The SpeedPro Project

These slides describe a describe a multivariate predictive multi-model approach called SpeedPro that (a) first identifies similar clusters of operation from the historic data that includes the real-time position of the probe vehicle, the weather data, and anonymized driver identifier, and then (b) uses these different models to estimate the traffic speed in real-time as a function of current weather, driver and probe vehicle speed. When the real-time information is not available our approach uses a different model that uses the historical weather and traffic information for estimation. Our results show that the purely historical data is less accurate than the model that uses the real-time information.

Slides from ISORC 2017

The emerging Fog Computing paradigm is providing an additional computational layer that enables new capabilities in real-time data-driven applications. The application of Fog Computing is especially interesting in the domain of Smart Grid where it can be used to prove a decentralized application framework that reflects the ongoing trend of distribution of intelligence in Smart Systems. These slides describe a component-based decentralized computation platform called RIAPS which provides an application architecture for such systems.

Slides from FMEC 2017

As the number of low cost computing devices at the edge of communication network increase, there are greater opportunities to enable innovative capabilities, especially in cyber-physical systems. For example, micro-grid power systems can make use of computing capabilities at the edge of a Smart Grid to provide more robust and decentralized control. However, the downside to distributing intelligence to the edge away from the controlled environment of the data centers is the increased risk of failures. The paper introduces a framework for handling these challenges. The contribution of this framework is to support strategies to (a) tolerate the transient faults as they appear due to network fluctuations or node failures, and to (b) systematically reconfigure the application if the faults persist.

The Microgrid Control Application Demonstration Using RIAPS Software

Our collaborators at North Carolina State University recently developed and demonstrated A distributed power system application is running on 4 RIAPS nodes that shows the initial capabilities of RIAPS platform services. The demonstration shows how distributed synchronization can be implemented. Three out of the four nodes are connected to a real-time simulator simulating the micro grid. The fourth node is used for logging data. This demo also uses the C37 device actor developed by the Vanderbilt University team as part of the initial capability implementation of RIAPS

Mechanisms for Optimizing On-Time Performance of Fixed Schedule Transit Vehicles

The on-time arrival performance of vehicles at stops is a critical metric for both riders and city planners to evaluate the reliability of a transit system. However, it is a non-trivial task for transit agencies to adjust the existing bus schedule to optimize the on-time performance for the future. For example, severe weather conditions and special events in the city could slow down traffic and cause bus delay. Furthermore, the delay of previous trips may affect the initial departure time of consecutive trips and generate accumulated delay. In this paper, we formulate the problem as a single-objective optimization task with constraints and propose a greedy algorithm and a genetic algorithm to generate bus schedules at time points that improves the bus on-time performance at timepoints which is indicated by whether the arrival delay is within the desired range. We use the Nashville bus system as a case study and simulate the optimization performance using historical data. The comparative analysis of the results identifies that delay patterns change over time and reveals the efficiency of the greedy and genetic algorithms.