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Turning Movement Count Data Integration Methods for Intersection Analysis and Traffic Signal Design

Traffic simulation is widely used for modeling, planning, and analyzing different strategies for traffic control and road development in a cost-efficient manner. In order to perform an intersection simulation, random vehicle trip data are typically applied to an intersection network, making them unr...

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Autores principales: Shokrolah Shirazi, Mohammad, Chang, Hung-Fu, Tayeb, Shahab
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9573370/
https://www.ncbi.nlm.nih.gov/pubmed/36236207
http://dx.doi.org/10.3390/s22197111
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author Shokrolah Shirazi, Mohammad
Chang, Hung-Fu
Tayeb, Shahab
author_facet Shokrolah Shirazi, Mohammad
Chang, Hung-Fu
Tayeb, Shahab
author_sort Shokrolah Shirazi, Mohammad
collection PubMed
description Traffic simulation is widely used for modeling, planning, and analyzing different strategies for traffic control and road development in a cost-efficient manner. In order to perform an intersection simulation, random vehicle trip data are typically applied to an intersection network, making them unrealistic. In this paper, we address this issue by presenting two different methods of incorporating actual turning movement count (TMC) data and comparing their similarity for intersection simulation and analysis. The TMC of three intersections in Las Vegas are estimated separately for one hour using a developed vision-based tracking system and they are incorporated into Simulation of Urban MObility (SUMO) for estimating traffic measurements and traffic signal design. t-tests with a 95% confidence interval on the simulation variables demonstrate the importance of using a route-based creation method which injects vehicles into a simulation environment based on the frame-level departure time. The intersection analyses and comparisons are performed based on estimated traffic measurements such as travel time, density, lane density, occupancy, and normalized waiting time. Since the critical edge of each intersection network is identified based on a higher normalized waiting time, new traffic signal designs are suggested based on the actual critical turning movements and improvements in vehicle travel time are achieved to better accommodate the actual traffic demand.
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spelling pubmed-95733702022-10-17 Turning Movement Count Data Integration Methods for Intersection Analysis and Traffic Signal Design Shokrolah Shirazi, Mohammad Chang, Hung-Fu Tayeb, Shahab Sensors (Basel) Article Traffic simulation is widely used for modeling, planning, and analyzing different strategies for traffic control and road development in a cost-efficient manner. In order to perform an intersection simulation, random vehicle trip data are typically applied to an intersection network, making them unrealistic. In this paper, we address this issue by presenting two different methods of incorporating actual turning movement count (TMC) data and comparing their similarity for intersection simulation and analysis. The TMC of three intersections in Las Vegas are estimated separately for one hour using a developed vision-based tracking system and they are incorporated into Simulation of Urban MObility (SUMO) for estimating traffic measurements and traffic signal design. t-tests with a 95% confidence interval on the simulation variables demonstrate the importance of using a route-based creation method which injects vehicles into a simulation environment based on the frame-level departure time. The intersection analyses and comparisons are performed based on estimated traffic measurements such as travel time, density, lane density, occupancy, and normalized waiting time. Since the critical edge of each intersection network is identified based on a higher normalized waiting time, new traffic signal designs are suggested based on the actual critical turning movements and improvements in vehicle travel time are achieved to better accommodate the actual traffic demand. MDPI 2022-09-20 /pmc/articles/PMC9573370/ /pubmed/36236207 http://dx.doi.org/10.3390/s22197111 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Shokrolah Shirazi, Mohammad
Chang, Hung-Fu
Tayeb, Shahab
Turning Movement Count Data Integration Methods for Intersection Analysis and Traffic Signal Design
title Turning Movement Count Data Integration Methods for Intersection Analysis and Traffic Signal Design
title_full Turning Movement Count Data Integration Methods for Intersection Analysis and Traffic Signal Design
title_fullStr Turning Movement Count Data Integration Methods for Intersection Analysis and Traffic Signal Design
title_full_unstemmed Turning Movement Count Data Integration Methods for Intersection Analysis and Traffic Signal Design
title_short Turning Movement Count Data Integration Methods for Intersection Analysis and Traffic Signal Design
title_sort turning movement count data integration methods for intersection analysis and traffic signal design
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9573370/
https://www.ncbi.nlm.nih.gov/pubmed/36236207
http://dx.doi.org/10.3390/s22197111
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