Networked Sensor-Based Adaptive Traffic Signal Control for Dynamic Flow Optimization
With the rapid advancement of modern society, the demand for efficient and convenient transportation has increased significantly, making traffic congestion a pressing challenge that must be addressed in the process of urban expansion. To effectively mitigate this issue, we propose an approach that l...
Uložené v:
| Vydané v: | Sensors (Basel, Switzerland) Ročník 25; číslo 11; s. 3501 |
|---|---|
| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Switzerland
MDPI AG
01.06.2025
MDPI |
| Predmet: | |
| ISSN: | 1424-8220, 1424-8220 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | With the rapid advancement of modern society, the demand for efficient and convenient transportation has increased significantly, making traffic congestion a pressing challenge that must be addressed in the process of urban expansion. To effectively mitigate this issue, we propose an approach that leverages sensor networks to monitor real-time traffic data across road networks, enabling the precise characterization of traffic flow dynamics. This method integrates the Webster algorithm with a proportional–integral–derivative (PID) controller, whose parameters are optimized using a genetic algorithm, thereby facilitating scientifically informed traffic signal timing strategies for enhanced traffic regulation. Geomagnetic sensors are deployed along the roads at a ratio of 1:50–1:60, and radar sensors are deployed on the roadsides of key sections. This can effectively detect changes in road traffic flow and provide early warnings for possible accidents. The integration of the Webster method with a genetically optimized PID controller enables adaptive traffic signal timing with minimal energy consumption, effectively reducing road occupancy rates and mitigating congestion-related risks. Compared to conventional fixed-time control schemes, the proposed approach improves traffic regulation efficiency by 17.3%. Furthermore, it surpasses traditional real-time adaptive control strategies by 3% while significantly lowering communication energy expenditure. Notably, during peak hours, the genetically optimized PID controller enhances traffic control effectiveness by 13% relative to its non-optimized counterpart. A framework is proposed to improve the efficiency of road operation under the condition of random traffic changes. The k-means method is used to mark key roads, and weights are assigned based on this to coordinate and regulate traffic conditions. These findings underscore our contribution to the field of intelligent transportation systems by presenting a novel, energy-efficient, and highly effective traffic management solution. The proposed method not only advances the scientific understanding of dynamic traffic control but also offers a robust technical foundation for alleviating urban traffic congestion and improving overall travel efficiency. |
|---|---|
| AbstractList | With the rapid advancement of modern society, the demand for efficient and convenient transportation has increased significantly, making traffic congestion a pressing challenge that must be addressed in the process of urban expansion. To effectively mitigate this issue, we propose an approach that leverages sensor networks to monitor real-time traffic data across road networks, enabling the precise characterization of traffic flow dynamics. This method integrates the Webster algorithm with a proportional–integral–derivative (PID) controller, whose parameters are optimized