Title

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

Campus Units

Civil, Construction and Environmental Engineering, Institute for Transportation

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

12-1-2018

Journal or Book Title

Transportation Research Record

Volume

2672

Issue

18

First Page

154

Last Page

166

DOI

10.1177%2F0361198118791380

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.

Research Focus Area

Transportation Engineering

Comments

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." Transportation Research Record 2672, no. 18 (2018): 154-166. DOI: 10.1177%2F0361198118791380. Posted with permission.

Copyright Owner

National Academy of Sciences: Transportation Research Board

Language

en

File Format

application/pdf

Published Version

Share

COinS