Urban Traffic Signals Timing at Four-Phase Signalized Intersection Based on Optimized Two-Stage Fuzzy Control Scheme

This paper proposes a signal timing scheme through a two-stage fuzzy logic controller. The controller first determines the signal phase and then adjusts the green time. At the first stage, the adaptive membership function of vehicle arrival rate is improved to adapt to the changing traffic flow. In...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Taiping Jiang [verfasserIn] Zili Wang [verfasserIn] Fuyang Chen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Übergeordnetes Werk:	In: Mathematical Problems in Engineering - Hindawi Limited, 2002, (2021)
Übergeordnetes Werk:	year:2021

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1155/2021/6693562

Katalog-ID:	DOAJ071712429

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allfields	10.1155/2021/6693562 doi (DE-627)DOAJ071712429 (DE-599)DOAJb2f0d83d4d6b4a50aaeef2c09218b677 DE-627 ger DE-627 rakwb eng TA1-2040 QA1-939 Taiping Jiang verfasserin aut Urban Traffic Signals Timing at Four-Phase Signalized Intersection Based on Optimized Two-Stage Fuzzy Control Scheme 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper proposes a signal timing scheme through a two-stage fuzzy logic controller. The controller first determines the signal phase and then adjusts the green time. At the first stage, the adaptive membership function of vehicle arrival rate is improved to adapt to the changing traffic flow. In addition to arrival rate and queue vehicles, a specific phase order rule is considered to avoid disordered phase selection in fuzzy control. At the second stage, the green time detection module decides whether to extend the current green time or switch phases every few seconds and the vehicle arrival rate is not required as the input to controller in real-time detection. Differential evolution algorithm with low space complexity and fast convergence is applied to optimize the fuzzy rules for avoiding artificial uncertainty. Simulation experiments are designed to compare traditional fuzzy controller, fixed-time controller, and fuzzy controller without flow prediction. Results show that the current proposed method in this paper can reduce vehicle delay significantly. Engineering (General). Civil engineering (General) Mathematics Zili Wang verfasserin aut Fuyang Chen verfasserin aut In Mathematical Problems in Engineering Hindawi Limited, 2002 (2021) (DE-627)320519937 (DE-600)2014442-8 1024123X nnns year:2021 https://doi.org/10.1155/2021/6693562 kostenfrei https://doaj.org/article/b2f0d83d4d6b4a50aaeef2c09218b677 kostenfrei http://dx.doi.org/10.1155/2021/6693562 kostenfrei https://doaj.org/toc/1024-123X Journal toc kostenfrei https://doaj.org/toc/1563-5147 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2088 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2021
spelling	10.1155/2021/6693562 doi (DE-627)DOAJ071712429 (DE-599)DOAJb2f0d83d4d6b4a50aaeef2c09218b677 DE-627 ger DE-627 rakwb eng TA1-2040 QA1-939 Taiping Jiang verfasserin aut Urban Traffic Signals Timing at Four-Phase Signalized Intersection Based on Optimized Two-Stage Fuzzy Control Scheme 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper proposes a signal timing scheme through a two-stage fuzzy logic controller. The controller first determines the signal phase and then adjusts the green time. At the first stage, the adaptive membership function of vehicle arrival rate is improved to adapt to the changing traffic flow. In addition to arrival rate and queue vehicles, a specific phase order rule is considered to avoid disordered phase selection in fuzzy control. At the second stage, the green time detection module decides whether to extend the current green time or switch phases every few seconds and the vehicle arrival rate is not required as the input to controller in real-time detection. Differential evolution algorithm with low space complexity and fast convergence is applied to optimize the fuzzy rules for avoiding artificial uncertainty. Simulation experiments are designed to compare traditional fuzzy controller, fixed-time controller, and fuzzy controller without flow prediction. Results show that the current proposed method in this paper can reduce vehicle delay significantly. Engineering (General). Civil engineering (General) Mathematics Zili Wang verfasserin aut Fuyang Chen verfasserin aut In Mathematical Problems in Engineering Hindawi Limited, 2002 (2021) (DE-627)320519937 (DE-600)2014442-8 1024123X nnns year:2021 https://doi.org/10.1155/2021/6693562 kostenfrei https://doaj.org/article/b2f0d83d4d6b4a50aaeef2c09218b677 kostenfrei http://dx.doi.org/10.1155/2021/6693562 kostenfrei https://doaj.org/toc/1024-123X Journal toc kostenfrei https://doaj.org/toc/1563-5147 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2088 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2021