using a genetic algorithm, thereby facilitating scientifically informed traffic signal timing strategies for enhanced traffic regulation. Geomagnetic sensors are deployed along the roads at a ratio of 1:50–1:60, and radar sensors are deployed on the roadsides of key sections. This can effectively detect changes in road traffic flow and provide early warnings for possible accidents. The integration of the Webster method with a genetically optimized PID controller enables adaptive traffic signal timing with minimal energy consumption, effectively reducing road occupancy rates and mitigating congestion-related risks. Compared to conventional fixed-time control schemes, the proposed approach improves traffic regulation efficiency by 17.3%. Furthermore, it surpasses traditional real-time adaptive control strategies by 3% while significantly lowering communication energy expenditure. Notably, during peak hours, the genetically optimized PID controller enhances traffic control effectiveness by 13% relative to its non-optimized counterpart. A framework is proposed to improve the efficiency of road operation under the condition of random traffic changes. The k-means method is used to mark key roads, and weights are assigned based on this to coordinate and regulate traffic conditions. These findings underscore our contribution to the field of intelligent transportation systems by presenting a novel, energy-efficient, and highly effective traffic management solution. The proposed method not only advances the scientific understanding of dynamic traffic control but also offers a robust technical foundation for alleviating urban traffic congestion and improving overall travel efficiency. With the rapid advancement of modern society, the demand for efficient and convenient transportation has increased significantly, making traffic congestion a pressing challenge that must be addressed in the process of urban expansion. To effectively mitigate this issue, we propose an approach that leverages sensor networks to monitor real-time traffic data across road networks, enabling the precise characterization of traffic flow dynamics. This method integrates the Webster algorithm with a proportional-integral-derivative (PID) controller, whose parameters are optimized using a genetic algorithm, thereby facilitating scientifically informed traffic signal timing strategies for enhanced traffic regulation. Geomagnetic sensors are deployed along the roads at a ratio of 1:50-1:60, and radar sensors are deployed on the roadsides of key sections. This can effectively detect changes in road traffic flow and provide early warnings for possible accidents. The integration of the Webster method with a genetically optimized PID controller enables adaptive traffic signal timing with minimal energy consumption, effectively reducing road occupancy rates and mitigating congestion-related risks. Compared to conventional fixed-time control schemes, the proposed approach improves traffic regulation efficiency by 17.3%. Furthermore, it surpasses traditional real-time adaptive control strategies by 3% while significantly lowering communication energy expenditure. Notably, during peak hours, the genetically optimized PID controller enhances traffic control effectiveness by 13% relative to its non-optimized counterpart. A framework is proposed to improve the efficiency of road operation under the condition of random traffic changes. The k-means method is used to mark key roads, and weights are assigned based on this to coordinate and regulate traffic