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

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, ITSPMs 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.

Platoon Priority Visualization Modeling and Optimization for Signal Coordination in the Connected Vehicle Environment

“…Compared with the existing works (2,(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22), more practical and emerged variables are covered in the proposed Bi-PCD. In addition, a new probabilistic surrogate quantification combined with an offset optimization is proposed to improve coordination performances.…”

“…A Bi-PCD is proposed to extend the scope of the existing PCD ( 16 – 19 , 22 ) and its variants ( 2 , 12 – 15 , 20 , 21 ). Compared with the existing works ( 2 , 12 – 22 ), more practical and emerged variables are covered in the proposed Bi-PCD. In addition, a new probabilistic surrogate quantification combined with an offset optimization is proposed to improve coordination performances.…”

“…Subsequently, the original PCD has been modified to reveal better performances of the coordination control. One considered vehicle speeds, labeled as Colored PCD ( 20 ), and another used multiple days’ integrations, labelled as Aggregate PCD ( 21 ). Nevertheless, existing PCD and its variants are all suffering the limitations mentioned above of using data from fixed advanced detectors, thus needing further improvements.…”

“…First, a probabilistic platoon-based bicyclic coordination diagram (Bi-PCD) is proposed to extend the scope of the existing PCD ( 16 – 19 , 22 ) and its variants ( 2 , 12 – 15 , 20 , 21 ). Compared with the existing works ( 2 , 12 – 22 ), more practical and emerged variables are covered in the proposed Bi-PCD. In addition, the proposed Bi-PCD obtains an explicit platoon estimation of the residual and moving platoon.…”

Real‐time predictive coordination based on vehicle‐triggered platoon dispersion in a low penetration connected vehicle environment

“…Finally, an extended model proposed by Huang et al. in 2018 [27] determines better coordination plans by integrating similar historical CV‐like automated vehicle location data. However, as with our previously outlined methodologies, the current detector‐free methods are not capable of use for real‐time signal coordination control, which means they do not feature CVs real‐time data [9].…”