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

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.

• Record URL:
  https://doi.org/10.1177/0361198118791380
• Record URL:
  https://doi.org/10.1177/0361198118791380
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/03611981
• Authors:
  • Huang, Tingting
  • Poddar, Subhadipto
  • Aguilar, Cristopher
  • Sharma, Anuj
  • Smaglik, Edward
  • Kothuri, Sirisha
  • Koonce, Peter
• Publication Date: 2018

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References; Tables;
• Pagination: pp 154-166
• Serial:
  • Transportation Research Record: Journal of the Transportation Research Board
  • Volume: 2672
  • Issue Number: 18
  • Publisher: Sage Publications, Incorporated
  • ISSN: 0361-1981
  • EISSN: 2169-4052
  • Serial URL: http://journals.sagepub.com/home/trr

Subject/Index Terms

• TRT Terms: Advanced traffic management systems; Automatic data collection systems; Data logging; Data quality; Machine learning; Multimodal transportation; Performance measurement; Quality control; Resource allocation; Sensors; Traffic data; Traffic flow theory; Traffic signal control systems; Traffic signal controllers; Traffic signals; Visualization
• Subject Areas: Data and Information Technology; Highways; Operations and Traffic Management;

Filing Info

• Accession Number: 01663977
• Record Type: Publication
• Report/Paper Numbers: 18-05800
• Files: TRIS, TRB, ATRI
• Created Date: Mar 22 2018 12:03PM