conditions. These findings underscore our contribution to the field of intelligent transportation systems by presenting a novel, energy-efficient, and highly effective traffic management solution. The proposed method not only advances the scientific understanding of dynamic traffic control but also offers a robust technical foundation for alleviating urban traffic congestion and improving overall travel efficiency.With the rapid advancement of modern society, the demand for efficient and convenient transportation has increased significantly, making traffic congestion a pressing challenge that must be addressed in the process of urban expansion. To effectively mitigate this issue, we propose an approach that leverages sensor networks to monitor real-time traffic data across road networks, enabling the precise characterization of traffic flow dynamics. This method integrates the Webster algorithm with a proportional-integral-derivative (PID) controller, whose parameters are optimized using a genetic algorithm, thereby facilitating scientifically informed traffic signal timing strategies for enhanced traffic regulation. Geomagnetic sensors are deployed along the roads at a ratio of 1:50-1:60, and radar sensors are deployed on the roadsides of key sections. This can effectively detect changes in road traffic flow and provide early warnings for possible accidents. The integration of the Webster method with a genetically optimized PID controller enables adaptive traffic signal timing with minimal energy consumption, effectively reducing road occupancy rates and mitigating congestion-related risks. Compared to conventional fixed-time control schemes, the proposed approach improves traffic regulation efficiency by 17.3%. Furthermore, it surpasses traditional real-time adaptive control strategies by 3% while significantly lowering communication energy expenditure. Notably, during peak hours, the genetically optimized PID controller enhances traffic control effectiveness by 13% relative to its non-optimized counterpart. A framework is proposed to improve the efficiency of road operation under the condition of random traffic changes. The k-means method is used to mark key roads, and weights are assigned based on this to coordinate and regulate traffic conditions. These findings underscore our contribution to the field of intelligent transportation systems by presenting a novel, energy-efficient, and highly effective traffic management solution. The proposed method not only advances the scientific understanding of dynamic traffic control but also offers a robust technical foundation for alleviating urban traffic congestion and improving overall travel efficiency. |
| Audience | Academic |
| Author | Wang, Xinhai Shao, Wenhua |
| AuthorAffiliation | School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China; kelly@bupt.edu.cn |
| AuthorAffiliation_xml | – name: School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China; kelly@bupt.edu.cn |
| Author_xml | – sequence: 1 givenname: Xinhai surname: Wang fullname: Wang, Xinhai – sequence: 2 givenname: Wenhua surname: Shao fullname: Shao, Wenhua |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40969099$$D View this record in MEDLINE/PubMed |
| BookMark | eNptkk1v1DAQhiNURD_gwB9AkbjAIa0_4tg-oWWhUKmihy5ny7HHi5fE3trZVuXX43bLqq2QD7ZnHr8z45nDai_EAFX1FqNjSiU6yYRhTBnCL6oD3JK2EYSgvUfn_eow5xVChFIqXlX7LZKdRFIeVIsfMN3E9BtsfQkhx9R81rlcZlavJ38N9SJp57ypL_0y6KGexzClONQupvrLbdBjcZ0O8aa-KPjo_-jJx_C6eun0kOHNw35U_Tz9uph_b84vvp3NZ-eNKflODTfQus7oXhLZM2wEbx0gw6EjFmuNJbjOMUHAIt5TbYlDzHIumICOGtnRo-psq2ujXql18qNOtypqr-4NMS2VTpM3AyjTMiQktlwCK0F7QXjXASDsuO46SorWp63WetOPYA2UOvXwRPSpJ_hfahmvFSaYCSpZUfjwoJDi1QbypEafDQyDDhA3WVHCCCGMSV7Q98_QVdyk8r93FOa85ZTTQh1vqaUuFfjgYglsyrJQvr2MgPPFPhMtaznfZvDucQ275P-1uwAft4BJMecEbodgpO5GSe1GqbAnz1jjp_v2liz88J8XfwEmZ8kF |
