Improve Traffic Volume Estimates from Regional Transportation Management Center (RTMC)

Status:  Active
Project Start Date:  06/22/2016

Summary:

MnDOT uses a large number of sensors in the freeway network to produce estimates of daily traffic for over 500 locations. Managing so many sensors to ensure correct functioning is a huge challenge. Thorough sensor screening is critical to ensure traffic data accuracy. While the RTMC uses detector data to measure operational characteristics, MnDOT needs to expand the use of the collected data to support the accurate estimation of Annual Average Daily Traffic (AADT) using well researched methodologies to screen out suspicious or invalid data. Implementing previously identified methods or enhancing the algorithms and methods with additional research should lead to a more efficient and transparent means of determining AADTs, and should enhance the ability of RTMC to strategically target sensor testing and repair. The objective of this project is to quickly identify loop problems from a large pool of loops in order to obtain more accurate data. A software tool will be developed to detect bad or suspicious sensor from the daily archived data and RTMC repair log data. Another application of this software is in identification of specific types of loop problems for maintenance operation.

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