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A rapidly growing number of vehicles in recent years cause long traffic jams and difficulty in the management of traffic in cities. One of the most significant reasons for increased traffic jams on the road is random parking in unauthorized and non-permitted places. In addition, managing of available parking places cannot achieve the expected reduction in traffic congestion related problems due to mismanagement, lack of real-time parking guidance to the drivers, and general ignorance. As the number of roads, highways and related resources has not increased significantly, a rising need for a smart, dynamic and effective parking solution is observed. Accordingly, with the use of multiple sensors, appropriate communication network and advanced processing capabilities of edge and cloud computing, a smart parking system can help manage parking effectively and make it easier for the vehicle owners. In this paper, we propose a multi-layer architecture for smart parking system consisting of multi-parametric parking slot sensor nodes, latest long-range low-power wireless communication technology and Edge-Cloud computation. The proposed system enables dynamic management of parking for large areas while providing useful information to the drivers about available parking locations and related services through near real-time monitoring of vehicles. Furthermore, we propose a dynamic pricing algorithm to yield maximum possible revenue for the parking authority and optimum parking slot availability for the drivers. [ABSTRACT FROM AUTHOR]

Time-varying pricing is seen as an appropriate means for unlocking the potential flexibility from electric vehicle users. This in turn facilitates the future integration of electric vehicles and renewable energy resources into the power grid. The most complex form of time-varying pricing is dynamic pricing. Its application to electric vehicle charging is receiving growing attention and an increasing number of different approaches can be found in the literature. This work aims at providing an overview and a categorization of the existing work in this growing field of research. Furthermore, user studies and the modeling of user preferences via utility functions are discussed. [ABSTRACT FROM AUTHOR]

The rapid advancements in information and communication technology during the third industrial revolution of the late 20th century has marked the beginning of a new era in the retail sector with the introduction of E-commerce. The dawn of the new century witnessed industry 4.0, revolutionizing all areas of online business by bringing in novel opportunities and possibilities. Despite the progress in technology, the determination of correct pricing on online selling platforms still remains a very complex task. The adoption of big data technology has enabled online sellers to make real-time price changes of high magnitude and proximity. However, with increasing awareness among buyers regarding modern pricing strategies, it is necessary to examine probable changes in consumer behavior when exposed to dynamic pricing scenarios. This study investigates the factors that influence consumer behavior, and their prospective online purchase decisions in a dynamic pricing context, through an exploratory factor analysis approach. A primary research survey was conducted, and 178 samples were finalized for data analysis through a series of web surveys completed by respondents in India. This study identifies, measures and classifies 27 research items into variables, namely shopping experience, privacy concerns, awareness about dynamic pricing, buying strategy, fair price perceptions, reprisal intentions and intentions for self-protection. These seven factors could be used to explain consumer behavior in a dynamic pricing situation. [ABSTRACT FROM AUTHOR]