How to incorporate urban complexity, diversity and intelligence into smart cities initiatives

Under the term “Smart City”, numerous technology-based initiatives are emerging to help cities face contemporary challenges while the concept itself is evolving towards a more holistic approach. Nevertheless, the capability of smart initiatives to provide an integrated vision of our cities is still very limited. Eventually, many of these initiatives fail to understand the complexity, diversity and intelligence that characterize contemporary cities. The purpose of this paper is to display an urban functional system, capable of interpreting the city in a more holistic way and of facilitating effective involvement of local stakeholders in the planning process of SCs initiatives

Traffic lights synchronization for Bus Rapid Transit using a parallel evolutionary algorithm

This article presents a parallel evolutionary algorithm for public transport optimization by synchronizing traffic lights in the context of Bus Rapid Transit systems. The related optimization problem is NP-hard, so exact computational methods are not useful to solve real-world instances. Our research introduces a parallel evolutionary algorithm to efficiently configure and synchronize traffic lights and improve the average speed of buses and other vehicles. The Bus Rapid Transit on Garzón Avenue (Montevideo, Uruguay) is used as a case study. This is an interesting complex urban scenario due to the number of crossings, streets, and traffic lights in the zone. The experimental analysis compares the numerical results computed by the parallel evolutionary algorithm with a scenario that models the current reality. The results show that the proposed evolutionary algorithm achieves better quality of service when compared with the current reality, improving up to 15.3% the average bus speed and 24.8% the average speed of other vehicles. A multiobjective optimization analysis also demonstrates that additional improvements can be achieved by assigning different priorities to buses and other vehicles. In addition, further improvements can be achieved on a modified scenario simply by deleting a few bus stops and changing some traffic lights rules. The benefits of using a parallel solver are also highlighted, as the parallel version is able to accelerate the execution times up to 26.9× when compared with the sequential version. Keywords: Bus Rapid Transit, Traffic lights synchronization, Evolutionary algorithm Document type: Articl

Traffic lights synchronization for Bus Rapid Transit using a parallel evolutionary algorithm

This article presents a parallel evolutionary algorithm for public transport optimization by synchronizing traffic lights in the context of Bus Rapid Transit systems. The related optimization problem is NP-hard, so exact computational methods are not useful to solve real-world instances. Our research introduces a parallel evolutionary algorithm to efficiently configure and synchronize traffic lights and improve the average speed of buses and other vehicles. The Bus Rapid Transit on Garzón Avenue (Montevideo, Uruguay) is used as a case study. This is an interesting complex urban scenario due to the number of crossings, streets, and traffic lights in the zone. The experimental analysis compares the numerical results computed by the parallel evolutionary algorithm with a scenario that models the current reality. The results show that the proposed evolutionary algorithm achieves better quality of service when compared with the current reality, improving up to 15.3% the average bus speed and 24.8% the average speed of other vehicles. A multiobjective optimization analysis also demonstrates that additional improvements can be achieved by assigning different priorities to buses and other vehicles. In addition, further improvements can be achieved on a modified scenario simply by deleting a few bus stops and changing some traffic lights rules. The benefits of using a parallel solver are also highlighted, as the parallel version is able to accelerate the execution times up to 26.9x when compared with the sequential version

A Model for Crime Management in Smart Cities

The main research problem addressed in this study is that South African cities are not effectively integrating and utilising available, and rapidly emerging smart city data sources for planning and management. To this end, it was proposed that a predictive model, that assimilates data from traditionally isolated management silos, could be developed for prediction and simulation at the system-of-systems level. As proof of concept, the study focused on only one aspect of smart cities, namely crime management. Subsequently, the main objective of this study was to develop and evaluate a predictive model for crime management in smart cities that effectively integrated data from traditionally isolated management silos. The Design Science Research process was followed to develop and evaluate a prototype model. The practical contributions of this study was the development of a prototype model for integrated decision-making in smart cities, and the associated guidelines for the implementation of the developed modelling approach within the South African IDP context. Theoretically, this work contributed towards the development of a modelling paradigm for effective integrated decision-making in smart cities. This work also contributed towards developing strategic-level predictive policing tools aimed at proactively meeting community needs, and contributed to the body of knowledge regarding complex systems modelling