Overview
FHWA and The National Operations Center of Excellence are sponsoring a webinar to show best practices utilizing analysis tools with the capability to manipulate data to monitor, evaluate, and manage system performance.
Data analysis and performance measurement are integral to planning for operations. They are central to the success of an objectives-driven, performance-based approach and are often the most resource-intensive aspect to planning for operations in terms of data, staff expertise, and simulation tools and models. Analysis and performance measurement are the key activities in two major elements of the planning for operations approach:
- The systematic process to develop and select M&O strategies to meet objectives.
- System performance monitoring and evaluation. Performance measures and analysis also play an important role in the development of operations objectives.
Results from the Strategic Highway Research Program 2 (SHRP2) Reliability effort shows agencies are “Data Rich” and the amount of data streams continue to grow. This creates both opportunities and challenges. The opportunities include the availability of more detailed data for planning, monitoring, predicting and management of transportation systems and networks and the challenges include managing huge data sets (Big Data). Transportation agency managers in planning and operations are aware of the importance of objective driven performance-based approach to make the business case for Transportation Systems Management and Operation (TSMO) strategies to decision makers and the public but have made limited progress in considering the data and analytics. The manipulation of transportation Big Data is required and currently there are limited tools robust enough to big data available.
This webinar will provide an overview of analysis tools currently in practice that have the capability to:
- Handle massive amounts of transportation data
- Utilize open source technologies & tools to ingest, store, align, and process data
- Accept various types of data sets from any source
- Integrate with open source and consumer off the shelf products
- Visually dashboard data to provide greater insights and understanding
Learning Objective:
Participants, at a high-level will be able to Identify analysis tools currently in practice that accommodate TSMO Big data.
Moderator:
Daniel Grate, Transportation Systems Management Operations Specialist at FHWA Resource Center Operations Technical Service Team
Agenda:
Transportation Research Informatics Platform – (TRIP):
James Pol, Technical Director of Safety Research and Development at Turner Fairbanks: Since 2014, James served as Technical Director for the FHWA Office of Safety R&D, where he coordinates the research activities of the Safety Data and Analysis, Human Factors, and Roadway Departure teams. James is leading new work on pedestrian safety, video analytics, Big Data analytics, and Automated Driving Systems as part of his portfolio. Over James’ nearly 30-year career, including over 20 years with the Federal Highway Administration, he served in a variety of program management and leadership positions. His prior work in Intelligent Transportation Systems included the development of research programs for traveler information, congestion management, archived data management, transportation management center operations, road weather management, and vehicle information systems. James has a bachelor’s in Civil Engineering from Rensselaer Polytechnic Institute in New York, a master’s in Computer Systems Management from the University of Maryland, and he is a registered professional engineer in Delaware. James is also a certified Project Management Professional from the Project Management Institute.
An Interactive Web-based Platform for Transportation Data Integration and Analytics - TITAN
Dr. Yaw Adu-Gyamfi, Assistant Professor at Civil/Environmental Engineering University of Missouri – Columbia: Dr. Yaw Adu-Gyamfi is an assistant professor in the Civil and Environmental Engineering Department at University of Missouri - Columbia. He received his Masters and Doctorate degrees in Civil Engineering from University of Delaware. Before joining the UMC faculty in 2017, Dr. Adu-Gyamfi worked as a research scientist at University of Virginia and Iowa State University. Dr. Adu-Gyamfi has served as PI or co-PI on more multiple projects funded by State agencies, DOTs, FHWA and USDOT- Exploratory Advanced Research. His research focusses on developing innovative technologies for sensor fusion, computer vision, machine learning, Big Data analytics and visualization. Dr. Adu-Gyamfi is a member for the Transportation Research Board’s Committee on Artificial and Advanced Computing, and serves on the Editorial Advisory Board of Journal of Big Data Analytics in Transportation.