Real-time Intersection Optimization for Signal Phasing, Timing, and Automated Vehicles' Trajectories
This study aims to develop a real-time intersection optimization (RIO) control algorithm to efficiently serve traffic of Connected and Automated Vehicles (CAVs) and conventional vehicles (CNVs). This paper extends previous work to consider demand over capacity conditions and trajectory deviations by...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | |
| container_start_page | |
| container_title | |
| container_volume | |
| creator | Pourmehrab, Mahmoud Elefteriadou, Lily Ranka, Sanjay |
| description | This study aims to develop a real-time intersection optimization (RIO)
control algorithm to efficiently serve traffic of Connected and Automated
Vehicles (CAVs) and conventional vehicles (CNVs). This paper extends previous
work to consider demand over capacity conditions and trajectory deviations by
re-optimizing decisions. To jointly optimize Signal Phase and Timing (SPaT) and
departure time of CAVs, we formulated a joint optimization model which is
reduced to and solved as a Minimum Cost Flow (MCF) problem. The MCF-based
optimization models is embedded into the RIO algorithm to operate the signal
controller and to plan the movement of CAVs. Simulation experiments showed
18-22% travel time decrease and up to 12% capacity improvement compared to the
base scenario. |
| doi_str_mv | 10.48550/arxiv.2007.03763 |
| format | Article |
| fullrecord | <record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_2007_03763</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2007_03763</sourcerecordid><originalsourceid>FETCH-LOGICAL-a673-38ba6a5b88f6e9ac932a4573594e8324a45925fda267f39db5693a5483c43daf3</originalsourceid><addsrcrecordid>eNotjz1PwzAURb0woMIPYMIbCwkhz3bssar4qFSpCCLW6CV-bo0Sp3ICAn49acp0de6VrnQYu7rPUqGlzO4wfvuvNM-yIs2gUHDO7Cthm4y-I74OI8WBmtH3gW8PU-d_cQbXR_7mdwFb_rLHwYfdLS-n-ZgYLF9-jn2HI1n-TnvftDTc8DLix_TVR0_DBTtz2A50-Z8LVj4-lKvnZLN9Wq-WmwRVAQnoGhXKWmunyGBjIEchC5BGkIZcTGBy6SzmqnBgbC2VAZRCQyPAooMFuz7dzprVIfoO40911K1mXfgDPYtQjg</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Real-time Intersection Optimization for Signal Phasing, Timing, and Automated Vehicles' Trajectories</title><source>arXiv.org</source><creator>Pourmehrab, Mahmoud ; Elefteriadou, Lily ; Ranka, Sanjay</creator><creatorcontrib>Pourmehrab, Mahmoud ; Elefteriadou, Lily ; Ranka, Sanjay</creatorcontrib><description>This study aims to develop a real-time intersection optimization (RIO)
control algorithm to efficiently serve traffic of Connected and Automated
Vehicles (CAVs) and conventional vehicles (CNVs). This paper extends previous
work to consider demand over capacity conditions and trajectory deviations by
re-optimizing decisions. To jointly optimize Signal Phase and Timing (SPaT) and
departure time of CAVs, we formulated a joint optimization model which is
reduced to and solved as a Minimum Cost Flow (MCF) problem. The MCF-based
optimization models is embedded into the RIO algorithm to operate the signal
controller and to plan the movement of CAVs. Simulation experiments showed
18-22% travel time decrease and up to 12% capacity improvement compared to the
base scenario.</description><identifier>DOI: 10.48550/arxiv.2007.03763</identifier><language>eng</language><subject>Mathematics - Optimization and Control</subject><creationdate>2020-07</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2007.03763$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2007.03763$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Pourmehrab, Mahmoud</creatorcontrib><creatorcontrib>Elefteriadou, Lily</creatorcontrib><creatorcontrib>Ranka, Sanjay</creatorcontrib><title>Real-time Intersection Optimization for Signal Phasing, Timing, and Automated Vehicles' Trajectories</title><description>This study aims to develop a real-time intersection optimization (RIO)
control algorithm to efficiently serve traffic of Connected and Automated
