EAGER-DynamicData: Dynamic Data Driven Distributed Simulation for Transportation System Applications on Emerging Computing Platforms

Georgia Tech Research Corporation, Atlanta GA

Investigators

Abstract

Major changes in urban transportation systems are now underway due to innovations occurring along several different dimensions. Smart cars, autonomous and semi-autonomous vehicles, electric vehicles, compact energy-efficient micro-vehicles, and increased deployment of vehicle-to-vehicle and vehicle-to-infrastructure communications are revolutionizing the vehicle fleet. New types of transportation services are emerging such as short-term vehicle rentals and commercial crowd-sourced taxi services that are increasing shared use and lessening reliance on private vehicle ownership. New sources of data and vast amounts of information concerning the transportation network itself are becoming available including crowd-sourced data provided by citizens through mobile devices and data collected from surveillance systems including unmanned aerial vehicles (UAVs). At the same time advances in computing and communications are enabling new, mobile high performance computing platforms that can be embedded within the transportation system. It is not well understood how these platforms can be best exploited in emerging transportation system applications to help address long-standing issues concerning safety, congestion, resource consumption, and pollution that significantly degrade the quality of life in the U.S. and throughout the world. New computational methods are needed to maximally exploit these new technologies to create the reliable, resilient, and efficient transportation systems demanded by modern cities. This project will explore synergies and challenges arising from the integration of three largely separate areas dynamic data analytics, embedded distributed simulations, and power-aware computing on emerging high performance mobile computing platforms for transportation system applications. The context for this research is power-tunable platforms termed micro-clusters that are small closely coupled multi-node computer systems assembled from mobile computing components. On a larger scale, distributed networked micro-clusters will form the computation core for many dynamic data driven application system (DDDAS) deployments in the future. This project seeks to develop deep understandings of key principles concerning the design and operation of micro-clusters executing integrated dynamic data analysis algorithms and embedded distributed simulations for emerging transportation system applications. A micro-cluster hardware testbed will be created as a key element of this project for experimental research in power aware dynamic data-driven distributed simulation and used for benchmarking. The results of this study will be used to develop a research agenda and follow-on research to develop approaches and techniques to minimize energy consumption while maintaining effectiveness of scalable DDDAS deployments. Beyond transportation system applications, platforms such as micro-clusters will become commonplace and widely used in the operation of real-world systems for a variety of applications besides transportation in areas such as manufacturing, logistics, and telecommunications. Project results will be incorporated into undergraduate and graduate courses in transportation, modeling and simulation and parallel computing. The project will engage underrepresented and K-12 groups in computing and engineering through participation in summer internship programs and summer camps.