Smart and Digital Cities

The multi-modal transportation problem is a type of shortest path problem (SPP). Its goal is to find the best path between two points in a network with more than one means of transportation. This network can be modeled using a weighted directed colored graph. Hence, an optimization method can be applied to find a better path between two nodes. There are some digital applications, like the well-known Google Maps, that present solution to this problem. Most of them choose the best path according to one objective function: the minimum travel time. However, this optimization process can consider multiple objective functions if different user’s interests are treated in the model such as the cheapest or the most comfortable path. In this work, we implemented and compared two different approaches of algorithms with multiple objectives. One of them is an exact method based on Dijkstra’s algorithm added by sum weight method, and the other one is a heuristic approach based on the non-dominated sorting genetic algorithm (NSGA-II). The computational results of the two methods were compared. The comparison shows that the heuristic method is promising due to the low execution time—around 20 s—and the quality of the results. This quality was measured by the closeness of the points found by the two methods in the objective domain. The runtime and the quality of the results can indicate that this modeling is suitable to a real-time problem, for instance, the multi-modal transportation problem.

Urban transport systems generally present complex topologies and constraints, and consequently model them and propose solutions that are not simple. Because of the importance of proposing solutions that improve urban mobility and the people’s quality of life, in this work, we propose two algorithms applicable to transport network and traffic systems. The first algorithm approaches the shortest path problem in colored graphs. In this case, the graphs’ coloration is used in a different and innovative form: each transport mode is represented by a color (label) and various edges exist between two nodes of the graph (each edge represents a transport mode). The second algorithm, in addition to the coloration used to find the shortest path, considers the multimodal flow network problem by an incremental process. Some examples are presented and the behavior of both algorithms is shown.

This paper presents an adaptation of the adaptive large neighborhood search (ALNS) heuristic in order to solve the crew scheduling problem (CSP) of urban buses. The CSP consists in minimizing the total of crews that will drive a fleet in daily operation as well as the total overtime. The solution for the CSP is a set of duties performed by the crews throughout the day, and those duties must comply with labor laws, labor union agreements, and the company’s operational rules. The CSP is a NP-hard problem and it is usually solved by metaheuristics. Therefore, an ALNS-like heuristic was developed to solve the CSP. Its implementation was tested with real data from a bus company which operates in Belo Horizonte, MG-Brazil, and it provided solutions quite superior to those both adopted by the company and the ones generated by other methods in the literature.

The location of projects in the My House My Life Program (PMCMV) is of great importance because it is not only a business relationship between buyers and developers, but also a contribution to the social transformation. The use of modern tools that help the launching plan of Real Estate companies, financiers, and buyers is a constant challenge to the sector, which requires accurate decisions. This article proposes a new tool for the planning of PMCMV residential real estate projects, using Operations Research techniques such as the p-median to locate candidate areas to receive ventures as close as possible to the existing potential demands. In order to illustrate the proposed method, a detailed case study was carried out in the Municipality of Rio de Janeiro. The results obtained in the case study demonstrate, in some cases, large variations between the positions of higher demand and the launches of developments in the PMCMV format promoted by the market.

In the last decade, selective waste collection and the posterior reinsertion of the recycled materials into the productive chain have become not only an important economic activity but also essential for the reduction of the environmental impacts associated with landfills. The collection step of the recycling process is, in essence, a transportation operation, with costs directly coupled with those of fuel. Thus, fuel consumption reduction can increase profit margins with a pleasant side effect of reducing greenhouse gas emissions. In this scenario, Green Vehicle Routing Problems (GVRP) are instrumental as enablers of more economic routes in the sense of fuel consumption. Metaheuristics are a valuable tool to deal with GVRP problems due to its NP-hardness nature.

Studies about network infrastructure have been realized and applied in a high variety of service-based industries, these studies are currently being used to design the network infrastructure in smart cities. However, planning network infrastructure in different levels is a big problem to be solved, because, generally, literature presents solutions where just one level is processed and the problems are solved individually. Planning the distribution and connection of equipment at various levels of a network infrastructure is an arduous task, it is necessary to evaluate the quantity and the best geographical distribution of equipment at each level of the network. This research presents a metaheuristic inspired by the concepts of the genetic algorithms. The proposed paper can search for solutions to plan the network infrastructure of multilevel capacitated networks, solving the network planning problem and obtaining results that are 20% better at cost when compared with other solutions.

The world population has been rising on a large scale, and this directly reflects on the electricity consumption. In this scenario, techniques for accurately forecasting energy consumption are particularly important, since these data can be applied in decision-making and good planning aimed at providing constant and reliable energy. Forecasting energy consumption with the most accurate value possible is not a trivial task and depends on some factors. One of the most recent works dealing with the subject at the Brazil presented a fuzzy logic-based prediction model using consumption, Gross Domestic Product index (GDP), and population and obtained good results. This work aims to evaluate, through an experimental study, the performance of classical regression techniques—linear regression, multilayer perceptron, and support vector regression—in energy consumption forecast in the Brazilian scenario. Also, we verified whether the inclusion of additional socioeconomic data could contribute to obtaining a more efficient model. When compared to the results available in the literature, our approach demonstrated superior performance in some situations.

This chapter presents the evaluation of the Model for Assistive Smart Cities (MASC), which is intended for ubiquitous accessibility. The model was evaluated with data obtained from a contextual simulator (SIAFU), applied in the central region of São Leopoldo city, Brazil. Unlike other approaches, the evaluation considers multiple accesses asynchronously, indicating that the model meets massive applications with response time within the standards indicated for this type of application. The evaluation considered requests from three different groups of users, characterized as: people with disabilities (PwDs), health professionals involved in the care of PwDs, and managers of public administration. The results of the evaluation indicated the feasibility of implementing the model in Smart Cities. In addition to collaborating with accessibility, the model favors the decision-making in the management of services in the cities.