| CitedBy_id | crossref_primary_10_3390_s25185760 |
| Cites_doi | 10.1109/TVT.2022.3160871 10.1016/S0019-9958(65)90241-X 10.1109/TITS.2022.3195221 10.1109/TITS.2024.3399066 10.1109/IMSCCS.2006.286 10.1109/JIOT.2024.3401829 10.1063/5.0193957 10.3390/math12132056 10.1016/B978-0-08-029365-3.50048-1 10.1109/ACCESS.2023.3278986 10.1016/j.engappai.2025.110440 10.1109/TITS.2024.3397077 10.1177/0361198105191700119 10.1177/0037549718777615 10.1109/IMCET.2018.8603041 10.1109/MCS.2006.1580151 10.1109/TMAG.2012.2220535 10.1109/JSEN.2018.2800009 10.1088/1755-1315/371/5/052034 10.3390/sym16020240 10.1109/NEMS.2009.5068589 10.1007/s44163-023-00087-z 10.1016/j.engappai.2017.10.013 10.1109/ACCESS.2021.3094270 10.1109/TSMC.1973.5408575 10.1007/978-3-319-14203-6_8 10.1287/trsc.31.1.5 10.1117/12.2684871 10.1007/978-3-031-48933-4_11 |
| ContentType | Journal Article |
| Copyright | COPYRIGHT 2025 MDPI AG 2025 by the authors. 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/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2025 by the authors. 2025 |
| Copyright_xml | – notice: COPYRIGHT 2025 MDPI AG – notice: 2025 by the authors. 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/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: 2025 by the authors. 2025 |
| DBID | AAYXX CITATION NPM 3V. 7X7 7XB 88E 8FI 8FJ 8FK ABUWG AFKRA AZQEC BENPR CCPQU DWQXO FYUFA GHDGH K9. M0S M1P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQQKQ PQUKI PRINS 7X8 5PM DOA |
| DOI | 10.3390/s25113501 |
| DatabaseName | CrossRef PubMed ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One Community College ProQuest Central Korea Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Health & Medical Complete (Alumni) Health & Medical Collection (Alumni Edition) PML(ProQuest Medical Library) ProQuest Central Premium ProQuest One Academic (New) ProQuest Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef PubMed Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Central China ProQuest Central ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Health & Medical Research Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic Publicly Available Content Database PubMed CrossRef |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1424-8220 |
| ExternalDocumentID | oai_doaj_org_article_c450891d79e54f6b82766ee01f7a6632 PMC12158395 A845477395 40969099 10_3390_s25113501 |
| Genre | Journal Article |
| GeographicLocations | China |
| GeographicLocations_xml | – name: China |
| GroupedDBID | --- 123 2WC 53G 5VS 7X7 88E 8FE 8FG 8FI 8FJ AADQD AAHBH AAYXX ABDBF ABUWG ACUHS ADBBV ADMLS AENEX AFFHD AFKRA AFZYC ALMA_UNASSIGNED_HOLDINGS BENPR BPHCQ BVXVI CCPQU CITATION CS3 D1I DU5 E3Z EBD ESX F5P FYUFA GROUPED_DOAJ GX1 HH5 HMCUK HYE IAO ITC KQ8 L6V M1P M48 MODMG M~E OK1 OVT P2P P62 PHGZM PHGZT PIMPY PJZUB PPXIY PQQKQ PROAC PSQYO RNS RPM TUS UKHRP XSB ~8M NPM PUEGO 3V. 7XB 8FK AZQEC DWQXO K9. PKEHL PQEST PQUKI PRINS 7X8 5PM |
| ID | FETCH-LOGICAL-c511t-7ce4f6cab929b51c874fe0c7e62d1aa19ef6f582ed07b3ad2f05d77858e63c963 |
| IEDL.DBID | BENPR |
| ISICitedReferencesCount | 1 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001508140600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1424-8220 |
