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

TRA-952: ENHANCING RESILIENCE OF TRAFFIC NETWORKS WITH CONNECTED VEHICLES

Improving resilience of transportation infrastructure is a multifaceted subject. One of these addresses the traffic serving capability of the transportation system. As the profession progresses in finding ways to improve infrastructure resilience in physical terms, an associated thought process is underway to enhance the adaptive capacity in traffic networks with intelligent systems and advanced related methods in order to cope with shocks in the traffic environment caused by nature-induced or other events. This paper reports research in-progress on measures for enhancing the resilience of road traffic networks with applications of connected vehicles. The need for resilient road traffic networks is defined in order to reduce the risk of severe loss of capability to serve demand. Resilience is the ability to resist the loss of traffic-serving capability by using traffic (geometric) and control system design advances (i.e. the inherent resilience) and by dynamically activating capacity-enhancing measures (i.e. the dynamic resilience). There is a need to go beyond the adaptive traffic control of intersections by enhancing inherent plus dynamic resilience of the traffic system at a broader spatial scale of a corridor or a wide-area road network. Connected vehicle technology and associated methods that yield resiliency measures (i.e. adaptive capacity attributes) are described. Ideas are advanced on how to apply these resiliency measures in practice in order to address efficiency and other issues in urban transportation. Finally, concluding remarks are presented on the technical feasibility of implementing the research ideas presented in this paper

Applications of AI, IoT, and Cloud Computing in Smart Transportation: A Review

Smart transportation systems have emerged as a promising solution for improving the efficiency, safety, and sustainability of transportation. The integration of emerging technologies such as Artificial Intelligence (AI), Internet of Things (IoT), and Cloud Computing has enabled the development of intelligent transportation systems that can optimize traffic flow, enhance driver safety, and reduce transportation costs. In this study, we conducted a systematic review of the literature to explore the applications of AI, IoT, and Cloud Computing in smart transportation systems. Our findings indicate that AI can be used for autonomous vehicles, traffic management, predictive maintenance, driver assistance, and demand forecasting. IoT can enable connected vehicles, real-time fleet management, smart parking, traffic monitoring, and remote diagnostics. Cloud Computing can facilitate vehicle-to-cloud communication, scalable infrastructure, data analytics, mobility-as-a-service, and predictive maintenance. The integration of these technologies can result in a comprehensive smart transportation system that can improve the overall efficiency of transportation systems. Our study provides insights for researchers, practitioners, and policymakers on the potential applications of AI, IoT, and Cloud Computing in smart transportation systems

PREDICTIVE ENERGY MANAGEMENT IN SMART VEHICLES: EXPLOITING TRAFFIC AND TRAFFIC SIGNAL PREVIEW FOR FUEL SAVING

This master thesis proposes methods for improving fuel economy and emissions of vehicles via use of future information of state of traffic lights, traffic flow, and deterministic traffic flow models. The first part of this thesis proposes use of upcoming traffic signal information within the vehicle\u27s adaptive cruise control system to reduce idle time at stop lights and lower fuel use. To achieve this goal an optimization-based control algorithm is formulated for each equipped vehicle that uses short range radar and traffic signal information predictively to schedule an optimum velocity trajectory for the vehicle. The objectives are timely arrival at green light with minimal use of braking, maintaining safe distance between vehicles, and cruising at or near set speed. Three example simulation case studies are presented to demonstrate potential impact on fuel economy, emission levels, and trip time. The second part of this thesis addresses the use of traffic flow information to derive the fuel- or time-optimal velocity trajectory. A vehicle\u27s untimely arrival at a local traffic wave with lots of stops and goes increases its fuel use. This paper proposes predictive planning of the vehicle velocity for reducing the velocity transients in upcoming traffic waves. In this part of the thesis macroscopic evolution of traffic pattern along the vehicle route is first estimated by combining a traffic flow model and real-time traffic data streams. The fuel optimal velocity trajectory is calculated by solving an optimal control problem with the spatiotemporally varying constraint imposed by the traffic. Simulation results indicatethe potential for considerable improvements in fuel economy with a little compromise on travel time