Building Intelligence in Automated Traffic Signal Performance Measures with Advanced Data Analytics
Civil, Construction and Environmental Engineering, Institute for Transportation
Journal or Book Title
Transportation Research Record
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.
Research Focus Area
National Academy of Sciences: Transportation Research Board
Huang, Tingting; Poddar, Subhadipto; Aguilar, Cristopher; Sharma, Anuj; Smaglik, Edward; Kothuri, Sirisha; and Koonce, Peter, "Building Intelligence in Automated Traffic Signal Performance Measures with Advanced Data Analytics" (2018). Civil, Construction and Environmental Engineering Publications. 263.