| IngestDate | Mon Nov 10 04:35:16 EST 2025 Tue Nov 04 02:01:59 EST 2025 Fri Sep 19 21:00:32 EDT 2025 Tue Oct 07 07:42:29 EDT 2025 Tue Nov 04 18:15:24 EST 2025 Tue Sep 23 02:21:37 EDT 2025 Sat Nov 29 07:08:10 EST 2025 Tue Nov 18 21:57:45 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 11 |
| Keywords | PID controller adaptive control sensor networks traffic signal timing traffic flow |
| Language | English |
| License | 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/). |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c511t-7ce4f6cab929b51c874fe0c7e62d1aa19ef6f582ed07b3ad2f05d77858e63c963 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| OpenAccessLink | https://www.proquest.com/docview/3217747373?pq-origsite=%requestingapplication% |
| PMID | 40969099 |
| PQID | 3217747373 |
| PQPubID | 2032333 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_c450891d79e54f6b82766ee01f7a6632 pubmedcentral_primary_oai_pubmedcentral_nih_gov_12158395 proquest_miscellaneous_3252225597 proquest_journals_3217747373 gale_infotracacademiconefile_A845477395 pubmed_primary_40969099 crossref_primary_10_3390_s25113501 crossref_citationtrail_10_3390_s25113501 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-06-01 |
| PublicationDateYYYYMMDD | 2025-06-01 |
| PublicationDate_xml | – month: 06 year: 2025 text: 2025-06-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Switzerland |
| PublicationPlace_xml | – name: Switzerland – name: Basel |
| PublicationTitle | Sensors (Basel, Switzerland) |
| PublicationTitleAlternate | Sensors (Basel) |
| PublicationYear | 2025 |
| Publisher | MDPI AG MDPI |
| Publisher_xml | – name: MDPI AG – name: MDPI |
| References | Lin (ref_17) 2024; 25 Nair (ref_35) 2024; 3059 Ali (ref_36) 2021; 9 ref_14 Yang (ref_7) 2024; 25 Zhu (ref_18) 2024; 11 ref_34 ref_11 ref_10 ref_32 ref_31 Zou (ref_45) 2019; 95 Zadeh (ref_28) 1973; SMC–3 Bai (ref_24) 2018; 18 Vopalensky (ref_22) 2013; 49 Yin (ref_38) 2019; 371 ref_39 ref_16 Balamutas (ref_26) 2023; 11 Wang (ref_5) 2025; 149 ref_37 Jin (ref_46) 2018; 68 Cheung (ref_9) 2005; 1917 Wang (ref_41) 2018; 31 Chabchoub (ref_19) 2021; 12 Azad (ref_40) 2023; 3 Zadeh (ref_27) 1965; 8 ref_25 Sen (ref_33) 1997; 31 Liu (ref_13) 2022; 71 Goel (ref_20) 2012; 40 ref_23 Shou (ref_15) 2012; 29 ref_21 Wu (ref_42) 2013; 10 ref_43 ref_1 Knospe (ref_44) 2006; 26 ref_2 Cano (ref_12) 2024; 252 Pt B ref_29 Lin (ref_3) 2023; 24 ref_8 Gartner (ref_30) 1983; 906 ref_4 ref_6 |
| References_xml | – volume: 71 start-page: 5960 year: 2022 ident: ref_13 article-title: Intelligent Traffic Light Control by Exploring Strategies in an Optimised Space of Deep Q-Learning publication-title: IEEE Trans. Veh. Technol. doi: 10.1109/TVT.2022.3160871 – volume: 8 start-page: 338 year: 1965 ident: ref_27 article-title: Fuzzy sets publication-title: Inf. Control. doi: 10.1016/S0019-9958(65)90241-X – volume: 10 start-page: 37 year: 2013 ident: ref_42 article-title: Application and Theoretical Basis of PID Control publication-title: Control Eng. China – volume: 24 start-page: 8555 year: 2023 ident: ref_3 article-title: Traffic Signal Optimization Based on Fuzzy Control and Differential Evolution Algorithm publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2022.3195221 – volume: 25 start-page: 14196 year: 2024 ident: ref_7 article-title: Enhancing robustness of deep reinforcement learning based adaptive traffic signal controllers in mixed traffic environments through data fusion and multi-discrete actions publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2024.3399066 – ident: ref_10 doi: 10.1109/IMSCCS.2006.286 – volume: 11 start-page: 28496 year: 2024 