Vehicles (CAVs) and conventional vehicles (CNVs). This paper extends previous
work to consider demand over capacity conditions and trajectory deviations by
re-optimizing decisions. To jointly optimize Signal Phase and Timing (SPaT) and
departure time of CAVs, we formulated a joint optimization model which is
reduced to and solved as a Minimum Cost Flow (MCF) problem. The MCF-based
optimization models is embedded into the RIO algorithm to operate the signal
controller and to plan the movement of CAVs. Simulation experiments showed
18-22% travel time decrease and up to 12% capacity improvement compared to the
base scenario.</description><subject>Mathematics - Optimization and Control</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotjz1PwzAURb0woMIPYMIbCwkhz3bssar4qFSpCCLW6CV-bo0Sp3ICAn49acp0de6VrnQYu7rPUqGlzO4wfvuvNM-yIs2gUHDO7Cthm4y-I74OI8WBmtH3gW8PU-d_cQbXR_7mdwFb_rLHwYfdLS-n-ZgYLF9-jn2HI1n-TnvftDTc8DLix_TVR0_DBTtz2A50-Z8LVj4-lKvnZLN9Wq-WmwRVAQnoGhXKWmunyGBjIEchC5BGkIZcTGBy6SzmqnBgbC2VAZRCQyPAooMFuz7dzprVIfoO40911K1mXfgDPYtQjg</recordid><startdate>20200702</startdate><enddate>20200702</enddate><creator>Pourmehrab, Mahmoud</creator><creator>Elefteriadou, Lily</creator><creator>Ranka, Sanjay</creator><scope>AKZ</scope><scope>GOX</scope></search><sort><creationdate>20200702</creationdate><title>Real-time Intersection Optimization for Signal Phasing, Timing, and Automated Vehicles' Trajectories</title><author>Pourmehrab, Mahmoud ; Elefteriadou, Lily ; Ranka, Sanjay</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a673-38ba6a5b88f6e9ac932a4573594e8324a45925fda267f39db5693a5483c43daf3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Mathematics - Optimization and Control</topic><toplevel>online_resources</toplevel><creatorcontrib>Pourmehrab, Mahmoud</creatorcontrib><creatorcontrib>Elefteriadou, Lily</creatorcontrib><creatorcontrib>Ranka, Sanjay</creatorcontrib><collection>arXiv Mathematics</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Pourmehrab, Mahmoud</au><au>Elefteriadou, Lily</au><au>Ranka, Sanjay</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Real-time Intersection Optimization for Signal Phasing, Timing, and Automated Vehicles' Trajectories</atitle><date>2020-07-02</date><risdate>2020</risdate><abstract>This study aims to develop a real-time intersection optimization (RIO)
control algorithm to efficiently serve traffic of Connected and Automated
Vehicles (CAVs) and conventional vehicles (CNVs). This paper extends previous
work to consider demand over capacity conditions and trajectory deviations by
re-optimizing decisions. To jointly optimize Signal Phase and Timing (SPaT) and
departure time of CAVs, we formulated a joint optimization model which is
reduced to and solved as a Minimum Cost Flow (MCF) problem. The MCF-based
optimization models is embedded into the RIO algorithm to operate the signal
controller and to plan the movement of CAVs. Simulation experiments showed
18-22% travel time decrease and up to 12% capacity improvement compared to the
base scenario.</abstract><doi>10.48550/arxiv.2007.03763</doi><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | DOI: 10.48550/arxiv.2007.03763 |
| ispartof | |
| issn | |
| language | eng |
| recordid | cdi_arxiv_primary_2007_03763 |
| source | arXiv.org |
| subjects | Mathematics - Optimization and Control |
| title | Real-time Intersection Optimization for Signal Phasing, Timing, and Automated Vehicles' Trajectories |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-23T17%3A53%3A45IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Real-time%20Intersection%20Optimization%20for%20Signal%20Phasing,%20Timing,%20and%20Automated%20Vehicles'%20Trajectories&rft.au=Pourmehrab,%20Mahmoud&rft.date=2020-07-02&rft_id=info:doi/10.48550/arxiv.2007.03763&rft_dat=%3Carxiv_GOX%3E2007_03763%3C/arxiv_GOX%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |