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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9573370 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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