ident: ref_18 article-title: Adaptive Broad Deep Reinforcement Learning for Intelligent Traffic Light Control publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2024.3401829 – ident: ref_34 – ident: ref_11 – volume: 3059 start-page: 030020 year: 2024 ident: ref_35 article-title: Design of traffic signal at Puthuppally junction using webster method publication-title: AIP Conf. Proc. doi: 10.1063/5.0193957 – ident: ref_39 – ident: ref_6 doi: 10.3390/math12132056 – ident: ref_14 – ident: ref_32 doi: 10.1016/B978-0-08-029365-3.50048-1 – ident: ref_1 – ident: ref_21 – volume: 31 start-page: 126 year: 2018 ident: ref_41 article-title: Comparative Study and Evaluation of Incremental PID and Positional PID Algorithms publication-title: Ind. Control Comput. – volume: 11 start-page: 50984 year: 2023 ident: ref_26 article-title: Passing vehicle road occupancy detection using the magnetic sensor array publication-title: IEEE Access doi: 10.1109/ACCESS.2023.3278986 – volume: 149 start-page: 110440 year: 2025 ident: ref_5 article-title: An adaptive traffic signal control scheme with Proximal Policy Optimization based on deep reinforcement learning for a single intersection publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2025.110440 – volume: 25 start-page: 15053 year: 2024 ident: ref_17 article-title: Problem-Specific Knowledge Based Multi-Objective Meta-Heuristics Combined Q-Learning for Scheduling Urban Traffic Lights With Carbon Emissions publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2024.3397077 – ident: ref_8 – volume: 40 start-page: 36 year: 2012 ident: ref_20 article-title: Intelligent traffic light system to prioritized emergency purpose vehicles based on wireless sensor network publication-title: Int. J. Comput. Appl. – ident: ref_31 – volume: 1917 start-page: 173 year: 2005 ident: ref_9 article-title: Traffic measurement and vehicle clas sification with a single magnetic sensor publication-title: Transp. Res. Rec. doi: 10.1177/0361198105191700119 – volume: 252 Pt B start-page: 124178 year: 2024 ident: ref_12 article-title: Improving traffic light systems using Deep Q-networks publication-title: Expert Syst. Appl. – ident: ref_2 – volume: 95 start-page: 271 year: 2019 ident: ref_45 article-title: Self-organization models of urban traffic lights based on digital infochemicals publication-title: Simulation doi: 10.1177/0037549718777615 – ident: ref_16 doi: 10.1109/IMCET.2018.8603041 – volume: 26 start-page: 30 year: 2006 ident: ref_44 article-title: PID control publication-title: IEEE Control. Syst. Mag. doi: 10.1109/MCS.2006.1580151 – volume: 49 start-page: 136 year: 2013 ident: ref_22 article-title: Temperature Drift of Offset and Sensitivity in Full-Bridge Magnetoresistive Sensors publication-title: IEEE Trans. Magn. doi: 10.1109/TMAG.2012.2220535 – volume: 18 start-page: 2713 year: 2018 ident: ref_24 article-title: Research on an improved resonant cavity for overhauser geomagnetic sensor publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2018.2800009 – volume: 371 start-page: 052034 year: 2019 ident: ref_38 article-title: InteQQArsection signal timing optimization based on Webster timing method publication-title: IOP Conf. Ser. Earth Environ. Sci. doi: 10.1088/1755-1315/371/5/052034 – volume: 12 start-page: 396 year: 2021 ident: ref_19 article-title: Intelligent traffic light controller using fuzzy logic and image processing publication-title: Int. J. Adv. Comput. Sci. Appl. – ident: ref_4 doi: 10.3390/sym16020240 – ident: ref_23 doi: 10.1109/NEMS.2009.5068589 – volume: 29 