Abstract: Traffic is one of the most important aspects of modern life, both in the developed world and in the developing world. Analysis of traffic systems through modeling and simulation is an essential tool for city planners to optimize traffic flow. Microscopic discrete-event simulation is a natural and powerful method for such analysis. Characteristics of traffic systems that need to be modeled include inter-arrival times, car-following behavior, lane-changing, turning, road structure, intersection structure, and traffic light timing. There are many challenges that must be confronted as well, including the calibration and validation of traffic models, scaling models to larger traffic systems, modeling the continuous nature of traffic in a discrete-event environment, and optimization of traffic system characteristics. An emerging challenge for traffic simulation is the incorporation of autonomous vehicles into traffic systems as not only traditional vehicles, but also as cooperative agents as well.

Abstract: Frameworks for big data arguably began with Google's use of MapReduce. Since then, a huge amount of progress has been made in the development of big data frameworks, many of which have been released as open source. Further to increase portability and ease of set- up, many are coded in a Java Virtual Machine (JVM) based language, eg, Java or Scala. In addition, processing of big data involves the flow of data, and of course, the processing of data as it flows. This computational paradigm is a natural for functional programming. Furthermore, the map, reduce and combiner have analogs in functional programming. There has been a trend in the last few years toward developing open source big data frameworks written in Scala to support big data analytics. Scala is a modern JVM language that supports both object-oriented and functional programming paradigms.

Abstract: As populations increase, city-wide traffic volumes will also increase in most cities around the world. Improving the efficiency, safety, and cost of such road systems is an essential social problem that must be solved as the number of drivers, and the size of mass transit systems increase. Comprehensive systems of traffic signals are needed to make traffic flows as efficient and as safe as possible. Big data analytics can be used to process the large amount of data needed to create useful models of traffic systems. However, the computational cost is extensive, due to the large size of the data, the complicated models needed to represent complex traffic systems, and the behavioral models needed to represent the vehicles in such traffic systems. Using polynomial regression and non-homogeneous Poisson arrival processes, a method is devised for creating a vehicle arrival process that closely fits real vehicle arrival data. Regression and a specialized discrete random variable are used to control vehicle behavior within the traffic model. The field of simulation optimization contains many techniques, but the specific characteristics of traffic simulation require creativity to apply them. There are particular challenges for gradient-based optimization such as quasi Newton methods such as developing proper step sizes in the search process, overcoming noise in the response surface, and the heavy cost of calculating gradients. Gradient-estimation based on the simultaneous perturbation algorithm reduces time requirements, however step sizes and noisy responses are still a complicating factor. There are gradient-free techniques such as the Nelder-Meade simplex algorithm, local Tabu search, and global genetic algorithm search, all of which reduce the effects of noise, and the difficulty of choosing appropriate step sizes. All data processing, modeling, simulations, and optimization are handled using ScalaTion, a scalable simulation and mathematics package developed for use with the Scala programming language.

Abstract: Big data analytics and scalable simulation modeling can be used to improve complex systems and processes. The explosive growth in the amount of available data for science and engineering in the coming years has the potential of enabling large scale, methodological decision making. For example, due to their high cost, careful planning of improvements to transportation systems is essential. For this problem, large amounts of sensor data from roads can be used for calibrating traffic models via optimization. In this paper, we examine the problem of traffic light synchronization in an effort to improve the efficiency of road systems.

Here is a pdf of the poster itself: Poster

Abstract: Understanding how services operate as part of large scale global networks, the related risks and gains of different network structures and their dynamics is becoming increasingly critical for society. Our vision and research agenda focuses on the particularly challenging task of building, analyzing, and reasoning about global service networks. This paper explains how Service Network Analysis (SNA) can be used to study and optimize the provisioning of complex services modeled as Open Semantic Service Networks (OSSN), a computer-understandable digital structure which represents connected and dependent services.