Adaptive neuro-fuzzy traffic signal control for multiple junctions

Wannige, C.T.; Sonnadara, D. U. J.

doi:10.1109/iciinfs.2009.5429853

Cited by 11 publications

(9 citation statements)

References 4 publications

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“…The model also minimizes the delay time significantly during red light phases at each junction. Wannige and colleagues also showed how traffic lights at both of the junctions synchronize adaptively when the volume of traffic increases significantly at one of the two junctions [33]. In a slightly different work, Dell'Orco and colleagues developed a neuro-fuzzy model to predict users' decisions in transport mode choice [34].…”

Section: Literature Reviewmentioning

confidence: 99%

Detecting Urban Transport Modes Using a Hybrid Knowledge Driven Framework from GPS Trajectory

Das

Winter

2016

IJGI

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Transport mode information is essential for understanding people's movement behavior and travel demand estimation. Current approaches extract travel information once the travel is complete. Such approaches are limited in terms of generating just-in-time information for a number of mobility based applications, e.g., real time mode specific patronage estimation. In order to detect the transport modalities from GPS trajectories, various machine learning approaches have already been explored. However, the majority of them produce only a single conclusion from a given set of evidences, ignoring the uncertainty of any mode classification. Also, the existing machine learning approaches fall short in explaining their reasoning scheme. In contrast, a fuzzy expert system can explain its reasoning scheme in a human readable format along with a provision of inferring different outcome possibilities, but lacks the adaptivity and learning ability of machine learning. In this paper, a novel hybrid knowledge driven framework is developed by integrating a fuzzy logic and a neural network to complement each other's limitations. Thus the aim of this paper is to automate the tuning process in order to generate an intelligent hybrid model that can perform effectively in near-real time mode detection using GPS trajectory. Tests demonstrate that a hybrid knowledge driven model works better than a purely knowledge driven model and at per the machine learning models in the context of transport mode detection.

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Section: Literature Reviewmentioning

confidence: 99%

Detecting Urban Transport Modes Using a Hybrid Knowledge Driven Framework from GPS Trajectory

Das

Winter

2016

IJGI

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“…The input variable Arrival (Figure 17) is expressed in four fuzzy sets, few (over 0-14), small (over 0-28), medium (over 14-42), and many (over [28][29][30][31][32][33][34][35][36][37][38][39][40][41][42], the input variable queue ( Figure 18) is expressed in four fuzzy sets, few (over 0- 19), small (over 0-38), medium (over [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38], and many (over 38-57), and the output variable green extension is fuzzified in four fuzzy sets, very short (over 0-10 second), short (over 0-20 second), medium (over 10-30 second), and long (over 20-30 second), which is shown in Figure 19.…”

Section: Fuzzy Phase Timingmentioning

confidence: 99%

“…Nakatsuyama et al [23] in extending the application of fuzzy logic to two consecutive junctions and work done by Chou et al [24] in applying fuzzy traffic controllers to multiple junctions. Wannige and Sonnadara [25] developed an adaptive neuro-fuzzy traffic signals control for two 4-way traffic junctions. The developed neuro-fuzzy system automatically draws membership functions and the rules by itself, thus making the designing process easier and more reliable compared to conventional fuzzy logic controllers.…”

Section: Introductionmentioning

confidence: 99%

Integrated fuzzy signal and ramp‐metering at a diamond interchange

Pham¹,

Alam²,

Potgieter³

et al. 2011

J of Advced Transportation

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SUMMARYWe propose a fuzzy logic control for the integrated signal operation of a diamond interchange and its ramp meter, to improve traffic flows on surface streets and motorway. This fuzzy logic diamond interchange (FLDI) comprises of three modules: fuzzy phase timing (FPT) module that controls the green time extension of the current phase, phase logic selection (PLS) module that decides the next phase based on the pre‐defined phase sequence or phase logic and, fuzzy ramp‐metering (FRM) module that determines the cycle time of the ramp meter based on current traffic volumes and conditions of the surface streets and the motorways. The FLDI is implemented in Advanced Interactive Microscopic Simulator for Urban and Non‐Urban Network Version 6 (AIMSUN 6), and compared with the traffic actuated signal control. Simulation results show that the FLDI outperforms the traffic‐actuated models with lower system total travel time, average delay, and improvements in downstream average speed and average delay. Copyright © 2011 John Wiley & Sons, Ltd.

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“…This system model works by making average delay small and synchronizing the green light phases between junctions [3]. A new intelligent intersection control system characterized by the agent-based control and the Local Simple Remote Complex (LSRC) based design principle can decrease the cost [4]. The Intelligent Traffic Signal Controller using FPGA (Field Programmable Gate Array) controller based on NF system is capable of taking intelligent decisions [5].…”

Section: Introductionmentioning

confidence: 99%

Neuro-Fuzzy based adaptive traffic flow control system

Iqbal¹,

Zahin²,

Islam³

et al. 2012

2012 International Conference on Communications, Devices and Intelligent Systems (CODIS)

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In this paper a Neuro-Fuzzy (NF) based traffic control system has been proposed which can take intelligent decision based on present traffic condition. Here we have trained the Adaptive Neuro-Fuzzy Inference System (ANFIS) system by variable traffic data and the output of this system is compared with Fuzzy logic based and Fixed Time based traffic control system. NF based traffic control system has been found more efficient as the average vehicular delay and the number of vehicles waiting in an intersection have been reduced.

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Adaptive neuro-fuzzy traffic signal control for multiple junctions

Cited by 11 publications

References 4 publications

Detecting Urban Transport Modes Using a Hybrid Knowledge Driven Framework from GPS Trajectory

Detecting Urban Transport Modes Using a Hybrid Knowledge Driven Framework from GPS Trajectory

Integrated fuzzy signal and ramp‐metering at a diamond interchange

Neuro-Fuzzy based adaptive traffic flow control system

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