Simulation of undular bores evolution with damping

Propagation of undular bores with damping is considered in the framework of perturbed extended Korteweg-de Vries (peKdV) equation. Two types of damping terms for the peKdV equation, namely linear and Chezy frictional terms, which describe the turbulent boundary layers in the fluid flow are considered. Solving the peKdV equation numerically using the method of lines shows that under the influence of damping, the lead-ing solitary wave of the undular bores will split from the nonlinear wavetrain, propagates and behaves like an isolated solitary wave. The amplitude of the leading wave will remain the same for some times before it starts to decay again at a larger time. In general the amplitude of the leading wave and the mean level across the undular bore decreases due to the effect of damping

Community and Social Media Use among Early PEV Drivers

Plug-in electric vehicles (PEVs) are now being offered for sale to consumers. Contemporaneously, multi-way social interactions among individuals, groups, businesses, governments, and other actors are increasingly facilitated by communication technologies: we take this to be “social media.” Can this confluence facilitate the formation of new interest-based communities among plug-in electric vehicle (PEV) buyers? How might this be important to promoting PEVs? This paper presents the results of 28 in-depth interviews with household PEV drivers in San Diego, California. These PEV drivers show wide variation in their descriptions of who they believe PEV drivers to be, conceptualizations of a PEV, uses of social media to engage other members of the community, and socially mediated and face-to-face interactions with other PEV drivers. Better understanding of the relationship between emerging PEV markets, social media and consumer-based communities will affect the ongoing management of transitions to electric-mobility

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