Massively parallelizable approach for evaluating signalized arterial performance using probe-based data

Poddar, Subhadipto; Chakraborty, Pranamesh; Sharma, Anuj; Knickerbocker, Skylar; Hawkins, Neal

Massively parallelizable approach for evaluating signalized arterial performance using probe-based data

File

2020_SharmaAnuj_MassivelyParallelizable.pdf (1.38 MB)

Date

2022-05-03

Authors

Poddar, Subhadipto

Chakraborty, Pranamesh

Sharma, Anuj

Knickerbocker, Skylar

Hawkins, Neal

Abstract

Effective performance of arterial corridors is essential to community safety and vitality. Considering the dynamic nature of traffic demand, efficient management of these corridors require frequent updating of the traffic signal timings through various strategies. Agency resources for these activities are commonly scarce and are primarily by public complaints. This study provides a workflow using probe-based data to measure and compare different segments on arterial corridors in terms of the traffic signal performance measures that can capture travel time dynamics across signalized intersections. The proposed methodology identifies a group of dynamic days followed by evaluation of travel rate based upon remaining non-dynamic days. Dynamic days represent the variability of traffic on a segment. Consequently, a corridor having high number of dynamic segments along with poor performance during normal days would be a candidate for adaptive control. Further, to handle the large-scale data source collected from city-wide or statewide traffic signals, the study adopts parallel computation-based strategy using MapReduce technique. A case study was conducted on 11 corridors within Des Moines, Iowa, to demonstrate the efficacy of the proposed approach, which identified two arterial corridors, Merle Hay Road and University Avenue, to be suitable for adaptive traffic signal control.

Academic or Administrative Unit

Department of Civil, Construction and Environmental Engineering

Institute for Transportation

Type

article

Comments

This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Intelligent Transportation Systems: Technology, Planning, and Operations on 03 May 2022. It is available online at DOI: 10.1080/15472450.2022.2069497. Copyright 2022 Taylor and Francis. Posted with permission.

Copyright

Wed Jan 01 00:00:00 UTC 2020