Intelligent transportation systems are one of the components to make smart cities through which they have sought to improve levels of safety, comfort, and efficiency of transportation systems. Vehicular networks support the exchange of messages by vehicles with the information necessary for proper functioning of these systems. Because of complexity of these communication networks, a technique widely used to evaluate their performance is simulation. However, a simulation problem involves choosing appropriate parameters to achieve realistic results. This work deals with the problem of realistic simulation in vehicular networks, through the simulation of a message dissemination application, where various simulation and application parameters are varied. The main contribution consists in analysis of results obtained according to the chosen parameters and the finding that these parameters must be adjusted properly to obtain results consistent with reality.

Communication and easy access to information are the main factors in reducing waste of time and resources. Communication systems are heavily dependent on telecommunications companies, which provide high speed services, but charge a high monthly fee per device, thus making some applications unaffordable. A LoRaWan is an emerging network technology that stands out for having characteristics such as long range capability, low power consumption and low cost, and focuses on sensing, thus proving to be a good alternative as a communication solution in applications for smart cities. This article highlights projects that use LoRaWan in simple applications with the potential to improve the quality of life of citizens by implementing the concept of a smart city in a distributed manner. In this study, tests are carried out on libraries, and frameworks are developed by open software communities on how they play a key role in the popularization of technology around the world.

Cities are undergoing transformations in several respects, but, mainly, regarding novel technologies. In this sense, the aim of this study is to understand the relation between the cities and the use of emerging technologies such as digital democracy, blockchain and, in particular, smart contracts. To discuss and analyze the possibilities of this evolution and transformation, a bibliometric search is carried out in this chapter. A search of keywords in some refereed world databases was made: Springer, SCOPUS, IEEExplore, Science Direct, Google Trends, Web of Science, and Taylor & Francis. This combination of databases was selected in order to become possible to reach a high coverage of numbers of works about digital city and related technologies.

The constant evolution of cities has driven the development of new tools for society. Applications inspired by operational research techniques, which aid decision making, can make viable dreams already dreamed up by philosophers. Among these, we highlight more participatory, legitimate, and reliable judicial and legislative systems. In this context, a multicriteria analysis seems necessary, balancing the different versions, beliefs, cultures, and consequent weights and measures desired by each citizen. In this paper, we present a new model for judicial/legislative processes in digital cities. The system proposes the use of sets of solutions, obtained from different weights adopted according to personal characteristics of those involved in the voting process. From a simple case of study, we highlight the possibilities, flexibility, and potential of the proposed system. The proposed framework shows up as promising tool for assisting decision making in other similar voting scenarios.

The Smart Planning has been presented in the discussions in both urban computing, operational research, and digital cities and in the urban and regional planning field. The first can be referred as process of the integration, analysis, and processing of data in order to optimize city services, while the second group has a long trajectory in defining and understanding the urban dynamics. As a matter of fact, conflicts in identifying key similarities in approaches for a more sustainable use of those tools are constant and must be carefully handled. Through an exploratory perspective, the goal is to highlight the vastness of the planning field and the rise of several complexities when introducing modern technologies in the urban sphere.

A global trend has been motivating programmers, investors, and the academia to go towards decentralized systems. Besides providing efficient solutions for complex problems faced in our daily life, these peer-to-peer communication protocols have been promoting greater freedom and transparency. In this paper, we point out open fields for researching, thinking, and developing in the context of Smart Cities and Smart Grids. Future cities will surely rely on efficient, autonomous, transparent, collaborative, and decentralized environment. In particular, we consider how renewable energy resources could be integrated with mini-/microgrids, which will be hearth of the future cities. Furthermore, we discuss possibilities for promoting territorial development, through the assistance of Blockchain-based platforms embedded with Computational Intelligence tools.

Battery management is key to enact the widespread use of microgrid-connected electric vehicles. We thoroughly review the literature and tackle the role of battery in the process of energy commercialization between the microgrids and utilities. In particular, by considering the battery management as a stochastic inventory control problem, we develop a dynamic programming model and we obtain an optimal policy for it. Then, we further explore the baseline model by investigating a scenario in which the microgrid is constrained by a budget defined a priori. Such a budget constraint captures situations when the microgrid profile is risk averse. We end by discussing the main issues that stem from such a budget constraint scenario.

Unmanned aerial vehicles (UAVs) have been used in several different fields. Each day, new applications for these devices arise. Also called RPA (remotely piloted aircraft) or popularly known by drones, they are becoming cheaper and more accessible to the whole population. Undoubtedly, the UAVs will play a fundamental role in the everyday life of the smart cities, urban areas, and industries, contributing to the improvement of the quality of life in its most different aspects. These devices can provide information acquisition about the industrial process and its equipment, data of raw materials yard, and also information about remote areas. Companies can benefit from UAVs applications either acting directly in the industrial process or simply with the acquisition of aerial images for specific purposes. In this chapter, some of the main applications of UAVs and their role in smart cities and industries as well as their characteristics and some of the most promising developments are presented. Several examples of applications along the production chain of heavy industries and for the primary sector of the economy are approached.

Critical systems require high reliability and are present in many applications. Standard techniques of software engineering are not enough to ensure the absence of unacceptable failures and/or that critical requirements are fulfilled. Verifying and certifying systems for Smart Cities is one of the challenges that still require some effort. Smart Cities models may be seen as Cyber-Physical Systems and they may be formalized as Finite State Machines. We discuss how to reason over these models as Finite State Machines formalized in a logical background from which it is possible to provide certified software for the Smart Cities domain.