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

Tingting Huang, Subhadipto Poddar, Cristopher Aguilar, Anuj Sharma, Edward J Smaglik, Sirisha Kothuri, Peter Koonce

Research output: Contribution to journalArticle

2 Citations (Scopus)

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.

Original languageEnglish (US)
JournalTransportation Research Record
DOIs
StateAccepted/In press - Jan 1 2018

Fingerprint

Traffic signals
Data visualization
Quality control
Learning systems
Controllers
Sensors

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Mechanical Engineering

Cite this

Building Intelligence in Automated Traffic Signal Performance Measures with Advanced Data Analytics. / Huang, Tingting; Poddar, Subhadipto; Aguilar, Cristopher; Sharma, Anuj; Smaglik, Edward J; Kothuri, Sirisha; Koonce, Peter.

In: Transportation Research Record, 01.01.2018.

Research output: Contribution to journalArticle

Huang, Tingting ; Poddar, Subhadipto ; Aguilar, Cristopher ; Sharma, Anuj ; Smaglik, Edward J ; Kothuri, Sirisha ; Koonce, Peter. / Building Intelligence in Automated Traffic Signal Performance Measures with Advanced Data Analytics. In: Transportation Research Record. 2018.
@article{3c16952c94fe44fa8b01141fda696ab4,
title = "Building Intelligence in Automated Traffic Signal Performance Measures with Advanced Data Analytics",
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.",
author = "Tingting Huang and Subhadipto Poddar and Cristopher Aguilar and Anuj Sharma and Smaglik, {Edward J} and Sirisha Kothuri and Peter Koonce",
year = "2018",
month = "1",
day = "1",
doi = "10.1177/0361198118791380",
language = "English (US)",
journal = "Transportation Research Record",
issn = "0361-1981",
publisher = "US National Research Council",

}

TY - JOUR

T1 - Building Intelligence in Automated Traffic Signal Performance Measures with Advanced Data Analytics

AU - Huang, Tingting

AU - Poddar, Subhadipto

AU - Aguilar, Cristopher

AU - Sharma, Anuj

AU - Smaglik, Edward J

AU - Kothuri, Sirisha

AU - Koonce, Peter

PY - 2018/1/1

Y1 - 2018/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85052575592&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85052575592&partnerID=8YFLogxK

U2 - 10.1177/0361198118791380

DO - 10.1177/0361198118791380

M3 - Article

AN - SCOPUS:85052575592

JO - Transportation Research Record

JF - Transportation Research Record

SN - 0361-1981

ER -