General fuzzy rule base for isolated traffic signal control‐rule formulation

Niittymäki, Jarkko

doi:10.1080/03081060108717669

Cited by 31 publications

(12 citation statements)

References 6 publications

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“…Using rough set theory and according to expert experience, it can be minimized as the following decision table. [4] : TABLE1 THE MINIMUM FUZZY DECISION TABLE U a b c d e 1 1 1 1 2 1 2 2 3 2 1 2 4 2 2 3 5 1 3 2 3 6 1 3 2 3 7 1 3 3 3 8 1 3 3 3 9 3 1 2 3 10 3 1 2 3 11 3 1 3 3 12 3 1 3 3 13 2 3 2 4 14 2 3 3 4 15 2 3 2 4 16 2 3 3 4 17 3 2 2 4 18 3 2 2 4 19 3 2 3 4 20 3 2 3 4 21 3 1 1 5 22 3 1 1 5 23 3 3 5 In the table above, a, b, c, d represents the main traffic flow of the current phase, the minor traffic flow of the current phase, the main traffic flow of the next phase, the minor traffic flow of the next phase. The four variables were taken in three levels: 1,2,3 represent the S, M, L. The decision properties e is take to five levels, representing the green light, as VS, S, M, L, VL.…”

Section: Cmentioning

confidence: 97%

Traffic forecast of multilayer Rough fuzzy traffic controller

Liu

2010

The 2nd International Conference on Information Science and Engineering

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Section: Cmentioning

confidence: 97%

Traffic forecast of multilayer Rough fuzzy traffic controller

Liu

2010

The 2nd International Conference on Information Science and Engineering

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“…A number of similar studies that show FLCs have significant potential for traffic control management have been presented in the literature. 8–18…”

Section: Introductionmentioning

confidence: 99%

Optimizing a fuzzy logic traffic signal controller via the differential evolution algorithm under different traffic scenarios

Doğan

Akgüngör

2016

SIMULATION

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This study aims at optimizing fuzzy logic controller (FLC) triangle membership functions (MFs) for different traffic volumes via differential evolution (DE). To achieve this goal, a new FLC with a red time limiter, which actually calculates green time and the extension time of traffic movement phase, is developed to control an intersection. Subsequently, this FLC is optimized with two levels, namely Level-1 and Level-2. Level-1 searches each fuzzy class’s minimum and maximum values (α and β) that generate the lowest average delay per vehicle with DE. Using DE Level-2 inherits Level-1 ranges and reshapes the MFs to explore lower delay values computed by Level-1. The proposed method is tested with nine different traffic scenarios. For each scenario, 15 different headways are applied for a four-leg isolated intersection. The results indicate that the intersection average performance is increased up to 52%, 48%, and 14% at 800, 1600, and 2400 veh/h total intersection volumes, respectively, after Level-1 optimizations. They also reveal that intersection control produces higher delay values in only four scenarios after Level-2 procedures. Consequently, it is shown that the DE has significant potential to optimize FLCs at the intersection signal control. In addition, tuning fuzzy class ranges is found to be more critical than the MF reshaping process in traffic control via FLCs.

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“…Fuzzy logic system (FLS) is a powerful tool for situations where implementation of the exact mathematical model is difficult or impossible (Niittymki, 2001). Pappis and Mamdani are pioneers of implementation a fuzzy logic controller for an isolated intersection with two one-way streets (Pappis and Mamdani, 1977).…”

Section: Introductionmentioning

confidence: 99%

ANFIS Traffic Signal Controller for an Isolated Intersection

Araghi

Khosravi

Creighton

2014

Proceedings of the International Conference on Fuzzy Computation Theory and Applications

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Traffic signal controlling is one of the solutions to reduce the traffic congestion in cities. To set appropriate green times for traffic signal lights, we have applied Adaptive Neuro-Fuzzy Inference System (ANFIS) method in traffic signal controllers. ANFIS traffic signal controller is used for controlling traffic congestion of a single intersection with the purpose of minimizing travel delay time. The ANFIS traffic controller is an intelligent controller that learns to set an appropriate green time for each phase of traffic signal lights at the start of the phase and based on the traffic information. The controller uses genetic algorithm to tune ANFIS parameters during learning time. The results of the experiments show higher performance of the ANFIS traffic signal controller compared to three other traffic controllers that are developed as benchmarks. One of the benchmarks is GA-FLC (Araghi et al., 2014), next one is a fixed-FLC, and a fixed-time controller with three different values for green phase. Results show the higher performance of ANFIS controller.

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General fuzzy rule base for isolated traffic signal control‐rule formulation

Cited by 31 publications

References 6 publications

Traffic forecast of multilayer Rough fuzzy traffic controller

Traffic forecast of multilayer Rough fuzzy traffic controller

Optimizing a fuzzy logic traffic signal controller via the differential evolution algorithm under different traffic scenarios

ANFIS Traffic Signal Controller for an Isolated Intersection

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