allfields_unstemmed	10.1155/2021/6693562 doi (DE-627)DOAJ071712429 (DE-599)DOAJb2f0d83d4d6b4a50aaeef2c09218b677 DE-627 ger DE-627 rakwb eng TA1-2040 QA1-939 Taiping Jiang verfasserin aut Urban Traffic Signals Timing at Four-Phase Signalized Intersection Based on Optimized Two-Stage Fuzzy Control Scheme 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper proposes a signal timing scheme through a two-stage fuzzy logic controller. The controller first determines the signal phase and then adjusts the green time. At the first stage, the adaptive membership function of vehicle arrival rate is improved to adapt to the changing traffic flow. In addition to arrival rate and queue vehicles, a specific phase order rule is considered to avoid disordered phase selection in fuzzy control. At the second stage, the green time detection module decides whether to extend the current green time or switch phases every few seconds and the vehicle arrival rate is not required as the input to controller in real-time detection. Differential evolution algorithm with low space complexity and fast convergence is applied to optimize the fuzzy rules for avoiding artificial uncertainty. Simulation experiments are designed to compare traditional fuzzy controller, fixed-time controller, and fuzzy controller without flow prediction. Results show that the current proposed method in this paper can reduce vehicle delay significantly. Engineering (General). Civil engineering (General) Mathematics Zili Wang verfasserin aut Fuyang Chen verfasserin aut In Mathematical Problems in Engineering Hindawi Limited, 2002 (2021) (DE-627)320519937 (DE-600)2014442-8 1024123X nnns year:2021 https://doi.org/10.1155/2021/6693562 kostenfrei https://doaj.org/article/b2f0d83d4d6b4a50aaeef2c09218b677 kostenfrei http://dx.doi.org/10.1155/2021/6693562 kostenfrei https://doaj.org/toc/1024-123X Journal toc kostenfrei https://doaj.org/toc/1563-5147 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2088 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2021
allfieldsGer	10.1155/2021/6693562 doi (DE-627)DOAJ071712429 (DE-599)DOAJb2f0d83d4d6b4a50aaeef2c09218b677 DE-627 ger DE-627 rakwb eng TA1-2040 QA1-939 Taiping Jiang verfasserin aut Urban Traffic Signals Timing at Four-Phase Signalized Intersection Based on Optimized Two-Stage Fuzzy Control Scheme 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper proposes a signal timing scheme through a two-stage fuzzy logic controller. The controller first determines the signal phase and then adjusts the green time. At the first stage, the adaptive membership function of vehicle arrival rate is improved to adapt to the changing traffic flow. In addition to arrival rate and queue vehicles, a specific phase order rule is considered to avoid disordered phase selection in fuzzy control. At the second stage, the green time detection module decides whether to extend the current green time or switch phases every few seconds and the vehicle arrival rate is not required as the input to controller in real-time detection. Differential evolution algorithm with low space complexity and fast convergence is applied to optimize the fuzzy rules for avoiding artificial uncertainty. Simulation experiments are designed to compare traditional fuzzy controller, fixed-time controller, and fuzzy controller without flow prediction. Results show that the current proposed method in this paper can reduce vehicle delay significantly. Engineering (General). Civil engineering (General) Mathematics Zili Wang verfasserin aut Fuyang Chen verfasserin aut In Mathematical Problems in Engineering Hindawi Limited, 2002 (2021) (DE-627)320519937 (DE-600)2014442-8 1024123X nnns year:2021 https://doi.org/10.1155/2021/6693562 kostenfrei https://doaj.org/article/b2f0d83d4d6b4a50aaeef2c09218b677 kostenfrei http://dx.doi.org/10.1155/2021/6693562 kostenfrei https://doaj.org/toc/1024-123X Journal toc kostenfrei https://doaj.org/toc/1563-5147 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2088 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2021
allfieldsSound	10.1155/2021/6693562 doi (DE-627)DOAJ071712429 (DE-599)DOAJb2f0d83d4d6b4a50aaeef2c09218b677 DE-627 ger DE-627 rakwb eng TA1-2040 QA1-939 Taiping Jiang verfasserin aut Urban Traffic Signals Timing at Four-Phase Signalized Intersection Based on Optimized Two-Stage Fuzzy Control Scheme 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This paper proposes a signal timing scheme through a two-stage fuzzy logic controller. The controller first determines the signal phase and then adjusts the green time. At the first stage, the adaptive membership function of vehicle arrival rate is improved to adapt to the changing traffic flow. In addition to arrival rate and queue vehicles, a specific phase order rule is considered to avoid disordered phase selection in fuzzy control. At the second stage, the green time detection module decides whether to extend the current green time or switch phases every few seconds and the vehicle arrival rate is not required as the input to controller in real-time detection. Differential evolution algorithm with low space complexity and fast convergence is applied to optimize the fuzzy rules for avoiding artificial uncertainty. Simulation experiments are designed to compare traditional fuzzy controller, fixed-time controller, and fuzzy controller without flow prediction. Results show that the current proposed method in this paper can reduce vehicle delay significantly. Engineering (General). Civil engineering (General) Mathematics Zili Wang verfasserin aut Fuyang Chen verfasserin aut In Mathematical Problems in Engineering Hindawi Limited, 2002 (2021) (DE-627)320519937 (DE-600)2014442-8 1024123X nnns year:2021 https://doi.org/10.1155/2021/6693562 kostenfrei https://doaj.org/article/b2f0d83d4d6b4a50aaeef2c09218b677 kostenfrei http://dx.doi.org/10.1155/2021/6693562 kostenfrei https://doaj.org/toc/1024-123X Journal toc kostenfrei https://doaj.org/toc/1563-5147 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_171 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2088 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 2021
