Why High-Performance Modelling and Simulation for Big Data Applications Matters

Modelling and Simulation (M&S) offer adequate abstractions to manage the complexity of analysing big data in scientific and engineering domains. Unfortunately, big data problems are often not easily amenable to efficient and effective use of High Performance Computing (HPC) facilities and technologies. Furthermore, M&S communities typically lack the detailed expertise required to exploit the full potential of HPC solutions while HPC specialists may not be fully aware of specific modelling and simulation requirements and applications. The COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications has created a strategic framework to foster interaction between M&S experts from various application domains on the one hand and HPC experts on the other hand to develop effective solutions for big data applications. One of the tangible outcomes of the COST Action is a collection of case studies from various computing domains. Each case study brought together both HPC and M&S experts, giving witness of the effective cross-pollination facilitated by the COST Action. In this introductory article we argue why joining forces between M&S and HPC communities is both timely in the big data era and crucial for success in many application domains. Moreover, we provide an overview on the state of the art in the various research areas concerned

Statistical characteristics of transitional queue conditions in signalized arterials

The performance of signalized arterials is related to queuing phenomena. The paper investigates the effect of transitional traffic flow conditions imposed by the formation and dissipation of queues. A cross-recurrence quantification analysis combined with Bayesian augmented networks are implemented to reveal the prevailing statistical characteristics of the short-term traffic flow patterns under the effect of transitional queue conditions. Results indicate that transitions between free-flow conditions, critical queue conditions that exceed the detector's length, as well as the occurrence of spillovers impose a set of prevailing traffic flow patterns with different statistical characteristics with respect to determinism, nonlinearity, non-stationarity and laminarity. The complexity in critical queue conditions is further investigated by introducing two supplementary regions in the critical area before spillover occurrence. Results indicate that the supplementary information on the transitional conditions in the critical area increases the accuracy of the predictive relations between the statistical characteristics of traffic flow evolution and the occurrence of transitions