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

dc.contributor.author Poddar, Subhadipto
dc.contributor.author Chakraborty, Pranamesh
dc.contributor.author Sharma, Anuj
dc.contributor.author Knickerbocker, Skylar
dc.contributor.author Hawkins, Neal
dc.contributor.department Department of Civil, Construction and Environmental Engineering
dc.contributor.department Institute for Transportation
dc.date 2021-04-30T15:53:54.000
dc.date.accessioned 2021-08-14T02:54:58Z
dc.date.available 2021-08-14T02:54:58Z
dc.date.copyright Wed Jan 01 00:00:00 UTC 2020
dc.date.issued 2022-05-03
dc.description.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.
dc.description.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.
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/ccee_pubs/289/
dc.identifier.articleid 1290
dc.identifier.contextkey 22629784
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath ccee_pubs/289
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/EzR2QMlz
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/ccee_pubs/289/2020_SharmaAnuj_MassivelyParallelizable.pdf|||Fri Jan 14 23:12:24 UTC 2022
dc.subject.disciplines Civil Engineering
dc.subject.disciplines Transportation Engineering
dc.subject.keywords Traffic Signal Retiming
dc.subject.keywords Arterial Corridor
dc.subject.keywords Signal Performance Measure
dc.subject.keywords Travel Time
dc.subject.keywords Probe Data
dc.subject.keywords Anomaly Detection
dc.title Massively parallelizable approach for evaluating signalized arterial performance using probe-based data
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication 717eae32-77e8-420a-b66c-a44c60495a6b
relation.isOrgUnitOfPublication 933e9c94-323c-4da9-9e8e-861692825f91
relation.isOrgUnitOfPublication 0cffd73a-b46d-4816-85f3-0f6ab7d2beb8
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