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callnumber-first	T - Technology
author	Taiping Jiang
spellingShingle	Taiping Jiang misc TA1-2040 misc QA1-939 misc Engineering (General). Civil engineering (General) misc Mathematics Urban Traffic Signals Timing at Four-Phase Signalized Intersection Based on Optimized Two-Stage Fuzzy Control Scheme
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topic_title	TA1-2040 QA1-939 Urban Traffic Signals Timing at Four-Phase Signalized Intersection Based on Optimized Two-Stage Fuzzy Control Scheme
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title	Urban Traffic Signals Timing at Four-Phase Signalized Intersection Based on Optimized Two-Stage Fuzzy Control Scheme
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title_full	Urban Traffic Signals Timing at Four-Phase Signalized Intersection Based on Optimized Two-Stage Fuzzy Control Scheme
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title_sort	urban traffic signals timing at four-phase signalized intersection based on optimized two-stage fuzzy control scheme
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title_auth	Urban Traffic Signals Timing at Four-Phase Signalized Intersection Based on Optimized Two-Stage Fuzzy Control Scheme
abstract	This paper proposes a signal timing scheme through a two-stage fuzzy logic controller. The controller first determines the signal phase and then adjusts the green time. At the first stage, the adaptive membership function of vehicle arrival rate is improved to adapt to the changing traffic flow. In addition to arrival rate and queue vehicles, a specific phase order rule is considered to avoid disordered phase selection in fuzzy control. At the second stage, the green time detection module decides whether to extend the current green time or switch phases every few seconds and the vehicle arrival rate is not required as the input to controller in real-time detection. Differential evolution algorithm with low space complexity and fast convergence is applied to optimize the fuzzy rules for avoiding artificial uncertainty. Simulation experiments are designed to compare traditional fuzzy controller, fixed-time controller, and fuzzy controller without flow prediction. Results show that the current proposed method in this paper can reduce vehicle delay significantly.
abstractGer	This paper proposes a signal timing scheme through a two-stage fuzzy logic controller. The controller first determines the signal phase and then adjusts the green time. At the first stage, the adaptive membership function of vehicle arrival rate is improved to adapt to the changing traffic flow. In addition to arrival rate and queue vehicles, a specific phase order rule is considered to avoid disordered phase selection in fuzzy control. At the second stage, the green time detection module decides whether to extend the current green time or switch phases every few seconds and the vehicle arrival rate is not required as the input to controller in real-time detection. Differential evolution algorithm with low space complexity and fast convergence is applied to optimize the fuzzy rules for avoiding artificial uncertainty. Simulation experiments are designed to compare traditional fuzzy controller, fixed-time controller, and fuzzy controller without flow prediction. Results show that the current proposed method in this paper can reduce vehicle delay significantly.
abstract_unstemmed	This paper proposes a signal timing scheme through a two-stage fuzzy logic controller. The controller first determines the signal phase and then adjusts the green time. At the first stage, the adaptive membership function of vehicle arrival rate is improved to adapt to the changing traffic flow. In addition to arrival rate and queue vehicles, a specific phase order rule is considered to avoid disordered phase selection in fuzzy control. At the second stage, the green time detection module decides whether to extend the current green time or switch phases every few seconds and the vehicle arrival rate is not required as the input to controller in real-time detection. Differential evolution algorithm with low space complexity and fast convergence is applied to optimize the fuzzy rules for avoiding artificial uncertainty. Simulation experiments are designed to compare traditional fuzzy controller, fixed-time controller, and fuzzy controller without flow prediction. Results show that the current proposed method in this paper can reduce vehicle delay significantly.
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title_short	Urban Traffic Signals Timing at Four-Phase Signalized Intersection Based on Optimized Two-Stage Fuzzy Control Scheme
url	https://doi.org/10.1155/2021/6693562 https://doaj.org/article/b2f0d83d4d6b4a50aaeef2c09218b677 http://dx.doi.org/10.1155/2021/6693562 https://doaj.org/toc/1024-123X https://doaj.org/toc/1563-5147
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up_date	2024-07-03T21:47:13.898Z
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