Document Type
Post-Print
Publication Date
8-2017
Subjects
Traffic signs and signals -- United States -- Control systems -- Management, Electronic traffic controls -- Performance -- Measurement, Machine learning, Traffic flow
Abstract
Automated traffic signal performance measures (ATSPMs) are an effort to equip traffic signal controllers with high-resolution data-logging capabilities and utilize this data to generate performance measures. These measures allow practitioners to improve operations as well as to maintain and operate their systems in a safe and efficient manner. Although these measures have changed the way that operators manage their systems, several shortcomings of the tool, identified by talking with signal operators, are a lack of data quality control and the extent of resources required to properly use the tool for system-wide management. To address these shortcomings, intelligent traffic signal performance measurements (ITSPMs) are presented in this paper, using the concepts of machine learning, traffic flow theory, and data visualization to reduce the operator resources needed for overseeing data-driven traffic signal management systems. In applying these concepts, Ms provide graphical tools to identify and remove logging errors and data from bad sensors, intelligently determine trends in demand, and address the question of whether or not coordination may be needed at an intersection. The focus of ATSPMs and ITSPMs on performance measures for multimodal users is identified as a pressing need for future research.
DOI
10.1177/0361198118791380
Persistent Identifier
https://archives.pdx.edu/ds/psu/28685
Citation Details
Huang, Tingting; Poddar, Subhadipto; Aguilar, Chris; Sharma, Anuj; Smaglik, Edward J.; Kothuri, Sirisha; and Koonce, Peter, "Building Intelligence in the Automated Traffic Signal Performance Measures with Advanced Data Analytics" (2017). Civil and Environmental Engineering Faculty Publications and Presentations. 483.
https://archives.pdx.edu/ds/psu/28685
Description
This is a manuscript of a proceeding published as Huang, Tingting, Subhadipto Poddar, Cristopher Aguilar, Anuj Sharma, Edward Smaglik, Sirisha Kothuri, and Peter Koonce. "Building Intelligence in the Automated Traffic Signal Performance Measures with Advanced Data Analytics." No. 18-05800. 2018. Transportation Research Board 97th Annual Meeting, Washington, DC, January 7-11, 2018. Posted with permission.
May be accessed at https://doi.org/10.1177/0361198118791380.