start-page: 96 year: 2012 ident: ref_15 article-title: Multi-Objective Dynamic Decision Model and Optimization Method for Signalized Intersections publication-title: J. Highw. Transp. Res. Dev. – volume: 3 start-page: 39 year: 2023 ident: ref_40 article-title: Smart control of traffic lights based on traffic density in the multi-intersection network by using Q learning publication-title: Discov. Artif. Intell. doi: 10.1007/s44163-023-00087-z – ident: ref_43 – volume: 68 start-page: 236 year: 2018 ident: ref_46 article-title: Hierarchical multi-agent control of traffic lights based on collective learning publication-title: Eng. Appl. Artif. Intell. doi: 10.1016/j.engappai.2017.10.013 – volume: 9 start-page: 102985 year: 2021 ident: ref_36 article-title: An adaptive method for traffic signal control based on fuzzy logic with webster and modified webster formula using SUMO traffic simulator publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3094270 – volume: 906 start-page: 75 year: 1983 ident: ref_30 article-title: OPAC: A demand responsive strategy for traffic signal control publication-title: Transp. Res. Rec. – volume: SMC–3 start-page: 28 year: 1973 ident: ref_28 article-title: Outline of a New Approach to the Analysis of Complex Systems and Decision Processes publication-title: IEEE Trans. Syst. Man Cybern. doi: 10.1109/TSMC.1973.5408575 – ident: ref_29 doi: 10.1007/978-3-319-14203-6_8 – volume: 31 start-page: 5 year: 1997 ident: ref_33 article-title: Controlled optimization of phases at an intersection publication-title: Transp. Sci. doi: 10.1287/trsc.31.1.5 – ident: ref_25 doi: 10.1117/12.2684871 – ident: ref_37 doi: 10.1007/978-3-031-48933-4_11 |
| SSID | ssj0023338 |
| Score | 2.4567626 |
| Snippet | With the rapid advancement of modern society, the demand for efficient and convenient transportation has increased significantly, making traffic congestion a... |
| SourceID | doaj pubmedcentral proquest gale pubmed crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source |
| StartPage | 3501 |
| SubjectTerms | adaptive control Algorithms Control equipment Controllers Efficiency Emergency communications systems Energy consumption Genetic algorithms Methods Motor vehicle fleets Optimization PID controller Radar systems sensor networks Sensors Traffic congestion Traffic control Traffic flow traffic signal timing Urban areas |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwEB6higMcEG9CCzIICS5R8_Arx21hxQEtSC1Sb5ZfgZXabLW7hb_fmTgbbQQSF67JKLLHM5754vE3AO94EMHFwPNaaplzoXjeiDbkrcDsvNC2FG1Pmf9FLRb64qL5ttfqi2rCEj1wUtyx55hCNGVQTRS8lU5XSsoYi7JVFqNlv_sWqtmBqQFq1Yi8Eo9QjaD-eEOJNB2hTaJPT9L_51a8F4umdZJ7gWf-EB4MGSObpZE-gjuxewz393gEn8D5IhVzx8DOEJau1vkJBqfAZsFe03bGMCIRVQQ7W_6gb52m-nSGCSv7mFrSs_nl6jf7iuJXw83Mp_B9_un89HM-tEvIPc5umysfUUHeOsx4nCi9VryNhVdRVqG0tmxiK3EJqhgK5WobqrYQQSktdJS1R0d8BgfdqosvgDnuAtUcRicbrkPUha-oR4cUwtkqyAw-7NRo_MAlTi0tLg1iCtK4GTWewdtR9DoRaPxN6ITWYhQgzuv-AVqCGSzB_MsSMnhPK2nIM3Ew3g4XDHBKxHFlZprIy-hgMoOj3WKbwWU3pkZwhtiqVnUGb8bX6Gx0gmK7uLohGUH4GEFYBs-TbYxjRqAsG8y3M9ATq5lMavqmW_7sCb2J4QMTVfHyf6jhEO5V1KO4_1N0BAfb9U18BXf9r-1ys37du8ktbmwWow priority: 102 providerName: Directory of Open Access Journals |
| Title | Networked Sensor-Based Adaptive Traffic Signal Control for Dynamic Flow Optimization |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/40969099 https://www.proquest.com/docview/3217747373 https://www.proquest.com/docview/3252225597 https://pubmed.ncbi.nlm.nih.gov/PMC12158395 https://doaj.org/article/c450891d79e54f6b82766ee01f7a6632 |
| Volume | 25 |
