applied to the generator, which will result in a larger quantity of energy recovered. Since this causes stress to the system, this functionality needs a robust intelligence layer

Book of abstracts of the 24th Euro Working Group on Transportation Meeting

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Review on Construction Procedures of Driving Cycles

The goal of this paper is to give an overview of the literature of construction techniques of driving cycles. Our motivation for the overview is the future goal of constructing our own driving cycles for various types of vehicles and routes. This activity is part of a larger project focusing on determination of fuel and energy consumption by dynamic simulation of vehicles. Accordingly, the papers dealing with sample route determination, data collection and processing, driving cycle construction procedures, statistical evaluation of data are in our focus