Adaptive intelligent traffic control systems for improving traffic quality and congestion in smart cities

A systematic review was undertaken to examine the solutions available for traffic congestion and associated problems in smart cities. Google Scholar and Google were used as search engines, leading to the final selection of 35 eligible papers for inclusion in this review, after a serious of screening based on definite criteria. Intelligent transport systems were found to be the most suitable solution to traffic congestion and associated problems in smart cities. Certain models and frameworks of smart cities include smart mobility and transport management systems. These can be approximated to intelligent transport systems. True intelligent transport systems are infrastructure-based or intelligent vehicle based or more preferably, a combination of both. The Internet of Things and cloud computing should be built into the system as they enable the operation of smart transport networks. Some methods of designing traffic control systems combining both Eulerian and Lagrangian approaches have been discussed for the possibility of using any of them to design a new automatic traffic monitoring and control system for smart cities. The practical implication of this research is that it can improve quality of life of people by minimizing traffic congestion. Limitations of this paper include this being a systematic review, availability of very few papers and not considering adaptive intelligent traffic control systems. Explanations for these limitations have been provide

Intelligent Transportation System for Smart-Cities using Fuzzy Logic

According to United Nations population statistics 2017, the world population is 7.6 billion and is growing rapidily alomost 11 billion by end of 21 century with a 70% chance of continued growth, this rapid increasing population have created low standards of living in cities. Smart Cities are facing pressures associated with due innovations and globalization to improve their citizens life. Computational intelligence is the study of adaptive mechanism to facilitate intelligent behavior in changing and complex environments. Traffic congestion and monitoring has become one of the critical issues in big cities. The adaptive mechanism of computational intelligence in changing the behavior of complex environments of smart city is very effective. The developing framework and services for smart-city requires sound infrastructure, latest current technology adoption. A framework model with the integration of cloud-data, social network (SN) services that is collecting stream data with smart sensors in the context of smart cities is proposed. The adaptive mechanism of computational intelligence in changing thebehavior of complex environments of smart city is very effective. A radical framework that enables the analysis of big-data sets stemming from Social Networking (SN) sites. Smart cities understanding is a broad concept only city transportation sector is focused in this article. Fuzzy logic modeling techniques are used in many fields i.e. medical, engineering. business and computing related problems. To solve various traffic management issues in cities a detailed analysis of fuzzy logic system is proposed. This paper presents an analysis of the results achieved using Fuzzy Logic System (FLS) for smart cities. The results are verified using MATLAB Simulation

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

Modeling the Internet of Things: a simulation perspective

This paper deals with the problem of properly simulating the Internet of Things (IoT). Simulating an IoT allows evaluating strategies that can be employed to deploy smart services over different kinds of territories. However, the heterogeneity of scenarios seriously complicates this task. This imposes the use of sophisticated modeling and simulation techniques. We discuss novel approaches for the provision of scalable simulation scenarios, that enable the real-time execution of massively populated IoT environments. Attention is given to novel hybrid and multi-level simulation techniques that, when combined with agent-based, adaptive Parallel and Distributed Simulation (PADS) approaches, can provide means to perform highly detailed simulations on demand. To support this claim, we detail a use case concerned with the simulation of vehicular transportation systems.Comment: Proceedings of the IEEE 2017 International Conference on High Performance Computing and Simulation (HPCS 2017