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Safe, Efficient and Sustainable Mobility Solutions

ATPIO and TRG Webinar: Application of big data analytics in transportation operations

ATPIO and TRG is jointly hosting a webinar on “Application of big data analytics in transportation operations” on 9th May, 2018;  10:00 AM ET (7:30 PM IST).

Webinar Linkhttps://connect.extension.iastate.edu/atpio/

Webinar Registration: Please put your name, affiliation and email id here. We will send you a calendar invite and reminder.

Abstract:

“You can’t manage what you don’t measure” – The quote attributed to both W. Edwards Deming and Peter Drucker explains the importance of the recent digital data explosion. The volume, velocity and variety of data generated nowadays make it difficult to use traditional techniques to analyze and extract useful information from these datasets. However, efficient storage and managing such rich data sources can help public and private sector to better understand the underlying systems and help in improved decision making and performance. Significant advancements can be made using big data analytics for building smart transportation networks leading to smarter cities and overall development of quality of life in US conditions.

In the past, only a limited number of data streams have been available from infrastructure mounted sensors for use in traffic management, but with the advent of smart phones, connected cars, etc., several nontraditional sources of data are being made available for analysis. However, these heterogeneous data streams arrive at very high rates and often can have spurious data. This exponential growth in relevant data streams has brought new opportunities and challenges in the realm of traffic management. Increased data enables improved monitoring, prediction, and management of traffic, but only if automation, is employed to handle the data volumes. These volumes would otherwise exceed the abilities of humans to process. This talk will highlight Big-data tools that can be used to timely analyze this data and perform real-time assessment and prediction of traffic conditions to improve system efficiency and safety, real-time identification of traffic incidents, and as input to future planning.

About the Speaker, Anuj Sharma:

 

Anuj Sharma, Ph.D., is an Associate Professor at Iowa State University and is the co-director of the REACTOR lab. Dr. Sharma’s research has been recognized by numerous federal, state and private agencies; including the National Science Foundation, Federal Highway Administration, National Institute of Health, Toyota, state Departments of Transportation, and city public works departments; with over eleven million dollars of competitive research funding, contributed to fund his research. He uses high performance computation driven big data discoveries to assist in making better short term (operational automation) and long term (smart policy) decisions. Sharma has over 100 conference/journal publications and is editor in chief for Springer’s Journal of Big Data Analytics in Transportation.

Sharma, in his role as a director of REACTOR lab at Iowa State University has, spearheaded the effort to ingest multiple data feeds on a cloud platform, Azure, perform analytics such as fusion, quality assurance, signal denoising, and machine learning based incident detection, and finally provide a consumable data feed as an open data service.