Building Intelligence in Automated Traffic Signal Performance Measures with Advanced Data Analytics

Date
2018-12-01
Authors
Huang, Tingting
Poddar, Subhadipto
Aguilar, Cristopher
Sharma, Anuj
Smaglik, Edward
Kothuri, Sirisha
Koonce, Peter
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Abstract

Automated traffic signal performance measures (ATSPMs) are designed to equip traffic signal controllers with high-resolution data-logging capabilities which may be used 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. While they have changed the way that operators manage their systems, several shortcomings of ATSPMs, as identified by signal operators, include a lack of data quality control and the extent of resources required to use the tool properly 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 ATSPMs. In applying these concepts, ITSPMs provide graphical tools to identify and remove logging errors and data from bad sensors, to determine trends in demand intelligently, and to 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.

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<p>This is a manuscript of an article published as Huang, Tingting, Subhadipto Poddar, Cristopher Aguilar, Anuj Sharma, Edward Smaglik, Sirisha Kothuri, and Peter Koonce. "Building Intelligence in Automated Traffic Signal Performance Measures with Advanced Data Analytics." <em>Transportation Research Record</em> 2672, no. 18 (2018): 154-166. DOI: <a href="http://dx.doi.org/10.1177%2F0361198118791380" target="_blank">10.1177%2F0361198118791380</a>. Posted with permission.</p>
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