| WOSCitedRecordID | wos001508140600001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: DOA dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: M~E dateStart: 20010101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: 7X7 dateStart: 20010101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: BENPR dateStart: 20010101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1424-8220 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0023338 issn: 1424-8220 databaseCode: PIMPY dateStart: 20010101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lj9MwEB6xXQ5w4L1sYKkCQoJLtHn5kRNql1YgsaViF6mcIsd2lkpL0m27cOO3M5O4oRWIC5cc7FFkZzyeb-zJNwAvU8NMYU0aJFzyIGUiDTJWmqBkiM5DqSJWNpT5H8RkImezbOoO3FYurXKzJzYbtak1nZEfJ4idEfomInmzuAqoahTdrroSGnuwT0xlcQ_2h6PJ9FMXciUYgbV8QgkG98crAtR0lbbjhRqy_j-35C2ftJsvueWAxnf_d-j34I6Dnv6gXSv34YatHsDtLULCh3A-abPCrfHPML6tl8EQvZzxB0YtaF_00bUR54R_Nr-gd520ie4-Il__bVvb3h9f1j_8jyj-zf3i-Qg-j0fnJ-8CV3ch0Ph51oHQNi25VgVCp4JFWoq0tKEWlscmUirKbMlRl7E1oSgSZeIyZEYIyaTliUaLPoBeVVf2EPwiLQwlL9qCZ6k0VoY6pmIfnLFCxYZ78Hqjh1w7UnKqjXGZY3BCKss7lXnwohNdtEwcfxMakjI7ASLPbhrq5UXubDHXKaLSLDIiswwnWshYcG5tGJVCIQCLPXhFSyEnE8fBaOX-VMApEVlWPpDEgkY3nB4cbTSeO9tf5b_V7cHzrhutlq5iVGXra5JhFGhjNOfB43ZxdWPGiJtnCNw9kDvLbmdSuz3V_GvDDE5UIYh42ZN_j-sp3IqpjHFzmHQEvfXy2j6Dm_r7er5a9mFPzETzlH1nT_3mqAKfpz9H2DZ9fzr98gtXmSyQ |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3fb9MwED6NDgl44PePwICAQPASLXHsOHlAqN1WbVopE-ukvQXHdkalLSltx7R_ir-RuyQNrUC87YHX2Irs5PPdd_b5O4A33AiTWcO9MIojjwvJvUTkxssFsnM_VoHIK8n8gRwO4-Pj5GANfi7uwlBa5cImVobalJr2yDdD5M5IfUMZfpx896hqFJ2uLkpo1LDYt5cXGLLNPuxt4_99y1h_Z7S16zVVBTyN5GLuSW15HmmVITHIRKBjyXPra2kjZgKlgsTmEY6UWePLLFSG5b4wUsYitlGoEa_43muwzkPOWQfWezvDgy9tiBdixFfrF4Vh4m_OiMDT0d2K16uKA_zpApZ84Gp-5pLD69_53z7VXbjdUGu3W6-Fe7Bmi_twa0lw8QGMhnXWuzXuIcbv5dTroRc3bteoCdl9F103aWq4h-MTetdWncjvIrN3ty8LdYZN_dPywv2M3c-aK6wP4ehKpvUIOkVZ2CfgZjwzlJxpsyjhsbGxrxkVM4mEyBQzkQPvF_891Y3oOtX-OE0x-CKIpC1EHHjddp3USiN_69Qj8LQdSBy8elBOT9LG1qSaI-tOAiMTK3CiWcxkFFnrB7lUSDCZA-8IeimZMByMVs1NDJwSiYGl3ZhU3ugE14GNBcLSxrbN0t_wcuBV24xWiY6aVGHLc-ojaCMBo1UHHtdgbsfMMWpOMDBxIF6B-cqkVluK8bdK-ZykUJDRi6f_HtdLuLE7-jRIB3vD_Wdwk1HJ5mrjbAM68-m5fQ7X9Y_5eDZ90axfF75e9Tr4BRUJhXA |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9NAEB6VFCE48H4YChgEgosVv_bhA0JJ04iqVYhoK_Vm1rvrEqm1Q5JS9a_x65ixHZMIxK0Hrt6VtWt_O_PN7uw3AG9iw0xmTexFXHIvZiL2EpYbL2fIzn2pApZXkvn7YjSSx8fJeAN-Lu_CUFrl0iZWhtqUmvbIuxFyZ6S-kYi6eZMWMR4MP06_e1RBik5al-U0aojs2csLDN_mH3YH-K_fhuFw53D7k9dUGPA0Eo2FJ7SNc65VhiQhY4GWIs6tr4XloQmUChKbcxx1aI0vskiZMPeZEUIyaXmkEbv43muwKdDrxx3Y7O-Mxl_acC_C6K_WMoqixO_OiczTMd6aB6wKBfzpDlb84Xqu5orzG975nz_bXbjdUG63V6-Re7Bhi_twa0WI8QEcjupseGvcA4zry5nXR-9u3J5RU_IHLrp00tpwDyYn9K7tOsHfRcbvDi4LdYZNw9Pywv2M3c-aq60P4ehKpvUIOkVZ2CfgZnFmKGnTZjyJpbHS1yEVOeGMZSo03IH3SwykuhFjp5ogpykGZQSXtIWLA6_brtNageRvnfoEpLYDiYZXD8rZSdrYoFTHyMaTwIjEMpxoJkPBubV-kAuFxDN04B3BMCXThoPRqrmhgVMikbC0J0n9jU52Hdhaoi1tbN48_Q01B161zWit6AhKFbY8pz6MNhgwinXgcQ3sdswxRtMJBiwOyDXIr01qvaWYfKsU0UkiBZk-e_rvcb2EGwj-dH93tPcMboZUybnaT9uCzmJ2bp_Ddf1jMZnPXjRL2YWvV70MfgE7L448 |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Networked+Sensor-Based+Adaptive+Traffic+Signal+Control+for+Dynamic+Flow+Optimization&rft.jtitle=Sensors+%28Basel%2C+Switzerland%29&rft.au=Wang%2C+Xinhai&rft.au=Shao%2C+Wenhua&rft.date=2025-06-01&rft.eissn=1424-8220&rft.volume=25&rft.issue=11&rft_id=info:doi/10.3390%2Fs25113501&rft_id=info%3Apmid%2F40969099&rft.externalDocID=40969099 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1424-8220&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1424-8220&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1424-8220&client=summon |