Real world applications using parallel computing techniques in dynamic traffic assignment and shortest path search

As the range of applications for Intelligent Transport Systems (ITS) grows wider, the efficiency of the underlying tools for Big Data Analytics becomes of crucial importance. Smart Cities are able to monitor, forecast and (possibly) control the pulse of collective interactions involving networks and environment (such as traffic and pollution) by means of key performance indicators. Technology-guided solutions can proactively support the sustainable development and the optimal management of infrastructures and services, improving the quality of life for both city dwellers and commuters. This requires processing huge amounts of data, continuously streaming in from a variety of fixed sensors (e.g. loops, cameras) and mobile devices (GPS trajectories). In particular, Mobility Control Centres need effective software solutions and fast algorithms to deal with two major problems: Traffic Forecasting and Route Guidance. This paper presents real world examples of large scale applications where both tasks are addressed by implementing parallel computing algorithms, achieving high performances and allowing real time management operations and end-user services. The first test case examines the performance of a routing platform covering the entire Austria region, while the second concerns large instances of dynamic traffic assignment for real-time forecasting

BIG DATA IN SMART CITIES: A SYSTEMATIC MAPPING REVIEW

Big data is an emerging area of research and its prospective applications in smart cities are extensively recognized. In this study, we provide a breadth-first review of the domain “Big Data in Smart Cities” by applying the formal research method of systematic mapping. We investigated the primary sources of publication, research growth, maturity level of the research area, prominent research themes, type of analytics applied, and the areas of smart cities where big data research is produced. Consequently, we identified that empirical research in the domain has been progressing since 2013. The IEEE Access journal and IEEE Smart Cities Conference are the leading sources of literature containing 10.34% and 13.88% of the publications, respectively. The current state of the research is semi-matured where research type of 46.15% of the publications is solution and experience, and contribution type of 60% of the publications is architecture, platform, and framework. Prescriptive is least whereas predictive is the most applied type of analytics in smart cities as it has been stated in 43.08% of the publications. Overall, 33.85%, 21.54%, 13.85%, 12.31%, 7.69%, 6.15%, and 4.61% of the research produced in the domain focused on smart transportation, smart environment, smart governance, smart healthcare, smart energy, smart education, and smart safety, respectively. Besides the requirement for producing validation and evaluation research in the areas of smart transportation and smart environment, there is a need for more research efforts in the areas of smart healthcare, smart governance, smart safety, smart education, and smart energy. Furthermore, the potential of prescriptive analytics in smart cities is also an area of research that needs to be explored